Health IT-Enabled Quality Measurement: Perspectives, Pathways ...

Health IT-Enabled Quality Measurement

PERSPECTIVES

PATHWAYS

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ACTICAL GUIDAN

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Health IT-Enabled Quality Measurement:

Perspectives, Pathways, and Practical Guidance

Prepared for: Agency for Healthcare Research and Quality U.S. Department of Health and Human Services 540 Gaither Road Rockville, MD 20850 http://www.ahrq.gov

Contract No. HHSA290200900024I

Prepared by: Booz Allen Hamilton

Authors: Rebecca A. Roper, M.S., M.P.H. Kristine Martin Anderson, M.B.A. Christina A. Marsh, M.M.H.S, M.A. Anjanette C. Flemming, M.P.H.

AHRQ Publication No. 13-0059-EF September 2013

http://www.ahrq.gov

http://www.ahrq.gov

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This document is in the public domain and may be used and reprinted with permission except those copyrighted materials that are clearly noted in the document. Further reproduction of those copyrighted materials is prohibited without the specific permission of copyright holders.

Suggested Citation: Roper RA, Anderson KM, Marsh CA, Flemming AC. Health IT-Enabled Quality Measurement: Perspectives, Pathways, and Practical Guidance. (Prepared by Booz Allen Hamilton, under Contract No. HHSA290200900024I.) AHRQ Publication No. 13-0059-EF. Rockville, MD: Agency for Healthcare Research and Quality. September 2013.

None of the investigators has any affiliations or financial involvement that conflicts with the material presented in this report.

This project was funded by the Agency for Healthcare Research and Quality (AHRQ), U.S. Department of Health and Human Services.

The opinions expressed in this document are those of the authors and do not reflect the official position of AHRQ or the

U.S. Department of Health and Human Services.

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Acknowledgments

The authors thank those institutions and individuals who thoughtfully responded to the June 2012 Request for Information and Public Comment posted in the Federal Register on quality measurement enabled by health information technology and those who participated in focus groups for sharing their time and candid insights. A complete listing of these contributors can be found in Appendix D.

We would also like to thank the following individuals for their contributions to this report and the Pathways to Quality project: Kathleen Ackerman (Office of the General Counsel, U.S. Department of Health and Human Services), Sadia Aslam (Booz Allen Hamilton), Tiffany Bauman (Booz Allen Hamilton), Jenny Griffith (Agency for Healthcare Research and Quality), Howard Isenstein (Booz Allen Hamilton), Fatima Khan (Booz Allen Hamilton), Christian Mahler (Office of the General Counsel, U.S. Department of Health and Human Services), Beth Mallory (Booz Allen Hamilton), Rachel Naugle (Booz Allen Hamilton), Julius Patterson (Agency for Healthcare Research and Quality), Janet Reynolds (Booz Allen Hamilton), and Mary Rolston (Agency for Healthcare Research and Quality)

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Contents1. Introduction ............................................................................................................................................12. Background .............................................................................................................................................3

2.1. Challenges to Achieving Health IT-Enabled Quality Measurement Ideals .......................................32.2. Advancement in Health IT Enables Evolution in Quality Measurement .........................................32.3. Ideals for Health IT-Enabled Quality Measurement ........................................................................5

3. Reflections: Perspectives, Pathways, and Practical Guidance ....................................................................63.1. Perspectives on Health IT-Enabled Quality Measurement ...............................................................63.2. Pathways to Enhanced Quality Measurement ..................................................................................9

3.2.1. Measure Development, Implementation, and Testing ........................................................... 93.2.2. Data Elements and Data Capture/Tools to Process Unstructured Data .............................. 103.2.3. Data Access, Sharing, Aggregation, and Integration ........................................................... 133.2.4. Patient Engagement ............................................................................................................ 143.2.5. Collaboration and Education .............................................................................................. 17

3.3. Practical Guidance: Table of Suggested Steps Toward Enhanced Health IT-Enabled Quality Measurement .................................................................................................................................18

4. Pursing Pathways to Achieve eImprovements .........................................................................................275. References ..............................................................................................................................................28

AppendixesAppendix A. Public and Stakeholder Insights ...........................................................................................A-1

A.1. Overview .....................................................................................................................................A-1A.2.1. Question-by-Question Analysis .........................................................................................A-1A.2.2. Key Concepts Across All Questions .................................................................................A-13

A.3. Focus Groups ............................................................................................................................. A-17A.3.1. Focus Group Insights ......................................................................................................A-18

Appendix B. Methods and Approaches .....................................................................................................B-1B.1. Environmental Snapshot ..............................................................................................................B-2B.2. Stakeholder Engagement ..............................................................................................................B-2

B.2.1. Request for Information and Public Comment ................................................................. B-3B.2.2. Stakeholder-Specific Focus Groups ................................................................................... B-3B.2.3. Multistakeholder Focus Group ......................................................................................... B-4

B.3. Additional Research, Synthesis, and Writing of the Final Report .................................................B-6B.4. Update of Partial Catalog of Current Activities To Improve Quality Measurement Enabled by

Health IT ....................................................................................................................................C-1

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Appendix C. Partial Catalog of Current Activities To Improve Quality Measurement Enabled by Health IT ............................................................................................................................C-1

Appendix D. List of Focus Group Participants and RFI Respondents ...................................................... D-1D.1. Focus Group Participants ............................................................................................................ D-1D.2. RFI Respondents ........................................................................................................................ D-3

Appendix E. Additional Resources ........................................................................................................... E-1

List of ExhibitsExhibit 1. Table of Suggested Steps Toward Enhanced Health IT-Enabled Quality Measurement .........19Exhibit A-1. Distribution of RFI Respondents by Stakeholder Group ......................................................A-1Exhibit A-2. Count of Responses to RFI Questions by Stakeholder Group ..............................................A-2Exhibit A-3. Unheard Voices by Responding Stakeholder Group .............................................................A-4Exhibit A-4. Categories of Required Infrastructure ..................................................................................A-5Exhibit A-5. RFI Respondent Recommendations for Motivating Measure Developers ............................A-6Exhibit A-6. Means by Which to Educate Providers by Respondent Stakeholder Group ........................ A-10Exhibit A-7. Total Mentions of MU, CDS, HIE, and Standards by RFI Question ................................ A-14Exhibit A-8. Mentions of MU, CDS, HIE, and Standards in RFI by Stakeholder Group ...................... A-15Exhibit B-1. Approach to Final Report Development .............................................................................B-2Exhibit B-2. Table of Representative Literature Review Search Terms ......................................................C-1Exhibit C-1. Partial Catalog of Current Activities To Improve Quality Measurement Enabled by

Health IT ............................................................................................................................C-2Exhibit E-1. Listing of Additional Resources for Information and Collaboration .................................... E-1

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Foreworduality measurement enabled by health information technology (IT) is an evolving field that seeks to support systems of quality improvement. The value of quality measurement is related to the com-pleteness of reporting, context, consistency in meaning, and the recipient’s ability to act upon the information. Health IT holds great promise for facilitating accurate and timely reporting of measures

to intended users, such as clinicians, caregivers, patients, payers, and public health officials. Diversified stakeholders of varying resources and priorities are striving to make advancements in health IT-enabled quality measurement.

Innovative advances in the field are frequently incremental. Capabilities of technology and care delivery processes evolve as the health care delivery system evolves; thus, we need to consider both the current and future state. With public and private partners in 2012 and 2013, we gathered at the blackboard of discovery to solicit stakeholder thoughts on current challenges, infrastructure gaps, and successful strategies, which are critical to the next phases of health IT-enabled quality measurement and reporting. These stakeholders resoundingly expressed their commitment to create and use health IT-enabled quality measurement in order to support a system of eImprovement (i.e., a comprehensive system of measurement and improvement, which is described in this report). This report presents stakeholder insights in a tiered fashion, offering readers the opportunity to glean summary findings (Section 3) or explore contextual information provided in more substantive narrative (Appendix A). Their reflections fall into three types of findings:

�� Perspectives—Differing perspectives inform stakeholders’ priorities, investments, and expectations in their pursuit of health IT-enabled quality measurement and the extent to which such quality measurement is able to support efficient system(s) of quality improvement. By exploring the characteristics and implications of these perspectives, tradeoffs and opportunities for coalescence can be realized. (Section 3.1)

�� Pathways—A number of critical topic areas need to be addressed in order to advance health IT-enabled quality measurement. In this paper we focus on measure development, implementation, and testing; data elements and data capture; data access, sharing, aggregation, and integration; patient engagement; and collaboration and education) (Section 3.2)

�� Practical Guidance—Delineation of 111 prospective research activities or infrastructure enhancements are organized around five topic areas (Section 3.3). An illustrative catalog of more than 150 programs and initiatives active in the area of health IT-enabled quality measurement is searchable by program characteristics (Appendix C).

For more information on the Pathways to Quality Through Health IT initiative, go to the Health IT-Enabled Quality Measurement Web page.

Related AHRQ-Sponsored Web Conference

A free, AHRQ-sponsored Webinar titled “A National Web Conference on Health IT-Enabled Quality Measurement: Perspectives, Pathways, and Practical Guidance,” will be held September 13, 2013, from 2:00–3:30 p.m., EDT.

Presenters will provide insights from this report—Health IT-Enabled Quality Measurement: Perspectives, Pathways, and Practical Guidance—and the associated RFI responses and focus groups.

To register for the Webinar or review the transcript and presentation slides after it has ended, go to http://healthit.ahrq.gov/events.

http://healthit.ahrq.gov/events

http://healthit.ahrq.gov/HealthITEnabledQualityMeasurement

http://healthit.ahrq.gov/HealthITEnabledQualityMeasurement

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The Agency for Healthcare Research and Quality (AHRQ) would like to thank the Request for Information and Public Comment respondents and focus group participants for their generosity in sharing their time and knowledge. Their insights were critical to advance the knowledge base on the intersection of health IT and quality measurement, which is provided in this report. Given the highly dynamic nature of health care, continued dialogue among stakeholders is needed not only to discuss ongoing issues, but also to ask new questions. AHRQ looks forward to ongoing collaboration with stakeholders to continue identifying areas in need of further research and to disseminating successful strategies for improving health IT-enabled quality measurement.

P. Jon White, M.D. Director Health Information Technology Portfolio Agency for Healthcare Research and Quality

Rebecca A. Roper, M.S., M.P.H. Director, Practice-Based Research Network Initiative Health Information Technology Portfolio Agency for Healthcare Research and Quality

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Executive SummaryPurpose and Context

Improving the quality of care is a top priority for the United States’ (U.S.) health care system. Accordingly, there are a myriad of efforts underway to improve quality through both payment and health care delivery reform. Both health IT and quality measurement factor strongly into a reformed health care delivery and financing system. This report examines the intersection of health IT and quality measurement, reflecting the expectation that health IT-enabled quality measurement can accelerate quality improvement. Stakeholders suggest that cycle times for quality improvement can be reduced, as meaningful, actionable information becomes available to decisionmakers (e.g., patients, families, and clinicians) in real or near real-time for care management. System-wide information could also be available for payers, providers, and purveyors of public health through the accurate and efficient use of technology. This concept is referred to as “eImprovement” in this report.

The July 2012 publication of an Environmental Snapshot—Quality Measurement Enabled by Health IT: Overview, Challenges, and Possibilities—provided a framework for this project. AHRQ subsequently sought the public’s input to identify the near term feasibility and priorities of various activities that could enhance measurement to support eImprovement through a series of stakeholder engagements. A Request for Information and Public Comment (RFI) was posted in the Federal Register, followed by the convening of a series of focus groups with various health care stakeholders (e.g., consumers, government, measure developers, providers, payers, vendors).

This report, Health IT-Enabled Quality Measurement: Perspectives, Pathways, and Practical Guidance, presents a high-level summary from the 15-question RFI and subsequent reflections provided by focus group participants on how to advance quality measurement enabled by health IT, as well as provides a list of RFI respondent and focus group participant suggested incremental steps toward advancing health IT-enabled quality measurement. While insight from patient-focused stakeholders was sought, this report does not fully explore the concerns, priorities, or needs of patients and their families. The RFI respondents and focus group participants recognized the importance of meaningful engagement by patients and their families, and offered some suggested pathways to enhance their engagement. The material in this report is organized to offer readers both high-level summaries of findings as well as contextually relevant discussions. This report is presented to share information, stimulate discussion, assist communication among stakeholders, facilitate understanding, and provide guidance on potential infra-structure enhancements that could be pursued, individually or collectively.

The Appendixes to this report summarize the input received as well as catalog the efforts of many organizations who are engaged in efforts to improve quality through health IT.

�� Appendix A—a more comprehensive summary of the findings from each RFI question as well as summaries of the focus group findings.

�� Appendix B—the methodology and approach for each of the stakeholder engagement activities

�� Appendix C—A Partial Catalog of Current Activities To Improve Quality Measurement Enabled by Health IT describes over 150 different programs and initiatives across public and private programs

�� Appendix D—a list of the RFI respondents and focus group participants who were incredibly generous with their time and insights

�� Appendix E—additional resources on health IT-enabled quality measurement

http://healthit.ahrq.gov/sites/default/files/docs/page/final-hit-enabled-quality-measurement-snapshot.pdf


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Background

Until recently, quality measurement relied almost exclusively on the use of electronic claims data, manual chart abstraction, and patient surveys. However, the enormous, recent growth in the adoption of health IT provides an opportunity for more efficient quality measurement, the development of new types of measures serving various purposes and end users, and tighter integration between quality measurement and quality improvement. The sudden increase in availability of digital information raises expectations across all stake-holders who use, work with, or oversee aspects of the health care system with respect to how data can be used to improve care, while many of those same stakeholders are simultaneously facing the challenges associated with acquiring new health IT systems and integrating them into the health care delivery organization. Although there is a great deal of consensus among health care stakeholders on a number of recurring ideals for health IT-enabled quality measurement—the need for patient-centricity and involvement, the importance of collaboration, the desire to align measures to national priorities, the importance of actionable measures in a system of quality improvement, and the need to use technology to support measurement—priorities may be different for various stakeholders.

Reflections: Perspectives, Pathways, and Practical Guidance

Given that advances in quality measurement are occurring in parallel with many other changes to health care delivery and payment, there is a need to consider the impacts of quality measurement on the overall health care system. Despite their common commitment to advancing health IT-enabled quality measurement, stake-holders vary on the degree to which they find it necessary that quality measurement instigates system-wide innovation that results from the concurrent changes to the health care systems, processes, and resources. A number of diversified perspectives emerged during stakeholder engagement activities (i.e., RFI and focus groups). It is of note that these perspectives were not singular for a particular type of stakeholder. Moreover, a given stakeholder may have expressed differing perspectives across projects and even within the deliberation of a particular infrastructure challenge.

For the purposes of this discussion—and to reflect the singularity with which participants expressed priorities at any one time—four perspectives were simplified and distilled as follows:

�� Quality measurement accelerating systemic quality improvement as the highest priority—Measurement should be focused on improving quality and developed recognizing its impact on and integration into the quality improvement enterprise for various stakeholders and purposes. Although burden and impact should be considered, measures and their integration within a quality improvement system does not need to be limited to current care delivery or health IT systems capabilities.

�� Quality measurement maximizing the current capabilities of health IT—Measurement should leverage all currently available health IT (e.g., electronic health records, registries) necessary, but constrain measure specifications to information that could reasonably be assumed to be generally available and widely accessible today to minimize implementation burden.

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�� Quality measurement relying on the current capabilities of a given delivery system—Measurement should be a byproduct of care delivery and seek to minimize impact on clinician workflow. Measure specifications should be constrained to information that would reasonably be needed to support care and meet current reporting requirements.

�� Centralized prioritization of measures, balancing perspectives—New measure development priorities should be driven by an authoritative entity through declarative means and tradeoffs in the above perspectives are explicitly managed. Quality measurement programs should be aligned and harmonized uniformly through a centralized multistakeholder process.

In actuality, one could expect a hybrid of perspectives, informed by the intended objective, resources available, and changes anticipated to provide the framework for a new health IT-enabled quality measurement.

This report highlights stakeholder views on each topic. At a high level, there appeared to be agreement about the importance of each topic; however, there were often diverging thoughts on how to operationalize solutions and how to prioritize iterative advancements in health IT-enabled quality measurement. Progress continues to be made in advancing health IT-enabled quality measurement. RFI respondents and focus group participants identified several topics as essential building blocks toward advancement of improved health IT-enabled quality measurement. These categories are presented somewhat chronologically in the process of quality measurement evolution, rather than relative priority of the issues:

Measure development, implementation, and testing

Data elements and data capture/tools to process unstructured data

Data access, sharing, aggregation, and integration

Patient engagement

Collaboration and education

As key topics are discussed in this report—measure development, implementation, and testing; data elements and data capture; data access, sharing, aggregation, and integration; patient engagement; and collaboration and education—tradeoffs are discussed through the lens of these perspectives where explored by stakehold-ers. For example, the priority for new measures may vary according to the perspective of the respondent and the particular issues raised through questions or conversations. RFI respondents, focus group participants, and researchers almost universally supported new measurement in areas that reflect new expectations of the care delivery system, such as care coordination, outcomes, and longitudinal measures. Each of these new measurement areas are aimed to change the status quo. However, the timing and specifications of these new measures may be approached or prioritized differently by stakeholders with different perspectives (i.e., quality measurement accelerating systemic improvement as the highest priority, quality measurement maximizing current health IT capabilities, and quality measurement relying on the current capabilities of a given delivery system).

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Is measurement an agent of change or is it a reflective measure of progress toward change? Both of these perspectives are valid. Ultimately, it was clear that there is no singular path to the realization of optimal eMeasurement and eImprovement. The dissemination of examples and suggestions is important in order to leverage developments from various approaches. The Practical Guidance: Table of Suggested Steps Toward Enhanced Health IT-Enabled Quality Measurement contains a list of all the stakeholder suggestions from the RFI and focus groups and Appendix C provides a catalog with examples from which stakeholders may gain insights.

Pursing Pathways to Achieve eImprovements

Throughout the stakeholder engagement activities, the long-term vision for quality improvement remained consistent: health-IT enabled quality measurement is an integral element of eImprovement. Stakeholders discussed that each quality measure should serve a specific purpose and be incorporated within a system of quality improvement. This means that health IT would facilitate measurement so that performance results can be shared with measure end users (e.g., clinicians, care givers, patients) in a timely manner to improve health care outcomes. eImprovement is reliant upon rapid feedback loops that would be supported by meaningful, actionable performance information at the patient and provider level, hospital and health system level, as well as at the population level.

The various issues presented in this report—measure development, implementation, and testing; data elements, data capture, and tools to process unstructured data; data access, sharing, aggregation, and integration; patient engagement; and collaboration and education—need to be reconsidered periodically to facilitate the prioritization of activities needed to continue the advancement of health IT-enabled quality measurement. Stakeholders engaged in the ongoing dialog should reflect on the various perspectives on quality measurement identified in this report to frame future deliberations and identify optimal paths going forward. Individuals and institutions may see their objectives, priorities, and paths forward differently. Patient perspectives will particularly need to be considered more carefully. Additionally, new perspectives may emerge with advancing technological capabilities and changing environmental factors (e.g., evolving medical curriculum, certification requirements in the use of EHRs, quality measurement, quality improvement; reimbursement; delivery system). “Furthermore, measure concepts must be prioritized based on the potential population-wide effect of achieving improvements in that measure.”1

With continued collaboration, the paths forward may be different but the destination will be the same—the successful next generation of quality measurement. Evolving quality measurement enabled by health IT can facilitate eImprovement and provide a foundation for advancing the “Triple Aim” of better health and better care at a lower cost.

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1. IntroductionImproving the quality of care delivered in the United States (U.S.) health care system is a top priority. Quality measurement enabled by health IT is an essential component of quality improvement. Given the rapid adoption of health IT and the importance of quality measurement to assess progress toward quality improvement, stakeholders throughout the health care system recognize the importance of examining the inter-section of health IT and quality measurement. Realizing the transformative potential of health IT-enabled quality measurement will require both new and improved measures, improvements to the technical infrastructure supporting quality measurement, enhanced reliability of quality data, improved policy guidance, enhanced systems of monitoring those values, and a means to mitigate quality improvement challenges.

The Agency for Healthcare Research and Quality (AHRQ) has been a leader in building the evidence base on quality measurement enabled by health IT. AHRQ has supported a continuum of research activities, innovative demonstrations, approaches, and methodological work in this area through their Health Information Technology Research Portfolio. As the Centers for Medicare & Medicaid Services (CMS) and the Office of the National Coordinator for Health IT (ONC) act to implement meaningful use, AHRQ continues to pursue various activities in concert with other Federal agencies to discern and disseminate successful strategies, challenges, and prioritized possibilities specifically pertaining to the intersec-tion of quality measurement and health IT.

Given the multitude of activities occurring at the intersection of quality measurement and health IT, this is an ideal moment for active dialogue with stakeholders, discussion about the challenges in

the field, and a more detailed consideration of the many stakeholders and their contributions. AHRQ seeks to identify pathways to the next generation of quality measurement to support the “successful transformation of the health care system to achieve better health outcomes as efficiently as possible.”1

In 2012, AHRQ released an Environmental Snapshot—Quality Measurement Enabled by Health IT: Overview, Challenges, and Possibilities—which described possibilities for the advancement and challenges to the advancement of health IT-enabled quality measurement as identified in the literature. Following the publication of the Environmental Snapshot, further research and stakeholder engagement activities were conducted on the topic of quality measurement enabled by health IT. A Request for Information and Public Comment

(RFI) with 15 questions was posted in the Federal Register and received 63 unique responses from a diverse set of stakeholders. Subsequently, a series of focus groups was held that consisted of 64 stake-holders from across the health care enterprise (e.g., vendors, measure developers, payers, providers, consumers, and government) to further discuss issues that arose in the RFI.

RFI respondents and focus group participants were made up of a diverse set of stakeholders that included representatives of: consumers, government, measure developers, payers, providers (including medical specialty societies), purchasers, researchers, and vendors. Organizations that represented consumers participated; however, patients and their families did not directly participate. It should be noted that these important stakeholders have expectations and uses for measures which may not be reflected in the perspectives, pathways, and practical guidance featured in this report.



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This report contains findings from the RFI and the focus groups. The material in this report is organized to offer readers both high-level summaries of findings as well as contextually relevant discussions. The observations are presented to share information, stimulate discussion, assist communication among stakeholders, facilitate understanding, and to provide guidance on potential infrastructure enhancements which could be pursued, individually, or collectively.

Appendix A provides a full summary of each stakeholder engagement activity and Appendix B provides the methodology and approach used for each stakeholder engagement activity. During the research process for the Environmental Snapshot, numerous programs and initiatives were identified that are working toward improving quality measurement enabled by health IT. Additional projects and programs were identified during stakeholder engagement activities. These are presented in the Partial Catalog of Current Activities To Improve Quality Measurement Enabled by Health IT (Appendix C).

This catalog of programs and initiatives is not exhaustive, but rather, illustrates the breadth and depth of the work being conducted by a variety of the stakeholders (e.g., Federal, State/Regional, and private) discussed in this report. Appendix D contains a list of the RFI respondents and focus group participants, who so generously gave of their time and knowledge. Additionally, a list of further web resources on quality measurement enabled by health IT is provided in Appendix E.

The Practical Guidance: Table of Suggested Steps Toward Enhanced Health IT-Enabled Quality Measurement (Section 3.3) in this report contains over 100 suggested next steps from RFI respondents and focus group participants. These recommendations provide some prospective practical guidance for a broad set of stakeholders throughout the health care system. Suggestions are categorized as:

�§ Measure development, implementation, and testing

�§ Data elements and data capture/tools to process unstructured data

�§ Data access, sharing, aggregation, and integration

�§ Patient engagement

�§ Collaboration and education

�§ Other topics and recommendations

The Partial Catalog of Current Activities To Improve Quality Measurement Enabled by Health IT, Appendix C is searchable through the use of “tags” by project type, health IT topic, care setting, and illustrative subpopulations. This catalog provides examples and retrospective practical guidance on over 100 programs and initiatives being conducted by—

�§ Federal Government

�§ State/Regional Communities

�§ Private Organizations

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2. BackgroundThe Institute of Medicine (IOM) reports, To Err is Human: Building a Safer Health System and Crossing the Quality Chasm: A New Health System for the 21st Century, changed how health care was perceived and discussed in the United States.2,3 These reports highlighted the need to improve the quality of health care in the United States. Since the publication of these reports, there has been much discussion and activities toward achieving a system that delivers high-quality care consistently, as described in Crossing the Quality Chasm.3 Meanwhile, rising costs continue to be a major concern in the United States (U.S.) health policy landscape, particularly given the evidence that higher costs are not necessarily correlated with higher quality. Improving the U.S. health care system will require pursuit of three aims: “improving the experience of care, improving the health of populations, and reducing per capita costs of health care.”4 Health IT and quality measurement are critical tools needed to achieve this “Triple Aim.”

The 2012 Environmental Snapshot provided an overview of the challenges and possibilities for advancing health IT-enabled quality measurement. Throughout the subsequent stakeholder engagement activities, RFI respondents and focus group participants acknowledged the challenges, but overwhelmingly demonstrated a desire to work together to evolve quality measurement enabled by health IT.

2.1. Challenges to Achieving Health IT-Enabled Quality Measurement Ideals

Health care stakeholders may have slightly different priorities for the near term and mid-term in support of health IT-enabled quality

measurements. Despite varied priorities and plans on how to achieve the future state of health IT-enabled quality measurement, stakeholders agree on the possibilities for health IT-enabled quality measurement. This results in some unresolved infrastructure challenges, gaps in the measure set, and a lag in the achievement of the promise of technology. For example, infrastructure challenges in a fragmented delivery system may pose challenges to the advancement of specific patient-focused measures. Disagreement on which measures are of greatest priority to pursue and the remaining pockets of low technology adoption can also slow progress. Such challenges can inhibit the effectiveness of using health IT to measure the quality of care. Moreover, key communica-tion challenges also exist; better engagement and exchange of ideas among quality measurement stakeholders is needed to address many of these key challenges. Patients and their families are key members of the health care community and must also be engaged as both contributors and users of quality information. While these challenges may be disconcerting; these different priorities also offer opportunities for innovations to be pursued and large scale advancements to be eventually realized.

2.2. Advancement in Health IT Enables Evolution in Quality Measurement

Until recently, quality measurement relied almost exclusively on the use of electronic claims data, manual chart abstraction, and patient surveys. However, the enormous, recent growth in the adoption of health IT has the potential to enable superior quality measurement. By the end of 2012, approximately 44.4 percent of non-Federal, acute care hospitals in the U.S had adopted at least a basic electronic health record (EHR) system, up from 12 percent in 2009.5


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During that same time period, adoption of EHRs increased from approximately 22 to 40 percent among office-based physicians.6 Health information exchange is also advancing; today over 40 percent of hospitals electronically send lab and radiology data to providers outside of their organizations.7

This progress is partially attributed to two recent pieces of legislation, which focus on the need to improve health care quality and contain health care cost growth: the American Recovery and Reinvestment Act of 2009 (ARRA) (Public Law No 111-5) and the Patient Protection and Affordable Care Act (ACA) (Public Law 111-1480). These laws have called for new approaches to cost and quality and have helped drive the development of the quality measurement enterprise and the adoption of health IT. ARRA contained the Health Information Technology for Economic and Clinical Health (HITECH) Act, requiring the government to take a leadership role in encouraging the meaningful use of health IT, which has increased the expectations for EHR functionality, including quality measurement. ARRA also funded the Beacon Community Program, which was designed to support health IT

infrastructure and the development and expansion of health information exchange.

ACA contains many provisions that address quality and quality reporting. For example, ACA extended CMS’ Physician Quality Reporting Initiative (PQRI)—which soon after became the Physician Quality Reporting System (PQRS)—and incentives through 2014; called for the creation of a National Quality Strategy; and initiated several quality programs, including the Hospital Value Based Purchasing program, the Hospital Readmissions Reduction Program, and the Medicare Shared Savings Program for Accountable Care Organizations (ACOs). ACA also gave new responsibilities to the Center for Quality Improvement and Patient Safety within AHRQ and the Center for Medicare and Medicaid Innovation (CMMI) within CMS to test new payment and health care delivery mechanisms. These new and extended programs hold the expectation that new payment models will drive the demand for improved quality measurement and that quality reporting will be enabled by health IT to a greater extent over time.

The National Quality Strategy. Section 3011 of the Affordable Care Act called for a National Quality Strategy (NQS) to be established to improve the delivery of health care services, patient health outcomes, and population health. This strategy, developed by AHRQ iteratively and collaboratively with numerous public and private stakeholders, is guided by three aims: Better Care, Affordable Care, and Healthy People/Healthy Communities. The NQS has six priorities that can, in part, leverage the benefits of health IT-enabled quality measurement, including engaging patients as partners, promoting communication and coordination of care, promoting best practices, and empowering new health care delivery and payment models. Several implementation activities have already taken place and collaboration for upcoming activities is ongoing.

Accountable Care Organizations. Affordable Care Act Section 3022 called for the establishment of the Medicare Shared Savings Program, which was launched in November of 2011. This program aims to facilitate coordination and cooperation among providers to improve the quality of care for Medicare Fee-For-Service (FFS) beneficiaries and reduce unnecessary costs through Accountable Care Organizations (ACO). Since passage of the Affordable Care Act, more than 250 ACOs have been established and as of January 2013, 106 ACOs have been selected to participate in the Shared Savings Program, collectively covering more than 4 million Medicare beneficiaries. As the program is refined, health IT-enabled quality measurement could offer new information for both improving care delivery and containing costs in new reimbursement models.

http://www.ahrq.gov/workingforquality/

http://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/sharedsavingsprogram/index.html

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2.3. Ideals for Health IT-Enabled Quality Measurement

There is a great deal of agreement across the literature, RFI respondents, and focus group participants on a number of high-level attributes or components of an ideal future state.2, 8–12 The following represent some examples of possible char-acteristics of the further state:

�� Measurement should be patient centered. A key aim of the National Quality Strategy is to “improve overall quality, by making health care more patient-centered, reliable, accessible, and safe.”13 Patients and their families need not only to have access to clear and useful performance information data to participate in choosing providers and treatments but also to be considered valuable contributors of data to support the creation of patient-centered quality measures.

�� Measurement should be supported by end users’ education and collaboration. The development of quality measures is an active team-effort. Collaboration is continually needed at every stage in the development cycle to ensure effective and efficient measurement. In addition to collaboration, a common understanding of the broader context of quality measures is needed by measure developers, generators, reporters, and end users so that measures derived for

one use do not inadvertently impact the interpretation of the quality measure if taken out of context and used for other purposes.

�� Measurement should be aligned to national priorities. Although there is less consensus around the extent to which the measure set should be broadened or narrowed, there is agreement that measures should be aligned across public and private programs to reduce burden. Furthermore, value—quality in relation to the cost of care—has been suggested as an overarching goal that could unite the interests of diverse health care stakeholders.14,15

�� Measurement should be actionable and built to work within a system of quality improvement. Meaningful measurement needs to be actionable for the end users (e.g., patients, providers). Additionally, quality measures should be used to generate clinical decision support and create actionable alerts that can be used at the point of care.

�� Measurement should leverage available technologies. In the desired future state, automation is increased and significant additional effort to manually extract data for measurement is eliminated. New tools enable providers and patients to contribute to and use measurement information in new ways.

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3. Reflections: Perspectives, Pathways, and Practical Guidance

The primary goal of quality measurement is to improve health care outcomes. Throughout the stakeholder engagement activities, the long-term vision for quality improvement remained consistent: health-IT enabled quality measurement must support eImprovement. Stakeholders suggest that cycle times for quality improvement can be reduced, as meaningful, actionable information becomes available to decisionmakers (e.g., patients, families, and clinicians) in real time or near real-time for care management. System-wide information could also be available for payers, providers, and purveyors of public health through the accurate and efficient use of technology. In other words, stakeholders would like to see measurement move beyond “checking the box” to meet program requirements and evolve into measurement that is actionable and timely for patients and providers and can improve care.

This chapter provides a discussion of perspectives, pathways, and practical guidance for advancing health IT-enabled quality measurement from the RFI responses and focus group discussions. The specific insights sought regarding the 15 substantive RFI questions are discussed in Appendix A. Additionally, Appendix A contains a summary of the subsequent stakeholder-specific discussions, which illuminated specific areas of interest such as: importance of risk adjusted rates, clinical decision support at the point of care, necessity of health information exchange, and so on.

3.1. Perspectives on Health IT-Enabled Quality Measurement

In the name of quality improvement, the U.S. health care system is simultaneously trying to accelerate the development of new measures, make the best use of health IT, and incentivize improve-ments in the way care is delivered.

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Despite their common commitment to advancing health IT-enabled quality measurement, stakehold-ers vary on the degree to which they find it necessary for quality measurement to instigate system-wide innovation given the concurrent changes to the health care systems, processes, and resources. Using the three-legged stool as an example (i.e., intended objective, resources available (time and money), and change anticipated), stakeholders seek to find balance among these three when choosing to pursue such enhancements. Often, there is desire for greater scope (i.e., increased specificity, volume, or complexity in information generated and reported) without comparable resources, which leaves the stool unbalanced.

During the analysis of RFI responses and focus group discussions, patterns emerged from the diversified respondents as to how activities in pursuit of health IT-enabled quality measurement should be prioritized. These perspectives conceptu-ally frame the relative priorities of how some stakeholders would like to proceed (see “Perspectives on Health IT-Enabled Quality Measurement” on page 8). For the purposes of this discussion these perspectives were simplified as: quality measurement accelerating systemic quality improvement as the highest priority, measurement maximizing current capabilities of health IT, measurement relying on the current capabilities of a given delivery system, and a centralized

prioritization of measures where tradeoffs in these perspectives are explicitly managed. These perspec-tives are presented as an opportunity to facilitate dialogue; frame how various stakeholders are phrasing their desires, priorities, and needs; and help inform the deliberative process. A number of key areas for improvement were discussed by RFI respondents and focus group participants. At a high level, there appeared to be agreement about the importance of addressing challenges in these areas. However, despite high-level agreement on the importance of making progress in each of these areas, there were often diverging thoughts on how to approach and operationalize solutions based on stakeholders’ perspectives on the acceptable level of changes to information systems and care processes that could be induced by quality measurement. New measures to support key areas cited by RFI respondents, focus group participants, and researchers (e.g., care coordination, specialty care, functional status, patient reported outcomes, patient experience, longitudinal measures) could be implemented as part of a broader change to care delivery, but the timing matters. If the measure is introduced before the care delivery change is fully implemented, the measure serves as an accelerant. If the measure is introduced after the change is implemented then the measure provides a yardstick for progress. Some respondents and stakeholders viewed using measurement as a tool to accelerate change as positive, while others viewed the same approach as a negative disruption. Still, others acknowledge the need for some disruption but believe that the degree of disruption should be centrally managed through a broad consensus process led by either the public or private sector or both.

The tradeoffs between perspectives was illustrated by an RFI respondent, “Consumers and purchasers are most interested—in many cases—in the directionality of measures, and are less concerned with creating the ‘ideal’ specifications with the exact exclusionary codes, etc. The mentality [of limiting measurement to “ideal” specifications] delays the development of meaningful measures. This mentality also may result in measures of patient experience, engagement, and outcomes that use patient-reported data being subject to greater scrutiny than other types of measures.”

Each planned incremental advancement in health IT-enabled quality measurement is defined by its—

�§ Intended objective

�§ Resources available

�§ Change anticipated

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Perspectives on Health IT-Enabled Quality Measurement

Four perspectives on the prioritization of activities to advance quality measurement enabled by health IT emerged from the various stakeholder engagement activities (e.g., RFI, focus groups). The perspectives identified here are simplified for the purposes of discussion; there are many possible hybrids of these perspectives and a given stakeholder may express more than one perspective in different situations. As patient and family engagement increases, such perspectives will evolve and inform the others.

Each perspective has advantages and disadvantages. The purpose of this discussion is to bring forth the underlying assumptions of each perspective to better understand the associated implications for strategies, opportunities, and possible solutions around key topics (e.g., data capture, data access, patient and consumer engagement, measure development, measure testing) as discussed by stakeholders. For example, stakeholders engaged in the collaborative pursuit of new quality measures could perhaps explicitly discuss how priorities might be viewed differently based on each perspective.

The following provides a brief description, in no particular order, of each perspective:

�§ Quality measurement accelerating systemic quality improvement as the highest priority—Measurement should be developed for use within identified approach for improving quality. Measures may be used to accelerate innovation in information systems and practices of care to support a future vision of high-quality health care delivery. Thoughtful consideration should be given to appropriately harmonize and align quality measures across programs. Moreover, burden and impact should be carefully considered, but measures should not be limited to the capabilities of current care delivery or health IT systems. Measures should be aggressively developed to optimize use for various stakeholders and purposes, recognizing the measurement needs of clinicians, institutions, patients, government, and other public health and reporting entities.

�§ Quality measurement maximizing the current capabilities of health IT—Measurement should leverage all currently available health IT (e.g., EHR, registries) needed, but constrain measure specification to information that could reasonably be assumed to be generally available and widely accessible to minimize implementation burden. New measures should be designed to be feasible today. Measure development can evolve as new data becomes available. Measures for public health and public reporting would be limited to measures that are readily available from current information systems.

�§ Quality measurement relying on the current capabilities of a given delivery system—Measurement should be a byproduct of care delivery and seek to minimize impact on clinician workflow. Measure specifications should be constrained to information that would reasonably be needed to support care. Measures for public health and public reporting would be limited to measures that can extract data collected in the course of providing care. Measurement as a byproduct of care may become more inclusive over time as quality measurement and improvement are integrated into clinical training across specialties.

�§ Centralized prioritization of measurement, balancing perspectives—New measure development priorities should be driven by an authoritative entity through declarative means and tradeoffs in the above perspectives are explicitly managed. Quality measurement programs should be aligned and harmonized uniformly through a centralized multistakeholder process.

Additionally, the objectives for advancements may differ as well. For some, the focus is near term with more definitive impact (e.g., EHR certi-fication, reporting). While others are ready to embrace a more comprehensive system of quality improvement and consideration of how health IT-enabled quality measurement is both informing the health care system and payment models, as well

as being informed by them. The objectives reflected in the perspectives could be a function of the level of experience with not only health IT, but also with an integrated quality improvement system. Individuals and institutions may express preference for different perspectives, depending on the particular quality measurement pursued, setting of care, or resources available to support the endeavor.

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At times, there can be hard to recognize and mutually exclusive desires and even divergent strategies regarding agreed upon priorities for short-term actions and long-term plans. Given the complexity of the systems and the number of factors in flux at any one time, it is often difficult to operationalize values for each of these three dimensions. Through the implementation phase, as adjustments are made (chosen or de facto), the overall balance of realized objectives, applied resources, and change tolerated, may be modified. There is no one way or “right” way. Yet, under-standing the breadth and interconnectedness of these actions is important. Clearly, “incremental” advancements do have system-wide impact, directly and indirectly.

3.2. Pathways to Enhanced Quality Measurement

The following sections discuss five key topic areas (measure development, implementation, and testing; data elements and data capture; data access, sharing, aggregation, and integration; patient engagement; and collaboration and education) identified by RFI respondents and focus group participants as critical areas for improvement to advance health IT-enabled quality measurement. Thematic reflections and recommen-dations for action are articulated for each topic. A full list of recommendations can be found in The Practical Guidance: Table of Suggested Steps Toward Enhanced Health IT-Enabled Quality Measurement. Wherever divergent stakeholder views appeared to be related to the four simplified perspectives articulated above, the discussion calls out any potential differences in priorities that may be expressed as a result of the perspectives.

3.2.1. Measure Development, Implementation, and Testing

RFI respondents and focus group participants frequently discussed the measurement development lifecycle and some of the associated challenges and concerns that arise at various points of the lifecycle as they relate to quality measurement enabled by health IT. Collaboration is a key tool that many of the stakeholders indicated as critical at every stage of the lifecycle. Collaboration is discussed separately in this chapter because of its importance for all aspects of quality measurement enabled by health IT.

RFI respondents and focus group participants indicated that improved specification for quality measures is needed to ensure more effective implementation. The implementation process for new measures can variably increase measurement burden, particularly on providers and vendors. Stakeholders recommended collaboration as critical throughout the lifecycle to ensure that implementa-tion burden is considered during the measure development phase. Many of the stakeholders who responded to the RFI or participated in the focus groups also emphasized the importance of automation to reduce measurement burden, suggesting that collaboration with vendors be improved. Vendors expressed the need for their active engagement with clinicians and recognized that meaningful engagement of patients both as providers and even verifiers of health care data as well as users of quality measurement reporting were underutilized. Moreover, provider focus group participants recommended that measure development should be aligned with the EHR certification program. There was also concern that variation occurs when vendors implement the same measure in different sites due to differences in workflow and other factors.

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Many participants encouraged the government to facilitate further the sharing of information and guidance on the “how to” strategies.

Testing was another key part of the measurement lifecycle that RFI respondents and focus group par-ticipants discussed improving. Some providers and vendors suggested that testing should begin while measures are in the development stage and should not be rushed. Measure developers expressed need for a more standard measure testing methodology, additional funding for testing, and more expansive test sites. Moreover, focus group participants across stakeholder groups suggested that there is a lack of best practices for testing in both scope and scale of measure testing.

RFI respondents and focus group participants noted that measures should be re-evaluated regularly to determine whether the measures are fulfilling their intended purpose. Stakeholders also recommended that further research is needed to test the reliability of chart abstraction versus EHR generated quality measures. The Practical Guidance: Table of Suggested Steps Toward Enhanced Health IT-Enabled Quality Measurement contains a full list of RFI respondent and focus group participant suggestions.

Stakeholder perspectives on approaches to measure implementation vary. Although stakeholders generally agreed about the importance of measures across care settings, those who feel that new measure development should be constrained to current health IT and delivery system capabilities may prioritize development of measures that are easier to implement as opposed to measures that require further action on the part of providers and their staff. However, stakeholders more aligned with a desire to leverage innovation may prioritize measures such as across care settings, if they conclude the measures are important to improving care quality despite potential implementation burden.

In January 2013, ONC and CMS hosted a Kaizen event that focused on making the eMeasure development process more efficient and effective. The event resulted in cross-contractor, cross-agency, and cross-stakeholder collaboration across the clinical quality measure enterprise. Workgroups focused on each stage of the lifecycle and were able to identify specific, actionable steps to improve the eMeasure development process. Additionally, workgroups have continued the work begun in January, 2013, and are piloting efforts that address some of the same issues brought up by RFI respondents and focus group participants.

3.2.2. Data Elements and Data Capture/Tools to Process Unstructured Data

Producing highly reliable and valid quality measures that are appropriate for high-stake uses requires clear measure specifications that accurately anticipate the availability of high-quality data (e.g., reimbursement). The supply chain for quality measure data is complex, hinging on both technology (e.g., user interfaces, data stores, data capture technologies) and processes (e.g., clinical workflow, data validation processes). Rapid intro-duction of changes in care delivery, the variability in EHR vendor applications and implementations, and the increased depth of clinical information available make data access and reliability issues particularly challenging at this point in time. Accordingly, RFI respondents and focus group participants cited data quality as a major barrier to eMeasurement. They cited inconsistencies in the scope of data elements captured in various health IT systems, frustration with increased requirements for data entry, problems with user interfaces, difficulty in retrieving data from various systems, and imprecise measure specifications as challenges.

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Given the expectations for data reuse (i.e., using the same data for a purpose other than the primary data function), it is likely that inconsistencies or erroneous entries at point of capture are carried downstream as well. RFI respondents and focus group participants discussed the importance of consistent, complete, and accurate data capture and developing mechanisms to process unstruc-tured data effectively.

The importance of standardization was frequently mentioned by RFI respondents and focus group participants. Representatives of providers, vendors, and measure developers all stated a need for standardization of measure specifications, data elements included in measurement, and the process to capture those data elements. Consistency of data capture was discussed as both a data entry issue and a workflow issue; stakeholders are interested in tools to ease data entry. Focus group partici-pants suggested that more consistency is required within vendors’ EHR platforms and that perhaps there should be incentives for vendors to build data standards into their products. However, many participants suggested that a standardized EHR user interface was unlikely to be developed because of the competitive nature of the software industry. RFI respondents that have participated in measure development recommended the continued use of tools such as the Measure Authoring Tool (MAT) and the Quality Data Model (QDM) for standardized eMeasure development; however, they suggested further refinement of these tools

particularly in the ability to support development of longitudinal measures. RFI respondents and focus group participants also indicated that while the National Quality Forum (NQF) requires eMeasure developers to use QDM data elements, current EHRs generally are not designed to support the QDM, complicating attempts for standardization.

Stakeholders frequently expressed a need for consistency in measurement of similar concepts (e.g., tobacco use or cigarette use) so that measure results are comparable across programs. The U.S. Health Information Knowledgebase has a Standards Portal, which can be used to research, review, and compare metadata between different Standards Developing Organizations.16 Additionally, NQF has adjusted their consensus review process to attempt to harmonize measure concepts, with limited success. There was recognition that common measure specifications may not be sufficient for comparability. Alignment down to the level of data capture and data storage may be required. Both data capture and data storage need to be tested for comparability at the measure implementation level, where few tools exist.

Natural language processing was suggested as a potential tool to reduce the requirement for standardized data entry, to validate measures, to identify patient inclusions and exclusions which may rely on information that is captured in text in many parts of the health record, and to assess the reliability of free text; however, some respondents expressed concern about relying on natural language processing for measurement purposes.

The Regenstrief Institute and the International Health Terminology Standards Development Organization recently announced that they have signed a long-term agreement to begin linking their health care terminologies—Logical Observation Identifiers Names and Codes (LOINC) and SNOMED CT. This collaboration will provide users with a common framework for using LOINC and SNOMED CT.

“The balance between structured and unstructured data in an EHR is a difficult one, since it represents a trade-off between

flexibility and standardization.”

http://ushik.ahrq.gov/index_sdo.jsp

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Focus group participants suggested that an evidence-based report should further examine the potential use of natural language processing for quality measurement. (See call out on this page to learn more about providing additional recommen-dations for this topic or submitting a new topic to AHRQ). Other potential tools that focus group participants discussed include voice translation technology and qualitative data analysis software. RFI respondents and focus group participants suggested that further research is needed to determine the best utilization of tools designed to handle unstructured data for the purposes of quality measurement.

Reliability of data is an issue that extends beyond EHRs. Both RFI respondents and focus group participants stressed the value of data from sources other than the EHR. Frequently mentioned sources included registries, lab and radiology systems, public health databases, payer information systems, and patient generated data. For each data source, a more stringent evaluation of the consistent capture of data was also noted as an area for improvement.

Approaches to improving the quality of data used in measurement can vary based on the participant’s and respondent’s perspectives on the role of measurement in inducing change. NQF suggests that all eMeasures being considered for endorsement use only data elements that are included in the QDM. Some RFI respondents and focus group participants also discussed the burden of data capture on providers and their staff and suggested that the value of any new data elements should be explicitly assessed. Some respondents advocated that measures rely solely upon data elements that are already required to support the

care delivery process. Alternatively, those more comfortable with using measurement to push innovation may conclude that a particular measure is so important that it should be implemented even if additional new data elements are needed, new system developments are needed, or provider burden is increased. Additional suggestions from the RFI respondents and the focus group partici-pants can be found in the Practical Guidance: Table of Suggested Steps Toward Enhanced Health IT-Enabled Quality Measurement.

“To improve consistency in data capture, we have to focus on the critical data elements.”

Based on stakeholder recommendation, AHRQ encourages coordinated end

user submission for potential systematic review to be conducted about the use of Natural Language Processing and Health IT-Enabled Quality Measures

As ever, AHRQ welcomes end user’s nominations for systematic reviews for topics of interest. Nominated research questions for a given topic that are informed by diverse end users who are committed to be disseminating partner organizations, will be prioritized. For selected topics, research questions will be refined with further input from stakeholder groups (e.g., guideline developers, policy-makers, clinicians, patients).

Nomination forms are available on AHRQ’s Effective Health Care Program Web site.

The most effective nominations provide specific information. For expository purposes, some examples are shown about acute infections and patient safety.

�� Identify the areas of controversy (e.g., what are the benefits and harms?).

�� What is the population of interest (e.g., for known asymptomatic carriers)?

�� Is there a standard comparison (e.g., versus standard contact precautions [glove and gown for contact])?

�� What are the important outcomes (e.g., to reduce infection incidence)?

http://effectivehealthcare.ahrq.gov/index.cfm/submit-a-suggestion-for-research/

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3.2.3. Data Access, Sharing, Aggregation, and Integration

The issues of data access, sharing, aggregation, and integration are particularly important to quality measures aimed at comprehensive, patient-centered care. Health information exchange is critical for many of the quality measures that RFI respondents and focus group participants have indicated are needed (e.g., longitudinal, episodic, patient-re-ported outcomes).7 RFI respondents and focus

group participants indicated that it is critical that barriers around data access, sharing, aggregation, and integration be lowered in order to enable better quality measurement. Respondents to the RFI and focus group participants expressed an interest in expanding data sources for measurement to include, for example, claims systems, registries, pharmacy systems, and laboratory systems. Payers, in particular, indicated that they would use for quality measurement “all data made available to them.”

The lack of health IT adoption across all settings of care concerned some RFI respondents and focus group participants.

A recent ONC report indicated that only 25 percent of hospitals and 31 percent of physicians could exchange clinical summaries with outside providers.7 Moreover, the report indicated that exchange is even more limited for long-term and post acute care settings.7 Lower adoption in nonacute settings, such as skilled nursing facilities, could limit the electronic data available to support quality measures for some of the most vulnerable

populations. The absence of electronic data would delay the development of new measures. More broadly, there are increased concerns about data consistency and data quality across sites of care. One focus group participant asked, “Who is the source of truth if data is in conflict [when data is shared]?” Focus group participants suggested that proprietary code among vendors and the need for funding and testing of aggregation tools are also challenges to data access, sharing, aggregation, and integration.

Stakeholders generally agreed that data sharing, aggregation, and integration are more hampered by policy issues than they are by technology issues. One example provided by a focus group stakeholder was the lack of “model policies” so that local policies could more easily be replicated across communities. Specific policy issues discussed by stakeholders include the need for strategies to address sensitive information and the need for shared accountability for shared information.

Many of the RFI respondents and focus group participants agreed that patient identifiers are critical for linking data across multiple sources;

In an effort to achieve data liquidity between systems and vendors the “CommonWell Health Alliance” was recently launched. This new vendor alliance represents 41 percent of the hospital EHR market and 23 percent of the ambulatory care EHR market. Initial participants include Cerner, McKesson, Allscripts, athenahealth, Greenway and RelayHealth. Per a press release, the Alliance will define, promote, and certify a national infrastructure with common platforms and policies.

“There is a need to think from a different point of view. It’s not just about what data

lives in an EHR…there is a bigger data set we need to think about.”

“The most challenging issues anticipated [in terms of data aggregation and

integration] are around policy, privacy, governance, and data ownership…the technology is pretty straightforward.”

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however, focus group participants indicated that they believed it would be unlikely that a national patient identifier would be implemented in the near future. One participant noted that the health information exchange and data sharing efforts that have been most successful have used some sort of common identifier. While participants agreed that common patient identifiers were important, the urgency to address that challenge is mitigated by the extent to which pertinent data from non-affiliated health care providers are available.

RFI respondents and focus group partici-pants discussed a number of possible solutions to addressing issues around data access, sharing, aggregation, and integration; the Practical Guidance: Table of Suggested Steps Toward Enhanced Health IT-Enabled Quality Measurement contains a full list of suggestions. For example, standard data definitions and data elements would ease some of the technological challenges to data aggregation. Additionally, new tools for data aggregation and integration would be advantageous. Stakeholders reinforced the importance that model policies and constructs be robustly tested and feasible solutions provided before policy changes are in place.

Stakeholder perspectives on the priority for advancement and acceleration of health information exchange vary. If a stakeholder felt that a global, patient-centered outcome measure was very important to advance quality, then

advancement of health information exchange would be seen by that stakeholder as imperative, particularly if health information exchange can facilitate the integration of outside data, which may reduce provider data collection burden by increasing automation and reducing duplicative efforts. However, other stakeholders feel that there is sufficient opportunity for improvement using data that exists in single sites of care, and might emphasize new measures that improve care without relying on an enhanced infrastructure for information exchange.

3.2.4. Patient Engagement

Patient engagement—which is inclusive of the engagement of families and caregivers in the care of patients—was frequently suggested by stakeholders as a means by which to improve health outcomes. It is frequently stated that patient engagement is the blockbuster drug of this century.17

Health Information Exchange

The ability to exchange clinical information between health care entities creates new opportunities to improve patient safety, care coordination, and quality measurement and improvement. Health information exchanges (HIEs) throughout the United States are facilitating information sharing.

The Indiana Health Information Exchange (IHIE) is the Nation’s largest HIE, connecting more than 90 hospitals and 110 clinics and surgery centers across Indiana and more than 25,000 across 17 states. IHIE uses a statewide network called the Indiana Network for Patient Care (INPC) to provide a “virtual” longitudinal patient record. The INPC handles over a million secure transactions a day, including 3 billion pieces of clinical data, 80 million radiology images, 50 million text reports, and 750,000 EKG readings. It enables all participating sites access to discharge summaries, operative notes, medication records, and pathology reports. In April, IHIE created a for-profit subsidiary to generate additional revenue towards self-sustainability. In May, IHIE launched the Quality First Web site, offering quality information across to 22 measures.

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Ensuring that each person and family is engaged as partners in their care is one of the National Quality Strategy priorities and is among the Meaningful Use

requirements. Stakeholders who responded to the RFI or participated in the focus groups frequently discussed the importance of patient engagement and how it relates to quality measurement enabled by health IT. Moreover, respondents and participants discussed the importance of patients as both users of quality information and providers of quality information to be fully engaged. Focus group participants suggested that patient engagement in quality measurement can lead to shared decision-making and can enhance successful patient-provider relationships. Despite its value, participants noted that the intersection of patient engagement and quality measurement can be challenging; patients, families, and caregivers are not often effectively engaged in conversations around quality measurement. Measures do not need to be of equal importance to all users of quality measures; however, the value to the end users and their means to benefit by that information need to be transparent. There is a need to better understand what measures are of most interest and value to patients and the most effective means by which patients, families, and caregivers can interact with measures.

Many RFI respondents and focus group participants (including participants from patient advocacy organizations) discussed the lack of existing quality measures that matter most to patients, families, and caregivers. Such an expansion of measures may signal a change in what constitutes a quality measure, beyond assessments of the actions of clinicians and the health care system.

The development of new types of measures, whose purpose could be to assist patients in their ability to manage their own health, could inform the health care system in general. Current measures are primarily designed for clinicians or oversight organizations; however, measures designed to engage patients in their care must consider that patients view quality differently than clinicians. Stakeholders stated that patients are looking for quality information particularly at two points in time: when choosing a physician or provider and when choosing treatment options for their illness or condition. Several focus group participants recommended research to further identify the needs of patients and determine how to translate those needs into useful measures. Often patients and families are not sure where to go for quality information until the need arises. Some focus group participants recommended that tools for health care quality could be marketed in advance, so that patients would know where to get information if needed as is common in other industries (e.g., OpenTable in dining, TripAdvisor for travel). When choosing a physician or provider, stakeholders suggested that patients would be interested in information about location, cost, and value (i.e., price in relation to quality). Additionally, patients want to have a provider rating focused on offering treatment options, a provider rating focused on working as part of a team, and a provider rating focused on success treating patient’s specific conditions. A need for personalization was a consistent theme. When considering treatment

options after a diagnosis is made, stakeholders suggested that patients want to know how various options for care have resulted in improvements in patients similar to them.

“When [patients, families, and their caregivers] are involved and the measure

is directly relevant to them…measurement becomes more meaningful.”

“The challenge is that you cannot design [public reporting of quality information] for a patient…you have to design it with them.”

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Once in treatment, stakeholders indicated that patients want information around how they are progressing against benchmarks or against their own goals. Furthermore, it was recommended that personal goals may need to be integrated with clinical goals.

RFI respondents and focus group participants indicated that information presentation must also be considered; information currently available to consumers today is often disorganized, incomplete, and of variable reliability. Patients were reported to not have confidence in many of the current sources of information (e.g., online opinions, payers). Presentation must be easy to understand; for example, some consumer focus group participants stated patients feel that discharge summaries are not well-received because they contain too much information and are not easily understood by

patients without assistance. Focus group participants suggested that some information might require complimentary education from a provider to be valuable, while other information should be accessible

and presented in ways in which the patient and/or family member can understand without assistance.

RFI respondents and focus group participants discussed a number of different tools and additional research that may be needed to better engage patients as users and contributors of quality information. Technologies such as personal health records (PHRs) and patient portals were suggested to assist in better integrating patient-reported information with other clinical data. Stakeholders also recommended that familiar platforms such as tablets, kiosks, and mobile devices be placed in frequently accessed areas (e.g., doctor’s offices, pharmacies, grocery stores) to engage consumers. Focus group participants suggested that systems to encourage patient contributions of data need to be less cumbersome with minimal manual entry.

Moreover, systems supporting patient-reported data should be intuitive and relevant. Stakeholders recommended that further conversations with consumers should focus on how and why patients might use quality information, as well what information interest patients and how they would like to see it presented. Several organizations were noted as conducting additional research and pilots in this area, such as The Informed Medical Decisions Foundation, the Centers for Aging Services Technologies, and the Cleveland Clinic, which have been included in Appendix C.18 Focus group participants indicated that further research is needed to determine the best mechanisms for presenting information to patients. Moreover, these technologies and the way information is presented should be designed with patients to ensure that they are effective. Additional recommendations around patient engagement can be found in the Practical Guidance: Table of Suggested Steps Toward Enhanced Health IT-Enabled Quality Measurement.

Blue Button, which enables patients to download their personal health information from online accounts, is currently available to Veterans, uniformed service members, and Medicare beneficiaries. Almost 1 million people have downloaded their own health information via Blue Button. Many private sector companies such as UnitedHealthCare and Aetna are also providing a way to “Blue Button” or download their health data for their members or beneficiaries.

Post acute care (e.g., skilled nursing, home health care, hospice) was recommended by focus group participants as a potential environment in which to pilot and test new patient-centered measures, ways of presenting and collecting information, and various tools for engagement of patients and families. Post acute care provides the opportunity to evaluate patient care episodically, examine an expanded care team, test a wide variety of measures, and review the use of measurement in a variety of care settings and across the care continuum.

“It is not so much that all voices are not heard, but they are not heard in the same place.”

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3.2.5. Collaboration and Education

RFI respondents and focus group participants, regardless of stakeholder group or affiliation, emphasized the need for collaboration and education as mechanisms for improving quality measurement enabled by health IT. Stakeholders involved in the development, implementation, and use of health IT-enabled quality measures include measure developers, IT vendors, providers, payers, researchers, patients and families, and many others. Given the variety of stakeholders that are engaged in these activities, collaboration may take numerous forms. RFI respondents and focus group participants frequently mentioned the importance of communication among these stakeholders to address the challenges associated with data elements and capture, data access and sharing, measure development and testing, and patient engagement.

Collaboration among all stakeholders was identified as important; however, RFI respondents and focus group participants suggested that vendors and measure developers, in particular, should engage each other early in the measure development process and work together throughout the measurement lifecycle. However, focus group participants recognized that with limited resources, consistent and regular engagement throughout the measurement lifecycle can be a challenge. RFI respondents and focus group participants indicated that consumers (i.e., patients and their families) and employers must also be a part of the collab-oration; however, it was suggested that engaging these two groups can also be challenging, due to a lack of resources. Further, focus group participants cautioned that patients and employers may have differing interests and should not be uniformly considered substitutes for one another.

RFI respondents affirmed the importance of “incorporating the science of health care delivery and quality measurement” into provider education. Additionally, one RFI respondent noted that many specialties require quality reporting and improvement activities in the board recertification process. RFI respondents further indicated that education for stakeholders is crucial throughout the measurement lifecycle. For example, it is important to demonstrate to providers that health IT-enabled quality measurement leads to improved care for patients and economic practice enhancement. RFI respondents and focus group participants recommended a number of different forums for education of stakeholders (e.g., continuing medical education courses and Webinars for providers, town hall meetings and online forums for patients). Additionally, it was suggested that the development of educational tools are further needed. For example, focus group participants recommended that a resource which summarizes vendor capabilities for measure development would be very useful to measure developers. Programs for consumers and purchasers are also needed to provide education on what quality information is available and how it can be interpreted.

It was frequently recommended by RFI respondents and focus group participants that the Federal Government may be the best suited stakeholder to convene diverse groups. These individuals suggested that large-scale, national col-laboratives are the best mechanisms for engaging a variety of stakeholders. However, the goal for any such collaboration or education activities needs to build a foundation that could be continued beyond an initial meeting. Collaboratives or educational programs that are tied to national level programs or initiatives may have greater interest among stakeholders.

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Workshops would need to be broad enough with meaningful content to attract diverse stakeholders and to compete with the many other conference offerings available. Possible topics recommended for discussion included role definitions, common vocabulary, issues with data elements, measure testing, and challenges in development and imple-mentation. Additional recommendations around collaboration and education can be found in the Practical Guidance: Table of Suggested Steps Toward Enhanced Health IT-Enabled Quality Measurement.

3.3. Practical Guidance: Table of Suggested Steps Toward Enhanced Health IT-Enabled Quality Measurement

The following table (Exhibit 1) contains ideas that were presented by stakeholders who responded to the RFI or participated in focus groups. This list is not meant to be directive, but simply to describe the recommendations made by stakeholders and provide prospective, practical guidance (Appendix C) contains examples of existing activities, which provides retrospective, practical guidance). Specific questions were asked in the RFI; the responses to those questions informed the customized content pursued in the stakeholder-specific focus groups. Responses tended to coalesce around particular

themes. Since the questions asked during the focus groups were driven by the RFI responses, the activities and topics in this table are also mapped back to the associated RFI question. Notation is made regarding whether the activity was suggested in RFI responses, during focus groups, or both. The fact that a recommendation was not made by RFI respondents may merely be a function of the selected focus of the RFI respondents’ responses to purposefully open-ended RFI questions. Similarly, a recommendation not mentioned by focus group participants may be attributed merely to the limited time available for each of the focus groups. Conversely, it should not be assumed that all par-ticipants agree with each recommended activity or action. Some items mentioned during focus groups also have a recommended timing; if such prior-itization was discussed it is labeled near term or mid-term. Relative prioritization was not discussed in all focus groups and was not asked of RFI respondents. Moreover, in some cases, additional “Reflections” are listed to elaborate on key observa-tions associated with the recommendation.

The suggestions in this table are organized by topic area and correspond with the topics discussed in Section 3.2 of this report. The suggestions are listed in no particular order within each heading. A summary of the RFI and focus group reflections can be found in Appendix A.

19



Exhibit 1. Table of Suggested Steps Toward Enhanced Health IT-Enabled Quality Measurement

Recommendations/Reflection Ass

ocia

ted

RFI

Q

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Timing Mentioned In...

Nea

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NT

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(M

T)

RFI

Res

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es

Focu

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ups

Measure Development, Implementation, and Testing

1 Determine the greatest need for new measures or for updating existing measures (i.e., measures that would add the most value) and focus on eSpecifying those measures instead of retrofitting all measures

3

2 Develop new types of measures (e.g., care coordination, specialty, functional status, clinical outcomes, overuse, efficiency, cost effectiveness, variation, value, resource use, readmission measures, shared decisionmaking, longitudinal measures, patient experience, subacute care)

2, 4, 10, 13

NT/MT

3 Define the purpose for certain measures (e.g., measures for quality improvement, public reporting) so that measures are developed to be consistent with their intended purpose

3

4 Develop measures that are actionable for patients and providers (and include the appropriate actor), accurate, relevant to consumers and their goals, and tied to reimbursement

2, 4, 10, 13

MT

5 Improve measure specifications so that they improve the usefulness and ability of electronic quality measurement to be feasible, valid, and reliable

7, 8, 13

NT

6 Assess the feasibility and impact of deploying (certain) new measures 2, 4, 10, 13

MT

7 Evaluate measures currently used for public reporting, their intended purpose, and whether they are really making a difference (i.e., fulfilling their intended purpose)

3

8 Evaluate measures used for quality improvement (i.e., assess if measurement really improves outcomes) and determine if small variances in what and how we measure changes outcomes

3

9 Offer financial incentives to motivate measure developers to develop new eMeasures 5

10 Increase electronic health record (EHR) adoption [Reflection: Many organizations (e.g., post acute care, State-based, mental health, substance abuse, disability) are far behind in the development of such measures due to a lack of technology, which has not been widely adopted largely because of the lack of financial incentives and lack of access to the same technical assistance commonly received by hospitals and other medical systems]

7, 10

11 Refine the Measure Authoring Tool (MAT) to include making it able to create longitudinal measures

8

12 Continue to improve and expand the EHR Incentive Program and EHR Certification Program (e.g., include more provider types and specialty-specific requirements, improve testing prior to release) [Reflection: Some providers felt left out of the Meaningful Use program (e.g., nurse practitioners, hospice, home health)]

7, 10

http://www.qualityforum.org/MAT/

http://www.cms.gov/Regulations-and-Guidance/Legislation/EHRIncentivePrograms/index.html

http://www.healthit.gov/policy-researchers-implementers/certification-programs-policy

http://www.healthit.gov/policy-researchers-implementers/certification-programs-policy

20




ocia

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Q

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Nea

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RFI

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Focu

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13 Develop measures that can be generated as byproduct of care, are actionable and intuitive, and that represent an understanding of operational and clinical workflows for an efficient generation of quality measures [Reflection: Data collected as a byproduct of care will reduce provider burden and duplicative data entry.] [Reflection: Quality measurement results need to be useful to providers in terms of improving care and meeting provider needs (e.g., improve productivity, sync with workflow, be more user-friendly][Reflection: Providers feel they bear most of the risk and administrative cost of quality measurement programs]

3, 7, 10, 11

14 Ensure that quality measures are evidence-based and that the measurement process is reliable and fair

3, 11

15 Develop a standardized measure testing process to ensure that the data elements required for measurement exist in the system, that the data is consistently populated, and that the data can be pulled for the purposes of measurement

3, 5, 7, 8, 13

NT

16 Conduct further field testing to test the validity and reliability of the data, the measures, and the measure specifications and then share lessons learned from this testing

3, 5, 7, 8, 13

NT

17 Conduct further testing of longitudinal measures (i.e., gathering data across systems and settings) as health information exchange adoption increases

3, 5, 8, 10, 13

MT

18 Conduct further testing and demonstrations of natural language processing for use in quality measurement

9

19 Conduct pilots to test the reliability of health IT-enabled outcomes measures using EHRs when compared with measures obtained through chart abstraction

3, 9

Data Elements and Data Capture/Tools to Process Unstructured Data

20 Agree on an overarching approach to data elements that will be available for measurements (e.g., those that can be captured through the provision and documentation of care or have been identified by the care process itself, those that focus on elements of patient care, those that inform the delivery of care and/or critical for the accomplishing of patient care, those that are needed for public reporting programs)

3, 7, 9

21 Identify critical pieces of information that need to be added in the electronic record to achieve desired measures [Reflection: The necessary data elements that are needed for measurement but that do not currently exist in systems need to be identified and steps put in place to ensure they are included in future releases and available for use.]

5, 7, 8, 9, 10

NT

22 Determine priorities of data capture beyond what is required for meaningful use and other programs [Reflection: Payer requirements and/or regulation often drives data usage and how it is collected.]

3, 7

23 Identify and remedy gaps in the value set 3, 9

24 Develop curricula and provide training to assist in ensuring data elements are entered properly and consistently

3, 7

25 Develop a “plug and play” approach for eMeasure development that will also facilitate easy updates of measure constructs as needed [Reflection: Quality measures should not be hard coded.]

3, 9

26 Investigate and test the best means by which to codify data in a standard way (e.g., HL7)

3, 9

21




ocia

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RFI

Q

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Nea

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Focu

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ups

27 Encourage vendors to increase consistency within their own platforms and products [Reflection: Although vendors offer providers the convenience of entering data in different places throughout their systems, measurement modules may only pull from one place; there is often no auto-population of that same data into other modules.]

9, 11

28 Assess where EHRs have the best data versus where the best data can and should be obtained from other places (e.g., registries, patient reported information, mobile monitoring devices)

9, 10, 14

29 Improve the data dictionary and categories of information aligned to meaningful use standards

9

30 Evaluate the usability of current data elements (structured or unstructured); determine what data is most feasible and useable and enhance that [Reflection: Some structured data is not even useable.]

9

31 Create a more effective means for EHRs to automatically consume Health Quality Measure Format (HQMF) or substitute [Note: HQMF is a standard for representing a health quality measure as an electronic document, developed by NQF, HL7, HIMA, and Alschular Associates.]

7, 8, 9 NT

32 Provide guidance on how to use templates, Quality Data Model (QDM), etc. to assist in standardizing how information is captured in EHRs [Reflection: A better means is needed to standardize data capture.]

5, 7, 8, 9, 10

NT

33 Gain general consensus from stakeholders on use of higher value data elements (e.g., National Library of Medicine shared value sets)

5, 7, 8, 9, 10

NT

34 Examine tools to incorporate unstructured data into eMeasures (e.g., natural language processing, Atlas.ti)

9 MT

35 Create a common way to represent data across clinical decision support (CDS) and quality measurement in order to be able use the data more effectively

6 MT

36 Increase standardization of data, data definitions, nomenclature, medical terminology, code sets, data submission and exchange methods, and value sets to support more efficient generation of quality measures

9

37 Develop a more centralized and uniform distribution of value sets, a singular platform for measurement, and consistent development of a measure from multiple sources

9

38 Increase standardization in specifications, vocabularies, clinical document architectures, data, and data exchange to improve efficiencies in the process of creating and implementing eMeasures [Reflection: It is important to have structured, standard data for measure specification.] [Reflection: Architecture data models, standards, and technologies must support accurate aggregation on longitudinal data across the continuum; this may require data governance plans to ensure maintenance and integrity of the data.]

9, 14

39 Assess which measures may be more likely candidates for natural language processing (NLP)[Reflection: Natural language processing may have potential in validating measures, identifying patient inclusions and exclusions, and identifying required data that is typically in free text.] [Reflection: Some RFI respondents suggested that psychiatric measures, radiology measures, and pathology measures may be good candidates for NLP but additional research is required.]

9

http://www.healthit.gov/policy-researchers-implementers/meaningful-use

http://www.healthit.gov/policy-researchers-implementers/meaningful-use

http://www.hl7.org/implement/standards/product_brief.cfm

http://www.qualityforum.org/QualityDataModel.aspx

https://vsac.nlm.nih.gov/

http://www.atlasti.com/index.html

22




ocia

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RFI

Q

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Nea

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(M

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RFI

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Focu

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40 Continue to assess the correct balance between structured and unstructured data in an EHR [Reflection: This balance of structured and unstructured data is a difficult one, since if represents a trade-off between flexibility and standardization.]

9

41 Create new means and methods to improve data quality, particularly if data will be reused downstream

10

Data Access, Sharing, Aggregation, and Integration

42 Connect EHRs, personal health records (PHRs), other clinical data sources (e.g., registries, laboratory data, vital records, patient entered data, home health care and long term care data, device monitoring data) and administrative data sources via HIE in order to properly integrate bidirectional information needed for electronic quality measurement

3, 7, 10

NT/MT

43 Remove barriers to data sharing by resolving issues around privacy, sensitive information, governance, and data ownership. Offer model policies that can be implemented by others [Reflection: Policy is the primary challenge in data sharing, not technology.]

14

44 Establish common patient identifiers within localities to track patients across systems and care settings for care coordination and patient safety [Reflection: Although a National Patient Index is not necessary, there needs to be some means to track patients. HIEs that have succeeded have a master patient index to cross organizational boundaries. Some have used mobile phone numbers.]

14

45 Continue to encourage EHR adoption [Reflection: Many providers, especially in post acute care settings, still do not have EHRs.]

10, 14

46 Use meaningful use as a lever to improve data integration, aggregation, and sharing; consider a third track which will encourage providers to develop and test new concepts such as model policies or new constructs

14

47 Facilitate a common understanding and rules around data sharing and data ownership 3, 7 NT

48 Develop interfaces to extract meaningful clinical data and solutions that aggregate data across a longitudinal record providing a “one-patient” view

14

49 Continue to resolve technical challenges with interoperability. 10, 14

50 Design and develop new rules engines to extract data from EHRs, integrate data with other sources, and present quality information at the point of care

14

Patient Engagement

51 Better engage patients, families, and caregivers in conversations around measurement; offer town hall meetings, online forums, PHRs, or portals to facilitate consumer engagement (including better engagement with their providers)

2, 4

52 Determine and implement a means by which to directly ask patients/consumers what information they would like to have for shared decisionmaking (i.e., what do they want to know?) [Reflection: Consumers’ views on quality are different than clinicians; quality needs to also be defined through the eyes of patients and how they want/need to use information.]

4

53 Identify the most effective tools (e.g., kiosks, Web sites/tools, PHRs/portals, mobile) and channels (e.g., faith-based organizations, employers, magazines) for presenting information to consumers that is of value to them

2, 4 MT

23




ocia

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RFI

Q

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Nea

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NT

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(M

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RFI

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Focu

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54 Provide quality information consistent with what patients and consumers want and need to know in order to make decisions. [Reflection: Patients want to know how to select the right provider for their condition, how to evaluate the quality and value of care they receive, how they are progressing against benchmarks, whether their doctors are speaking to one another, etc.]

4

55 Provide quality information in formats that can be easily understood and useful for decisionmaking. [Reflection: Information is currently disorganized and incomplete, if available at all.]

4

56 Involve patients/consumers in the process of designing how information will be presented to them [Reflection: Tools and interfaces need to be designed WITH consumers, not as others seek to design with consumers in mind.]

2, 4

57 Educate consumers on why quality measurement is important and how it can benefit them

4

58 Determine the information that could be collected from patients that will be most helpful for patient decisionmaking and/or most positively impact health outcomes

2, 4 NT

59 Identify the most effective means by which to capture and integrate patient reported information

2, 4 MT

60 Increase consumers’ ability to contribute quality information through various technologies (e.g., patient portals, PHRs, interactive mobile devices, telehealth)

4

61 Implement the CAHPS® family of surveys more widely with the government’s help 4

62 Collect personal goals from patients/caregivers and integrate with clinical goals 4

63 Personalize existing measures for consumer use in decisionmaking 2, 4 MT

64 Assess the feasibility of a central source or endorsement process for publically available quality information

2, 4 MT

65 Provide information to patients/consumers both on the macro (clinician-specific) and micro (disease- or episode-specific) levels. Sometimes patients/consumers want information about a given provider (e.g., quality, cost, and location), but it may also be valuable to know how a provider performs in treating a certain disease

4

66 Use meaningful use information (e.g., Dr. X has an EHR, reports quality information, is part of a patient centered medical home (PCMH) as a means by which to provide valuable information to patients/consumers

4

67 Increase patient access to different types of information and provide a means by which patients/consumers can better understand the information presented to them [Reflection: Consideration must be made for patients’ ability to access and understand their own data.]

4

68 Use post acute care as a test bed for providing quality information to patients/consumers [Reflection: Post acute care may be the best places to get input from patients and families and would allow a view into longitudinal and episodic care.]

4

69 Conduct further research around information that will have the most meaning to patients/consumers in the future; this could be modeled off of the Informed Medical Decisions Foundation, the Robert Wood Johnson Foundation Consumer Purchaser Discloser Project, or the work being done by the Centers for Aging Services Technologies

2, 4

24




ocia

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RFI

Q

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Nea

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NT

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(M

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RFI

Res

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Focu

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Collaboration and Education

70 Convene ongoing AHRQ, other government, or private third-party organized training/workshops for vendors, measure developers, providers, and other stakeholders to educate one another on a variety of topics (e.g., capabilities, roles, how to build infrastructure in the payer environment, how to understand State-specific processes) as well as to form collaboratives to discuss common challenges and share best practices

2, 5, 8, 11, 12

NT

71 Develop a common vocabulary between measure developers and vendors (e.g., terminology, taxonomy)

3, 5, 8, 12

NT

72 Leverage technical assistance and Regional Extension Centers (RECs) to assist frontline providers with electronic quality measure (eMeasure) implementation, data capture, and report generation [Note: RECs are entities funded by the Office of the National Coordinator for Health IT to help providers adopt and use EHRs.]

7, 11 NT/MT

73 Create a learning and resource portal for measure developers, vendors, providers, and other stakeholders that will enable access to educational material, vendor capabilities for measure development, and best practices across the eMeasure development and implementation lifecycles

2, 5, 8, 11, 12

NT/MT

74 Develop curricula for consumers and purchasers on what is available pertaining to quality information and how to interpret it; create a lexicon for consumers and purchasers

2, 4

75 Engage consumers and purchasers as early as possible around activities that include them or are directed toward them

2, 4

76 Acknowledge constraints in participation based on bandwidth (e.g., consumer advocates, purchasers, vendors) and assess value in participation; if value is assessed as high, strategize how to get critical stakeholders more involved

2, 4, 11, 12

77 Continue to foster collaborative relationships (e.g., informaticists and measure developers) and/or leverage professional associations as a means by which to reach providers

11, 12

78 Better engage providers—particularly frontline providers, pharmacists, and post acute care providers—in eMeasure development and implementation; use medical societies as possible intermediaries if directly engaging providers is too difficult (i.e., it may be hard to engage providers already feeling burdened by measurement activities)

2, 11

79 Use real life examples, case studies, success stories, etc. to engage providers 11

80 Create financial incentives or incentive models to encourage engagement and collaboration of providers, measure developers, and vendors

5, 11, 12

81 Provide opportunities for multistakeholder engagement, such as collaboratives and forums, to allow for greater understanding of different stakeholder perspectives (e.g., need for vendors to better understand measure development and measure developers need to better understand particular aspects of technology), to create a better understanding of the opportunities available (e.g., data that can be leveraged) to improve health IT-enabled quality measurement, and to discuss key topics, such as cross-setting measurement

2, 5, 11, 12

http://www.healthit.gov/providers-professionals/regional-extension-centers-recs

25




ocia

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Q

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Focu

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82 Offer conferences, continuing medical education, and Webinars to educate providers on quality measurement, including topics such as how health IT-enabled quality measurement leads to improved care for patients and economic practice enhancement, risk adjustment, informatics competencies, and terms and technologies

11

83 Engage medical specialty societies and professional organizations as a means by which to reach providers, as well as to assist in communication and collaboration among providers, measure developers, and vendors

11

84 Build provider confidence and demonstrate that the technology does not hinder care or impact the provider adversely [Reflection: Providers will be more engaged when they believe that health IT-enabled quality measurement improves patient care and practice.]

11

85 Offer a variety of technologies, tools, and communication channels (e.g., collaborative pilot studies, focus groups) to facilitate bidirectional communication and collaboration between measure developers and vendors.

12

86 Engage frontline clinicians and representatives from professional societies on advisory panels for measure development

3, 5, 11, 13

NT

87 Convene proactive panels or interactions among vendors, measure developers, and providers as part of a transparent measure development and implementation process

5, 7, 8, 11, 12

NT

88 Conduct research and publish case studies in high-impact journals to illustrate value of quality measurement

11

Other Topics and Recommendations

89 Harmonize similar measures and value sets used in measures [Reflection: Too often measures are similar but cannot be compared because of differences in denominators, exclusion criteria, or ways in which data is collected.]

7, 11 NT

90 Align measures and incentives across public (e.g., Meaningful Use, EHR Certification, Physician Quality Reporting System (PQRS) and private programs to reduce burden in quality measure data collection and reporting)

7, 11 NT

91 Align new measures to a standard framework, such as the National Quality Strategy or the “Triple Aim”

3

92 Develop an integrated systems approach to the eMeasurement lifecycle (e.g., development, testing, endorsement, implementation), which incorporates provider workflow (e.g., CDS tools), EHR certification, and Federal reporting requirements

3, 6, 7, 8, 11, 12

NT/MT

93 Use real-time quality measurement to identify a gap in care, prompt a provider to take a specific action to improve the outcome for a patient, change a course of treatment, or refine a care plan.

6

94 Use real-time reporting to assist with overall improvement of care quality 6

95 Research consumer trust of health IT (as opposed to IT in other industries, such as banking)

2, 4 NT

96 Research the cause of eMeasure implementation variation 8, 12 NT

97 Identify the correct financial incentives to improve performance 11, 13 NT

98 Research the most effective ways to merge clinical data and claims data 14 NT

26




ocia

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99 Research the best methodologies for case mix and risk adjustment 11, 13 NT

100 Research the best way to include patient-reported outcomes 2, 4 NT

101 Research the transition from paper-based measures to eMeasures to share lessons learned 7, 11 NT

102 Create aspirational measures to drive future technological development 5 NT

103 Research to determine the measures of greatest impact to patient outcomes 2, 4 NT

104 Research the elements that impact consumer choice in other industries for adoption in health care

2, 4 NT

105 Research the future alignment of clinical decision support and quality measurement 6, 11 NT

106 Research the impact of quality metrics on consumer choice in health insurance exchanges

2, 4 MT

107 Research topics in Comparative Effectiveness NA MT

108 Research the impact of small variations in measure attributes (i.e., elements of a measure), data collection, and measurement outputs on health care outcomes

3, 9

109 Research and understand a path forward in incorporating NLP [Reflection: Need a longer term vision for NLP.]

9

110 Research options for effective HIE self-sustainability; could look at heavily architected HIEs and the impact of local reimbursement rates, especially around those with PCMHs and Accountable Care Organizations (ACOs) where HIE is more important

10, 14

111 Research additional methods for effective data sharing [Reflection: The Prescription Drug Monitoring Program may be a valuable use case on how to share information.] [Reflection: States have come together to share information through this program but there are still challenges around how to import and print results.]

14

https://www.bja.gov/Default.aspx

https://www.bja.gov/Default.aspx

27



4. Pursing Pathways to Achieve eImprovementsQuality measurement enabled by health IT is important to delivering high-quality, safe, and affordable patient-centered health care in the United States. Quality measurement information can be used to maximize quality improvement efforts, inform consumers, and reward high-quality performers. Advancing health IT-enabled quality measurement is a dynamic enterprise with many advancements occurring through the efforts of a diverse set of stakeholders throughout the health care system. Stakeholders are actively engaged and moving toward eImprovement—measurement which has evolved beyond “checking the box.” This means that health IT would need to facilitate measurement so that it can be shared back with end users (e.g., clinicians, care givers, patients) in a timely manner to improve health care outcomes. eImprovement is reliant upon rapid feedback loops that would be supported by meaningful, actionable information at the patient level, provider or health care practitioner level, hospital and health system level, and at the population level.

The importance of informed collaboration was a major theme throughout the development of this report. Incremental advancements such as those identified in the Practical Guidance: Table of Suggested Steps Toward Enhanced Health IT-Enabled Quality Measurement require informed and candid collaboration, although preferred collaboration approaches varied widely. Careful thought needs to go into the design of collab-oratives to ensure engagement of the breadth and depth of stakeholder groups that is required for success. The ONC/CMS Kaizen event held in January 2013 is an example of the type of cross-contractor, cross-agency, and cross-stake-holder collaboration that is needed to sustain continued advancement of health IT-enabled quality measurement. Such specific, well-managed

collaboration could be replicated across a number of other key topic areas and could be initiated by any number of entities. Appendix E contains a list of resources, which includes links to collaboration and ongoing dissemination activities (e.g., National Quality Strategy, AHRQ, ONC, CMS).

The various issues presented in this report—measure development, implementation, and testing; data elements, data capture, and tools to process unstructured data; data access, sharing, aggregation, and integration; patient engagement; and collaboration and education—need to be reconsidered periodically to facilitate the pri-oritization of activities needed to continue the advancement of health IT-enabled quality measurement. Stakeholders engaged in the ongoing dialog will need to reflect on the various perspec-tives on quality measurement identified in this report to continue the path forward. Individuals and institutions may see their objectives, priorities, and paths forward differently. Patient perspec-tives will particularly need to be considered. Additionally, new perspectives may emerge with advancing technological capabilities and changing environmental factors (e.g., evolving medical curriculum, certification requirements in the use of EHRs, quality measurement, quality improvement; reimbursement; delivery system). “Furthermore, measure concepts must be prioritized based on the potential population-wide effect of achieving improvements in that measure.”1

With continued collaboration, the paths forward may be different but the destination will be the same—the successful next generation of quality measurement. Evolving quality measurement enabled by health IT can facilitate eImprovement and provide a foundation for advancing the “Triple Aim” of better health and better care at a lower cost.

28



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A-1



Appendix A. Public and Stakeholder Insights

A.1. Overview

A 15 question Request for Information (RFI) was posted in the Federal Register in July 2012 following the publication of an Environmental Snapshot—Quality Measurement Enabled by Health IT: Overview, Challenges, and Possibilities. The Environmental Snapshot identified seven key stakeholder groups—consumers, providers, payers, health IT vendors, government, measure developers and endorsers, and researchers. Questions for the RFI were developed with these stakeholder groups in consideration. Over the course of December 2012 and January 2013, stakeholder-specific (government and nongovernment) focus groups (e.g., government, consumers, providers, payers, health IT vendors, and measure developers and endorsers) were held that further explored topics that were raised in the RFI; members of the research community were included in each of the focus groups. In April 2013, a multistakeholder focus group was held that built on the RFI and previous focus group findings.

Exhibit A-1. Distribution of RFI Respondents by Stakeholder Group

A.2. Public Response to the Request for Information

An RFI was published in the Federal Register on July 20, 2012 with an original comment period of 30 days. The comment period was extended for an additional 30-day period due to the number of requests for extension. There were a total of 64 responses. Sixty-three of the respondents were unique; one of the respondents submitted a response to a single question during the first 30 days and then submitted a more complete response during the second 30 days that included the response

from the original submission. The respondents were first categorized by the pre-determined list of stakeholder groups (described in the Environmental Snapshot and later used as the basis for the focus groups). Exhibit A-1 illustrates the distribution of respondents by stakeholder group.

A.2.1. Question-by-Question Analysis

The RFI included 15 separate questions. Analysis was conducted on a question-by-question basis, as well as across all questions. Fourteen respondents answered all of the questions, 39 answered some of the questions,

http://healthit.ahrq.gov/sites/default/files/docs/page/NRCD1PTQ%2520Final%2520Draft%2520Background%2520Report%252007102012_508compliant.pdf


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and 10 provided general comments on the topic of quality measurement enabled by health IT without alignment to any particular question. Additionally, responses with general comments that did not relate to a specific question (e.g., questions within response labeled, but additional substantive material provided outside of those labeled responses) were labeled “other.” Exhibit A-2 provides a count of RFI responses to specific questions by stakeholder group.

Exhibit A-2. Count of Responses to RFI Questions by Stakeholder Group

Question #

All Respondents

(n=63)Providers

(n=26)Vendors (n=13)

Payers (n=4)

Government (n=2)

Measure Developers

(n=2)Consumers

(n=2)Others (n=14)

Question 1 (Roles and Interest) 48 19 11 2 2 2 2 10

Question 2 (Unheard Voices) 44 18 9 2 2 2 2 9

Question 3 (Infrastructure to Support Difficult to Generate Measures)

41 17 8 3 1 2 2 8

Question 4 (Engaging Consumers) 37 14 10 3 - 2 2 6

Question 5 (Leveraging EHR Data for New Measures) 40 18 9 2 1 2 2 6

Question 6 (Real Time Reporting and CDS) 46 19 11 4 1 1 2 8

Question 7 (Strategies in eMeasure Generation) 35 15 9 1 - 2 1 7

Question 8 (MAT and Other Approaches to Effective eMeasure Development)

38 18 8 2 2 2 1 5

Question 9 (Data Standards and NLP) 42 17 10 3 2 2 - 8

Question 10 (Longitudinal Measures and Data Reuse) 34 16 7 2 1 2 - 6

Question 11 (Educating and Engaging Providers) 44 19 10 3 1 2 1 8

Question 12 (Bidirectional Communication between Measure Developers and Vendors)

35 16 9 2 - 2 1 5

Question 13 (Payment Models) 25 13 7 3 - 1 - 1

Question 14 (Aggregation and Automation) 22 9 7 1 - 1 1 3

Question 15 (Examples) 23 8 9 2 - 1 1 3

Other Comments 48 19 11 2 2 2 2 10

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Question 1: Briefly describe what motivates your interest in clinically-informed quality measures through health information technology. To what extent is your interest informed by a particular role (e.g., provider, payer, government, vendor, quality measure developer, quality improvement organization, standards organization, consumer advocate) in this area?

In an effort to correctly categorize respondents into a particular stakeholder group, respondents were asked to what extent their interest was informed by a particular role. This information, in addition to reviewing the respondents’ Web sites and any self-identified information within the RFI responses, provided the basis for the categorization of respondents into stakeholder groups. Exhibit 2 illustrates the breakdown of the 63 respondents by stakeholder group. Forty-one percent of the respondents were providers or organizations representing providers. Twenty-one percent of the respondents were vendors or organizations representing vendors. Six percent were payers or organizations representing payers. Three percent of the respondents came from departments within two different government agencies. Another 3 percent of respondents were primarily classified as measure developers. Three percent of the respondents were from organizations that represent consumers. Twenty-two percent of the respondents were categorized as “other.” This included IT and informatics-related professional associations, consultants, miscellaneous nonprofit organizations, a standards organization, an academic institution, and a researcher (nonphysician).

Question 2: Whose voices are not being heard or effectively engaged at the crucial intersection of health IT and quality measurement? What non-regulatory approaches could facilitate enhanced engagement of these parties?

There were 44 responses to Question 2. Sixty-eight percent of the respondents to this question responded that providers were an unheard voice; particularly, frontline providers and post acute care providers. Most of the respondents who indicated that providers were unheard were providers or organizations representing providers. Using real-life examples (e.g., other providers sharing their personal experiences, case studies), funding, and collaboratives were recommended as approaches to facilitate engagement with providers. Forty-five percent of the respondents to this question indicated that patients, families, and their caregivers are not effectively engaged at the intersection of health IT and quality measurement. Respondents recommended town hall meetings, online forums, and the use of personal health records (PHRs) or portals as ways of facil-itating consumer engagement. Twenty-three percent of the respondents to this question suggested that EHR vendors are unheard. The respondents to this question recommended forums and collaboratives would be a useful mechanism for EHR vendors to be able to discuss implementation issues. Exhibit A-3 illustrates how different stakeholder groups responded to this question.

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Exhibit A-3. Unheard Voices by Responding Stakeholder Group

Who is an Unheard Voice? Tota

l Res

pons

es Respondent Classification

Pro

vide

rs

Vend

ors

Gov

ernm

ent

Mea

sure

D

evel

oper

s

Con

sum

ers

Paye

rs

Oth

er

Providers (esp. frontline providers and post acute care) 30 17 5 1 1 6

Patients, their families, and caregivers 20 4 6 1 2 2 5

EHR vendors 10 4 2 1 3

Payers and/or Purchasers 4 3 1

Health informaticists 4 3 1

Measure and guideline developers 3 2 1

Standards developers 3 2 1

Quality organizations 3 2 1

EHR and measure implementers 3 2 1

Consumers of quality information and quality reports 3 1 2

Other groups of unheard voices 9 4 2 1 1 1

Regardless of the unheard voice, respondents to this question across all stakeholder groups indicated that multistakeholder collaboratives and forums are particularly important. One respondent went so far as to state, “it is not so much that all voices are not heard, but they are not heard in the same place.” Additional recommendations included the publication of further research studies (e.g., feasibility, population, measure testing) in the area of quality measurement enabled by health IT.

Question 3: Some quality measures of interest have been more difficult to generate such as measures of greater interest to consumers, measures to assess value, specialty-specific measures, measures across care settings (i.e., measures enabled by health information exchange), and measures that take into account variations in risk. Describe the infrastructure that would be needed to ensure development of such measures.

There were 41 responses to Question 3. The question asked generally about infrastructure and the majority of responses to this question could be classified into one of four categories (see Exhibit A-4): 1) technologic elements of the potential infrastructure (e.g., standard definitions of data elements, tools for aggregation, the need for greater interoperability), 2) policies needed for the infrastructure (e.g., incentives, new payment models to emphasize shared accountability), 3) elements for governance required (e.g., organizational infra-structure to support interdisciplinary collaboration and communication, the need for medical specialty societies and professional organizations to be more engaged in measure development and governance), and 4) processes needed for the infrastructure (e.g., severity and risk adjustment, test cases, field testing).

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Exhibit A-4. Categories of Required Infrastructure

Response Categorization Counts % of Total (41)

Technology 29 71%

Policy 16 39%

Governance 14 34%

Process 13 32%

Vendors were most likely to discuss technology; however, they also frequently brought up the importance of governance. Two organizations (one a large provider organization and the second a health IT professional association) specifically mentioned the National Quality Strategy and that measures and priorities from Federal programs should be aligned with the National Quality Strategy. Additionally, two respondents to this question explicitly discouraged focusing on a specific technological infrastructure with a caution that it could stifle innovation.

Question 4: What health IT-enabled quality measures, communication channels, and/or technologies are needed to better engage consumers either as contributors of quality information or as users of quality information?

There were 37 responses to question 4. Forty-three percent of Question 4 respondents recommended personal health records and patient portals to facilitate consumer engagement and communication with physicians. Thirty-eight percent recommended the use of innovative technologies (e.g., mobile, telehealth, tablets). Twenty-four percent indicated that patient-reported data needs to be better integrated with other clinical data and better incorporated throughout the standard physician workflow. Respondents across all stakeholder groups agreed on the importance of effectively educating consumers, particularly regarding why measurement is important and how the information can benefit them. Two respondents commented that more information is needed from trusted third-parties that translate health care quality data into information that consumers can use. Respondents also generally agreed on the importance of consumers as contributors of quality information; a common theme was the use of various technologies (e.g., patient portals, personal health records, interactive mobile devices) to support consumer contributions.

Question 5: How do we motivate measure developers to create new health IT-enabled quality measures (which are distinct from existing measures which were retooled into electronically-produced quality measures) that leverage the unique data available through health IT? Please provide examples of where this has been successful. What new measures are in the pipeline to leverage data available through health IT?

There were 40 responses to Question 5. Forty-three percent of Question 5 respondents recommended the development of strong, national, large-scale collaboratives that engage all stakeholder parties. Thirty-six percent of the respondents to this question indicated that a revenue stream for measure development or financial incentives/development contracts might motivate measure developers; however, none of the respondents who have developed measures mentioned financial incentives. Additionally, 14 percent of the

http://www.ahrq.gov/workingforquality/

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respondents to Question 5 indicated that education (e.g., process of measure development, available technol-ogies, stakeholders’ roles, implementation best practices) for the stakeholders is critical. One consumer group noted that many organizations (e.g., State-based, mental health, substance abuse, disability) are far behind in the development of such measures due to a lack of technology, which has not been widely adopted, largely because of the lack of financial incentives and lack of access to the same technical assistance commonly received by hospitals and other medical systems. It was also suggested by one respondent to this question that clinical need and evidence, not the availability data, should dictate which measures should be pursued; new measures should be clinically informed, improvement focused, evidence-based, and data derived in spite of whether they can be fully supported by health IT. Exhibit A-5 illustrates how different stakeholder groups responded to this question.

Exhibit A-5. RFI Respondent Recommendations for Motivating Measure Developers

How to Motivate Measure Developers Tota

l Res

pons

es Respondent Classification

Pro

vide

rs

Vend

ors

Mea

sure

D

evel

oper

s

Con

sum

ers

Paye

rs

Oth

er

Strong, national, large-scale collaborative are needed to engage all stakeholder parties. 12 4 4 1 3

Revenue streams for measure development, financial incentives, and development contracts would further motivate measure developers.

10 1 3 1 2 3

Education to provider communities, measurement developers, and other stakeholders is needed to increase engagement.

4 2 1 1

Specialists and specialty societies should be engaged and encouraged to participate in the measure development process to ensure more health IT-enabled quality measures for specialists.

3 1 2

A business case demonstrating the return on investment (patient outcomes or financial) would strengthen the case for measure developers to pursue complex measures.

3 2 1

The NQF Framework for multiple chronic conditions provides many insights, including the promotion of the critical exchange of key variables to better manage individuals across settings.

2 2

Further research is needed to encourage measure development, including research to further develop the evidence base.

2 1 1

A framework or standardized process for the development of quality measures enabled by health IT would be useful to measure developers.

2 2

Private and public payers should require electronic specification of measures. 2 1 1

Front line physicians should be encouraged to participate in the measure development process.

2 1 1

Additional tools (e.g., data standards, guidelines for data integration) would be helpful to measure developers.

2 1 1

The ability to exchange and access broader data sets freely is needed. 2 1 1

Other potential motivators include payers recognizing the value of registries, quality assurance tools, a greater alignment of measures across Federal programs, and considering nonclinical data.

4 4

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A number of the Question 5 respondents offered examples of successful eMeasures. Where further information could be found in publically available resources, these activities were added to the Partial Catalog of Current Activities To Improve Quality Measurement Enabled by Health IT (Appendix C).

The third part of the question asked respondents what new measures are in the pipeline to leverage data available through health IT. Five Question 5 respondents mentioned that they are in some stage of planning or developing quality measures enabled by health IT; however, they did not provide specifics about which measures they are developing.

Question 6: Describe how quality measurement and “real-time” reporting could inform clinical activity, and the extent to which it could be considered synonymous with clinical decision support.

There were 46 responses to Question 6. Sixty-one percent of the Question 6 respondents indicated that real-time quality measurement can identify a gap in care or prompt a provider to take a specific action to improve the outcome for a patient. Twenty percent of the respondents to this question indicated that real-time reporting can assist with overall improvement of care quality. Fifteen percent of the respondents to this question suggested that real-time information can prompt a provider to change a course of treatment or refine a care plan. Another 15 percent of respondents suggested that real-time quality measurement provides real-time information to providers and patients to make informed medical decisions. Respondents to this question also described the extent to which real-time quality measurement and clinical decision support are synonymous. Fifty-eight percent of the Question 6 respondents indicated that real-time quality measurement has broader uses than just clinical decision support; 26 percent stated that real-time quality measurement supports clinical decision support; and 16 percent indicated that real-time quality measurement could be considered synonymous to clinical decision support. Moreover, respondents to this question further observed that not all measures may be suited for real-time quality reporting (e.g., structural, outcome, or volume measures have limited value at the point of care), but for those that are well-suited, results must be delivered to clinicians in ways that are automated and actionable. Two vendors offered ideas on how to most effectively present data (e.g., prioritized alerts, dashboards).

Question 7: Among health IT-enabled quality measures you are seeking to generate in a reliable fashion, including the currently proposed Meaningful Use Stage 2 measure set, what types of advances and/or strategies for eMeasure generation, if pursued, would support more efficient generation of quality measures?

There were 35 responses to Question 7. Forty-six percent of the Question 7 respondents suggested that greater standardization of data, data definitions, nomenclature, medical terminology, code sets, data submission and exchange methods, and value sets are needed to support more efficient generation of quality measures. Seventeen percent of the respondents to this question indicated that harmonization of measures, rule sets, frameworks, and standards are needed across all Federal reporting or measurement programs. An additional 17 percent of the respondents suggested a more centralized and uniform distribution of value sets, a singular platform for measurement, and the consistent development of a measure from multiple sources is needed. Seventeen percent of the respondents to this question recommended that data collection and quality measurement should be a byproduct of care and should represent an understanding of operational

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and clinical workflows for an efficient generation of quality measures. Question 7 respondents reporting on Meaningful Use experiences described inaccuracies within eMeasure specifications, the inability to access necessary clinical information, and a lack of sufficient field testing. However, creating a framework for measure development that leverages the EHR incentive program may help these issues in future stages.

Question 8: Many EHR, HIE, and other health IT vendors are developing software code to support measures. Tools such as the Measure Authoring Tool (MAT) were created to improve efficiencies in the process of creating and implementing eMeasures. What additional approaches might be used to enable consistent, accurate, and efficient quality measurement when using health IT?

There were 38 responses to Question 8. Thirty-four percent of the Question 8 respondents indicated the need for standardization in specifications, vocabularies, clinical document architectures, data, and data exchange to improve efficiencies in the process of creating and implementing eMeasures. Twenty-four percent of the respondents to this question indicated that communication, coordination, and collaboration are needed among stakeholders. Additionally, 18 percent of respondents indicated that greater field testing is needed to define validity and accuracy of the data, the measure specifications, and the measure itself. Furthermore, responding organizations that have participated in measure development recommended the continued use of the Measure Authoring Tool and the Quality Data Model; however, they suggested further refinement of these tools particularly in the ability to create longitudinal measures. One Question 8 respondent suggested that focusing on ideal specifications might limit and delay the development of meaningful measures, especially those most meaningful to consumers. A number of the respondents to this question expressed the need for greater understanding of different stakeholder points of view (e.g., need for vendors to better understand measure development and measure developers need to better understand particular aspects of technology).

Question 9: How do you see the establishment and adoption of data standards impacting the future of health IT-enabled quality measurement? For what types of quality measures should a combination of natural language processing and structured data be considered?

There were 42 responses to Question 9 (34 responded to the question around data standards; 28 responded to the natural language processing question). Fifty-three percent of the respondents to the standards question confirmed that data standards have a highly positive impact on quality measurement. Five of these respondents further indicated that data standards will ensure quality measures can be compared across settings. Three of these respondents indicated that data standards can improve interfaces between different IT systems within and across health care organizations and contribute to the quality and validity of the data. A number of the respondents also recommended activities to ensure data standards have a positive impact (e.g., standards need to be advanced and applicable to all providers, nonproprietary innovations and standards, outreach to clinicians).

Twenty-eight of the Question 9 respondents discussed the role of natural language processing in quality measurement. Respondents suggested that particular measures may be likely candidates for natural language processing (e.g., psychiatric measures, radiology measures, pathology measures). Moreover, natural language processing may have potential in validating measures, identifying patient inclusions and exclusions, and

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identifying required data that is typically in free text. Respondents to this question observed that it can be difficult to discern the right balance between structured and unstructured data in the EHR. It was suggested that natural language processing may not yet be advanced enough to ensure accuracy and usability in com-mercially available products, particularly in terms of comparing performance across settings. A number of respondents recommended further testing and demonstrations of natural language processing for the use in quality measurement.

Question 10: Much support has been voiced for the need of longitudinal data in quality measurement. What are the strengths and weaknesses of different information architectures and technologies to support health IT-enabled quality measurement across time and care settings? How can data reuse (capture once, use many times) be supported in different models? What examples might you provide of successful longitudinal health IT-enabled quality measurement (across time and/or across multiples care settings)?

There were 34 responses to Question 10. Twenty-three of these respondents replied to the first part of the question regarding the different information architectures and technologies to support health IT-enabled quality measurement across time and care settings. Thirty-nine percent of the Question 10 respondents indicated that data standards need to be improved. Thirty-five percent of the respondents to this question suggested that data warehouses or registries with patient level data and identifiers are needed. Thirty percent stated that broader interoperability is needed. Seventeen percent of respondents to this question also suggested that barriers around data need to be removed (i.e., ownership, privacy, and governance needs to be resolved) and 9 percent of respondents stated that data quality needs to be improved. Moreover, one of these respondents noted that until there is data liquidity, information will be captured in multiple settings and it will be difficult to reconcile this to “one source of truth.”

Ten of the Question 10 respondents discussed data reuse in their responses. Respondents generally concurred with the premise of data reuse (that data should be collected once and used as many times as is practical and possible); however, they did not specifically discuss how it might be supported in different models. Thirty percent of these respondents stated that data reuse requires consistency and standard approaches to recording information in health IT. Another 30 percent recommended databases and registries to ensure high- quality data collection, and 20 percent suggested that data collected as a byproduct of care will reduce provider burden and duplicative data entry.

A number of the Question 10 respondents offered examples of successful longitudinal health IT-enabled quality measurement. These examples included a number of registries. Respondents also referred to programs and activities within their own organizations working toward longitudinal measurement enabled by health IT. Where further information could be found in publically available resources, these activities were added to the Partial Catalog of Current Activities To Improve Quality Measurement Enabled by Health IT (Appendix C).

Respondents to this question also made comments specific to longitudinal measures and data reuse. Two respondents cautioned that too much attention on a specific technical infrastructure can inadvertently discourage or inhibit new, innovative methods and that a “one size fits all” approach will not work. Some respondents also cautioned that secondary use of EHR data must take into account potential data quality

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issues inherent whenever data collected for one purpose is reused for another. Finally, architecture data models, standards, and technologies must support accurate aggregation of longitudinal data across the continuum. This will require data governance plans to ensure maintenance and integrity of the data.

Question 11: What are the most effective means by which to educate providers on the importance of health IT-enabled quality measurement and how clinical information is used to support health IT-enabled quality measurement and reporting? How can providers be better engaged in the health IT-enabled quality measurement process?

There were 44 responses to Question 11. Of the 44 respondents, 30 discussed the mechanisms or modalities best suited to educating providers (Exhibit A-6). Twenty-three percent of these respondents suggested conferences, continuing medical education, and Webinars. An additional 23 percent of Question 11 respondents recommended research and case studies in high-impact journals and 23 percent suggested using medical specialty societies and professional organizations as intermediaries to educate providers. Several of these respondents recommended the types of information that need to be conveyed to providers. Respondents indicated that it is important to demonstrate to physicians that health IT-enabled quality measurement leads to improved care for patients and economic practice enhancement. Risk adjustment, informatics competen-cies, and terms and technologies were also suggested topics.

Exhibit A-6. Means by Which to Educate Providers by Respondent Stakeholder Group

Response Categorization Tota

l

Respondent Classification

Pro

vide

rs

Vend

ors

Gov

ernm

ent

Oth

er

Conferences, continuing medical education, and Webinars can provide educational opportunities. 7 4 1 2

Research, case studies, and examples of the effectiveness of quality measurement in high-impact publications and journals.

7 4 1 2

Medical specialty societies and professional organizations are well situated to educate providers. 7 4 1 2

Real world success stories, anecdotes, and personal education from fellow clinicians with similar backgrounds.

4 2 1 1

Vendor-offered training can educate and lead to future collaboration. 4 2 2

Medical student and resident training programs should incorporate education about quality measurement enabled by health IT.

4 1 3

Information and education needs to be built into health IT software. 2 1 1

Other educational modalities 9 3 2 2

Of the 44 Question 11 respondents, 32 discussed the means by which to engage providers in health IT-enabled quality measurement. Forty-seven percent of respondents to this question indicated that quality measurement results need to be useful to providers in terms of improving care. Thirty-four percent of Question 11 respondents suggested that quality measurement needs to be a byproduct of care, actionable, and

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intuitive. Moreover, quality measures should be harmonized across programs to reduce burden. Thirty-one percent of the respondents to this question reported that providers will become more engaged if health IT meets provider needs (e.g., improves productivity, syncs with workflow, is more user-friendly). Overall, respondents emphasized the importance of quality measures being evidence-based, valid, and reliable in addition to the measurement process being reliable and fair.

Two respondents to Question 11 commented that education is not the issue; there are other obstacles such as providers that have not yet adopted health IT, measurement burden, and providers bearing most of the risk and administrative cost of quality measurement programs. Some respondents also further suggested that success in the initial set of eMeasures is needed to build provider confidence and demonstrate that the technology does not hinder care or impact the provider adversely.

Some respondents to this question suggested specific roles for AHRQ and others in engaging providers. For example, one respondent suggested that AHRQ should look at how benchmarking tools could be established which may require the aggregation of data across competing EHR vendors. Another respondent suggested AHRQ should share success stories with providers of cases where outcomes were improved because of avail-ability of data (at the individual or population level).

Question 12: What is the best way to facilitate bidirectional communication between vendors and measure developers to facilitate collaboration in health IT-enabled measure development?

There were 35 responses to Question 12. All of the Question 12 respondents agreed that bidirectional com-munication between measure developers and vendors is critical to facilitating collaboration in developing health IT-enabled quality measures. Thirty-four percent of these respondents recommended a variety of technologies and tools to facilitate bidirectional collaboration. An additional 34 percent of the Question 12 respondents suggested different modes of communication (e.g., collaborative pilot studies, focus groups) to facilitate communication. Twenty-six percent of the respondents to this question suggested that the timing of communication is fundamental to facilitating bidirectional communication. Most of the respondents that commented on the timing of communication agreed that communication should take place early in the process, often, and ongoing throughout the process. Moreover, 17 percent of the respondents to this question suggested that medical specialty societies should be leveraged in these efforts and 14 percent of the respondents discussed the importance of funding for these types of efforts.

Question 13: To what extent do you anticipate adopting payment models that use quality measurement informed by electronic clinical records (as opposed to exclusively using claims data)? What strategies are you pursuing to gain access to clinical data and test the reliability of health IT-enabled clinical outcome measures? How do you anticipate sharing quality measure results with consumers and other stakeholders?

There were 25 responses to Question 13. Four of the Question 13 respondents specifically commented on the extent to which they anticipate adopting payment models using quality measures informed by health IT. These respondents indicated that they are already engaged in reimbursement models based on quality measurement and they anticipate this increasing over the next 3 to 5 years. Activities such as Accountable Care Organizations, Patient-Centered Medical Homes, health risk assessments, use of the SF36v2™ health

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survey, and the use of the CAHPS® survey were all mentioned in relation to these payment models. Some respondents expressed concern about the timing of these activities given that vendors are only beginning to develop and test components to support new payment models and many providers have yet to adopt basic health IT.

Six of the Question 13 respondents discussed gaining access to clinical data to test the reliability of health IT-enabled clinical outcomes measures. It was recognized by most of the respondents who use quality measurement data for reimbursement that testing of the reliability of health IT-enabled clinical outcomes measures would be imperative. Although most respondents are not yet heavily engaged in this area, a few were able to offer recommendations. Three organizations cited using registries to access clinical data and one organization indicated that they are engaging in a clinical data repository pilot to aggregate EHR and claims data. Another organization is planning an internal pilot to test the reliability of health IT-enabled outcomes measures using EHRs when compared with measures obtained through chart abstraction.

Many respondents to this question agreed that sharing quality information with consumers is a good idea. It can be a means by which to inform and engage patients in their care and even encourage adherence to prevention advice. While payer respondents indicated that they are already sharing information with consumers, most respondents are still exploring the most appropriate way to share results with consumers and other stakeholders. Keys to doing this include ensuring the accuracy of data and delivering the data in a way that is meaningful and useful to patients.

Question 14: What tools, systems, and/or strategies has your organization been using to aggregate information from various EHRs and other health IT for use in quality measurement? What strategies is your organization pursuing to move toward greater automation in quality measurement?

There were 23 responses to Question 14. Eight respondents to this question described tools or technologies that they are currently using for aggregation or automation. For example, one respondent mentioned the use of proprietary code, which uses a clinical rules engine to recognize quality data from the EMR and uses it to present quality information at the point of care. Seven Question 14 respondents described tools or technolo-gies that they are developing for improved aggregation or automation. Examples include interfaces to extract meaningful clinical data and solutions that aggregate data across a longitudinal record providing a “one patient” view. Three respondents described some of the challenges associated with greater aggregation and automation. For example, one barrier reported is the different languages that health care providers and other organizations speak (e.g., different terms used to describe same condition), which can result in duplication of effort. These variations in terminologies or languages also act as an impediment to advancements in aggregation and automation being replicated across multiple sites. Another barrier described was the lack of a technological infrastructure in some settings like home health care.

Question 15: Please describe scalable programs, demonstrations, or solutions (domestic or internationally) that show material progress toward quality measurement enabled by health IT.

There were 23 responses to Question 15. Respondents to this question shared 10 different programs, demon-strations, or solutions that they believe show material progress toward quality measurement enabled by health IT. An additional 10 of the Question 15 respondents shared programs and projects occurring within

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their own organizations. Where further information could be found in publically available resources, these activities were added to the Partial Catalog of Current Activities To Improve Quality Measurement Enabled by Health IT (Appendix C).

A.2.2. Key Concepts Across All Questions

In addition to the question-by-question analysis, responses were examined to identify themes across the questions. Exhibit A-7 represents a characterization of the RFI responses by question and provides a count of how many respondents’ answers mentioned meaningful use (MU), clinical decision support (CDS), health information exchange (HIE)/interoperability, or data standards.

MU was mentioned at least once in the responses to all of the questions except for Question 6 (Real Time Reporting and CDS) and was most frequently mentioned in Question 7 responses (Strategies in eMeasure Generation). CDS was most frequently mentioned in Question 6, which directly addressed CDS and HIE was mentioned most in response to Question 3 (Infrastructure to Support Difficult to Generate Measures). Data standards were most frequently discussed in Question 9, which directly addressed standards (Data Standards and natural language processing [NLP]).

Exhibit A-7. Total Mentions of MU, CDS, HIE, and Standards by RFI Question

Question # All

Res

pond

ents

(n

=63)

MU

CD

S

HIE

/In

tero

pera

bilit

y

Stan

dard

s

Question 1 (Roles and Interest) 48 8 4 9 9

Question 2 (Unheard Voices) 44 5 2 5 7

Question 3 (Infrastructure to Support Difficult to Generate Measures) 41 3 5 15 3

Question 4 (Engaging Consumers) 37 2 1 5 5

Question 5 (Leveraging EHR Data for New Measures) 40 7 5 4 6

Question 6 (Real Time Reporting and CDS) 46 - 33 - 1

Question 7 (Strategies in eMeasure Generation) 35 12 3 2 14

Question 8 (MAT and Other Approaches to Effective Measure Development) 38 6 2 4 15

Question 9 (Data Standards and NLP) 42 6 2 4 40

Question 10 (Longitudinal Measures and Data Reuse) 34 1 1 11 4

Question 11 (Educate and Engage Providers) 44 6 2 3 3

Question 12 (Bidirectional Communication between Measure Developers and Vendors) 35 5 - 4 6

Question 13 (Payment Models) 25 2 1 - -

Question 14 (Aggregation and Automation) 22 2 3 1 3

Question 15 (Examples) 23 2 - 2 3

Other Comments 48 6 3 4 8

Total Mentions - 73 67 73 127

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A.2.2.1. Responses by Stakeholder

Exhibit A-8 provides a breakdown of the MU, CDS, HIE/interoperability, and standards mentions by each stakeholder group that responded to the RFI. Forty-six percent of the organizations representing providers mentioned MU at least once in their RFI response. Additionally, 54 percent of this stakeholder group mentioned CDS, 46 percent mentioned HIE or interoperability, and 73 percent mentioned standards. Fifty-four percent of the vendor respondents mentioned MU in their RFI response, 85 percent mentioned CDS, 46 percent mentioned HIE or interoperability, and 77 percent mentioned standards. Fifty percent of payers mentioned MU at least once in their RFI response, 100 percent mentioned CDS, 74 percent mentioned HIE or interoperability, and 100 percent mentioned standards. Both of the organizations repre-senting consumers mentioned MU at least once in their RFI response, one of the organizations mentioned CDS, one mentioned HIE or interoperability, and one mentioned standards. Both of the measure developers mentioned MU, CDS, HIE/interoperability, and standards. One of the two government respondents mentioned MU; neither mentioned CDS; and both mentioned HIE and standards. Fifty-seven percent of the “other” respondents mentioned MU in their responses; 57 percent mentioned CDS; 86 percent mentioned HIE or interoperability; and 93 percent mentioned standards.

Exhibit A-8. Mentions of MU, CDS, HIE, and Standards in RFI by Stakeholder Group

Provider Response Categorization To

tal #

R

espo

nden

ts

MU CDSHIE/

Interoperability Standards

# % # % # % # %

Providers 26 12 46% 14 54% 12 46% 19 73%

Vendors 13 7 54% 11 85% 6 46% 10 77%

Payers 4 2 50% 4 100% 3 75% 4 100%

Consumers 2 2 100% 1 50% 1 50% 1 50%

Measure Developers 2 2 100% 2 100% 2 100% 2 100%

Government 2 1 50% - - 2 100% 2 100%

Others 14 8 57% 8 57% 12 86% 13 93%

A.2.2.2. Discussion of Identified Themes

Several topics recurred across questions. Prior to beginning the analysis, Meaningful Use, clinical decision support, and health information exchange and interoperability were identified as concepts that should be tracked across questions. Data standards, collaboration, medical specialty societies, lesser heard providers, and measure harmonization were identified as emerging themes while reviewing the responses.

Meaningful use. Meaningful use (MU) was mentioned by 34 of the 63 RFI respondents in their answers to RFI questions. Some of the respondents discussed their experiences with MU implementation and used these experiences to demonstrate some of the challenges with health IT-enabled quality measurement. A hospital network and an organization representing hospitals specifically noted: inaccuracies within eMeasure

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specifications, the inability to access necessary clinical information, a lack of measure stewards, and a lack of sufficient testing. Some providers felt left out of the MU program (e.g., nurse practitioners, hospice, home health care). However, some of the respondents were encouraged by MU and are hopeful that the incentive program and EHR certification requirements will expand further (e.g., include more provider types, specialty-specific requirements). Some of the respondents expressed concern about the validity of the EHR data. One respondent indicated that they conducted a study in which they compared automated electronic reporting to manual review of electronic records for 12 quality measures (10 from MU); they found that the accuracy of electronic reporting varied substantially across the MU measures. Additionally, a number of commenters suggested that government programs in general should be harmonized. One example provided by a respondent was their inability to report for the Physician Quality Reporting System (PQRS) via their EHR. The respondent indicated that EHR vendors must be prequalified by Centers for Medicare & Medicaid Services to submit PQRS data. Physicians who are using a system that has not gone through this process do not have the option to electronically report their PQRS measures, even if their system is certified for the MU program, which requires many of the same measures.

Clinical decision support. Clinical decision support (CDS) was frequently mentioned by respondents. Question 6, which asked about real time reporting and clinical decision support, received the most mentions of clinical decision support. Respondents to Question 6 suggested that real-time quality measurement and CDS are on the same continuum; however, most did not believe them to be the same. One respondent stated that quality measurement and CDS serve different roles; the goal of CDS is not to measure, but to guide providers in decisionmaking. Real-time quality measurement can be fed into CDS, which can improve patient care. However, some respondents were concerned about the ramifications of alert fatigue. One vendor suggested that more sophisticated dashboards could be developed that prioritize interventions that will have the greatest impact for the patient, where excess alerts may be otherwise overwhelming.

Health information exchange and interoperability. Health information exchange (HIE) and interoperabil-ity was mentioned at least once by each stakeholder group. HIE was most frequently mentioned in relation to Question 3 (architecture needed for measures more difficult to generate) and Question 10 (need for longitudi-nal data). Measures across settings and over time are particularly reliant on greater HIE. Respondents noted some of the challenges that are associated with greater HIE: some providers have not yet adopted EHRs; more standards are needed for exchange and interoperability protocols; vendors use proprietary code that can be difficult to facilitate exchange; data ownership, privacy, and governance have yet to be resolved; and patient identifiers are lacking.

Data standards. Data standards were mentioned by all stakeholder groups and were mentioned at least once in every question except for one (Question 6). Forty-nine of the 63 RFI respondents mentioned standards in their RFI responses. Data standards were most frequently mentioned in Question 9, which specifically asked about data standards. Respondents generally indicated that standards are needed to facilitate quality measurement enabled by health IT. Standards were often mentioned in the context of measure development (i.e., importance of having structured, standard data for measure specification). Comments on data standards can best be characterized by one respondent’s statement that “the balance between structured and unstruc-tured data in an EHR is a difficult one, since it represents a trade-off between flexibility and standardization.” In addition to data standards, respondents also commented on the importance of standards needed for interoperability and exchange protocols, value sets, and terminologies.

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Collaboration. Increased collaboration between all stakeholders was a recurring theme as a means by which to improve eMeasure development and implementation. A number of respondents suggested that large-scale, national collaboratives are the best mechanism for engaging a variety of stakeholders. Collaboration was par-ticularly discussed in relation to measure developers and vendors. Responses to Question 12, which focused on measure developer and vendor collaboration, indicated that this communication should take place as early and often as possible throughout the measure development and implementation process. Moreover, collabora-tion was also cited as a means by which to improve education and engagement of both patients and providers, especially those who feel lesser heard.

Medical specialty societies. Medical specialty societies were mentioned as potential partners in the measure development process and to engage frontline providers. A number of the respondents indicated that providers, particularly frontline providers, are unheard voices. Moreover, respondents suggested that frontline providers need to be more actively engaged in measure development. Medical specialty societies were suggested as possible intermediaries to engage these providers who are already burdened by measurement activities. These organizations are already actively educating and engaging providers, disseminate information, develop measures, and contribute to technical expert panels for measure development.

Lesser heard providers. Some of the respondents indicated that nonphysician and nonacute hospital providers are often overlooked in the conversation. For example, one respondent noted that nurse practi-tioners were not included in the MU program. Additionally, post acute care and long-term care providers (particularly home health and hospice) indicated that they are lagging behind in health IT adoption. One reason cited for lack of adoption is cost and exclusion from the MU incentive program. One respondent observed that failure to include post acute care providers will inhibit the ability for quality measurement across settings to be captured and used in any meaningful way. Moreover, there appears to be a lack of forums for discussion of such cross-care setting measurement. An organization representing pharmacists suggested that pharmacists are also unheard voices.

Harmonization. Harmonization was frequently mentioned by respondents as a way to reduce burden and to prioritize next steps. Respondents suggested that agreed upon frameworks are needed to drive harmoniza-tion. Respondents encouraged AHRQ to work with other Federal partners to align and harmonize existing activities and programs, Federal and State rules, regulations, and guidelines in order to reduce barriers to appropriate quality measure data collection and reporting.

A.3. Focus Groups

The initial findings from the RFI and the objectives for the forthcoming focus groups were presented to government stakeholders in December, 2012. Nineteen individuals participated representing eight agencies. Participants were invited to provide feedback on areas needing further information or activities of which they felt warranted for further pursuit with nongovernment stakeholders. Government participants were also invited to recommend individuals for the stakeholder-specific focus groups.

Between January 7 and January 17, 2013, five focus groups were held via WebEx to discuss issues relating to quality measurement enabled by health IT. Participants were sought that represented a variety of organization

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types and sizes, geographic regions, and experiences. Additionally, a combination of established voices and lesser known voices were invited to participate in the focus groups. Thirty-seven individuals representing vendors, measure developers, payers, consumers, and providers participated in the focus groups; no focus group included more than nine nongovernmental participants. Researchers were included across each of the focus groups. Focus groups were organized around each of these broad categories of stakeholders. While many similar themes were explored during each focus group, the questions were unique for each group. The questions were developed using the RFI analyses as a guide to identify areas where further information would be beneficial.

Each stakeholder-specific focus group had its own set of objectives and talking points. Broadly, the objectives were as follows:

�� Identify actionable, incremental advancements needed to move toward the next generation of health IT-enabled quality measurement.

�� Learn the primary challenges participants of each stakeholder group experience today and foresee in the future in accelerating the use of health IT for quality measurement.

�� Elaborate on successful strategies for engaging other stakeholders.

In addition to these objectives, participants were asked to prioritize the actionable, incremental advancements in terms of a near-term or a mid-term timeframe.

On April 1, 2013, a sixth nongovernmental focus group was convened that included a diverse set of stake-holders. This eight-person, multistakeholder focus group was held via WebEx to further discuss issues relating to quality measurement enabled by health IT. Eight individuals participated, representing measure developers, health IT vendors, consumers, payers, and providers. Often these senior-level participants represented experience and expertise in two or more stakeholder perspectives. Topics selected for examination were key topics discussed by RFI respondents and stakeholder-specific focus group participants. The questions were developed using the RFI and focus group analyses as a guide to identify areas were further information would be beneficial.

The objectives of the multistakeholder focus group were as follows:

�� Elaborate on identified actionable, pro-active, incremental advancements needed to move toward the next generation of health IT-enabled quality measurement.

�� Identify the chronological order of those incremental advancements in the near (1–2 years) and mid-term (3–5 years).

�� Discuss the requisite stakeholders—their particular challenges, needs, perspectives, and roles.

�� Identify risks or challenges toward accomplishing incremental advancements and possible mitigations.

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A.3.1. Focus Group Insights

Over the course of the focus groups, participants provided many critical insights related to the objectives described above. The following provides information on some of the key themes identified throughout the focus groups, which relate back to the focus group objectives.

A.3.1.1. Actionable, Incremental Advancements Needed

New measures. Although each focus group was approached differently, the types of new measures needed for the next generation of quality measurement were discussed in all of the focus groups. Care coordination measures and longitudinal measures (i.e., measures across time and settings) were of interest to many par-ticipants in both stakeholder-specific and multistakeholder focus groups. However, the lack of information exchange (i.e., the technical ability to transfer data between facilities and settings) and data liquidity (i.e., the ability of patient data to be accessed and moved throughout the health care system securely) makes these measures difficult to develop and implement. It was suggested by multistakeholder participants that it would also be helpful to measure the success of a team in providing care. One participant mentioned that Kaiser is piloting an effort to measure the success of a team in health care; lessons from this pilot may be useful. Payers would like to see new measures of efficiency, variation, and appropriateness of care; measures that provide a combination of clinical outcomes and cost are of particular interest to them. Multistakeholder participants indicated that there is a lack of measures on efficiency and value. It was suggested that better care coordi-nation and cost measures may be developed if payers and purchasers take an active involvement in measure development. Additional measures for specialty providers are also needed.

Participants across all focus groups indicated that clinical outcome, shared decisionmaking, and functional status measures would be useful, and likely of specific interest to consumers. In other words, measures are needed that help support consumers in caring for themselves and making decisions about their care. Ideally, measures would be meaningful to patients and better describe health care from the patient perspective as opposed to just the provider perspective. Moreover, the consumer participants raised the issue of making measures personal to patients. Risk adjustment measures are best understood in terms of the “average” or “typical” patient; however, patients are more interested in how a particular provider or care site would rate in terms of their specific conditions and/or demographic information. There also needs to be information available to help consumers interpret quality measurement results and research is needed to determine which patient-reported outcomes most improve the status of patients. Multistakeholder participants also discussed the concept of measures that matter to patients. They suggested that patients might want to learn about whether they are getting quality care and how they can evaluate whether the doctor is treating them well. For example, a patient with a particular condition might be interested in seeing how they score against a benchmark. Multistakeholder participants also suggested that patients might be interested in measures around whether they are receiving information on all of the treatment options and cost of care, as well as whether their physicians are communicating with each other.

Across stakeholder focus groups, participants recommended that new measures should be actionable; quality measures should be used to generate CDS and create actionable alerts. One participant indicated that not only should the measures be actionable, but that the specific role(s) responsible for action should be indicated

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(e.g., patient, physician, medical assistant, social worker). Moreover, multistakeholder focus group partic-ipants suggested that before developing and implementing measures, it is critical to determine whether measuring something really changes the outcome and whether small incremental variances in measurement really makes a difference in outcomes. Overall, participants suggested that there needs to be a better under-standing of the purpose of new measures. Moreover, it needs to be determined what data are needed and the best way to collect this data. For example, measures used for quality improvement are different than those used for quality reporting and the information needed for those measures may be collected differently. Participants agreed that it is important that the measures used are making a difference. It was suggested by several multistakeholder participants that the “Triple Aim” (i.e., better care, better health, and lower costs) could serve as a good framework to determine new measures needed that would offer the most value.

One participant suggested that aspirational measures developed for use in 5 years and then in 10 years could help create a vision that health IT could drive towards. It was also suggested that feasibility and the impact of deploying new measures should be assessed.

Harmonization. While it was generally agreed that stakeholders should continue to work toward greater harmonization, there was a general sense that true harmonization of measures would be too difficult and too expensive. One participant stated that harmonization was “next to impossible” because measures are developed both locally and nationally for different purposes. One participant indicated that they are working on new models for harmonization (e.g., data and methodology for measures). The lack of resources available for harmonization also was cited as a challenge, as well as measure ownership and stewardship. However, many participants suggested that alignment to and between Federal, public, and private entities is needed and that alignment to national priorities should be of greater focus than attempting to harmonize similar measures across varied uses.

Additionally, many participants raised the issue of a new measurement development lifecycle. Participants recommended that quality measurement should be aligned with provider workflow. Clinically justified measures would be developed in concert with emerging technology functionality and certification criteria and would also be aligned with workflow. This would ensure that all stakeholders and processes work together and that quality measurement information is more meaningful.

Testing. Participants across the stakeholder groups agreed that measurement testing is a critical activity. Vendors and measure developers were particularly concerned that measurement testing is not where it should be. Currently, there is too rapid a need to eSpecify measures, so the ideal steps for testing do not always occur or are rushed. Vendors suggested that it is important to begin testing while the measures are still being developed. Moreover, there needs to be a standard testing methodology and more funding for testing.

Additionally, feasibility, reliability, and validity of each data element need to be tested in addition to the measures. Ultimately, there is a need to incorporate testing throughout the development and implementation process. Lessons learned from reliability and validity testing should be made available to other stakehold-ers. However, there are a number of barriers that participants discussed. For example, measure developers had experienced a lack of willingness of organizations to incorporate and test; more funding is needed in this area given the cost to the organization testing the measure. Moreover, it was felt that there is a lack of

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best practices for testing in both scope and scale of measure testing. There was also concern that variation occurs when vendors implement the same measure in different sites, which is a factor of many variables (e.g., clinical workflow).

One participant suggested that the ideal steps for measure testing are: (1) understand the data elements that currently exist within EHR, (2) ensure that the data elements that make up the measure can be calculated consistently, (3) ensure that data can be retrieved electronically and that it is consistent with what is seen in EHR, and (4) ensure that fields needed make sense in workflow.

A.3.1.2. Primary Challenges

Data access and sharing. Most of the stakeholder groups explicitly articulated that data sharing is critical to allow for certain types of measures desired for the future (e.g., outcomes, longitudinal). Bidirectional data exchange is needed. Payers, in particular, indicated that they would use “any and all” data that they would have the ability to access. The best mechanisms for integrating clinical and administrative data need to be determined. Additionally, this data needs to be merged with other data (e.g., registries, PHRs, birth/death certificates). Multistakeholder participants also suggested that EHRs are not the only source of information and that different sources of data are needed. It was suggested that registries have become popular with many hospitals; perhaps because they get something of value back from registries in terms of caring for their population. The Prescription Drug Monitoring Program was one example mentioned where States have come together to share information. One stakeholder-specific focus group participant suggested that a national strategy around data sharing is needed to ensure that outputs are valuable to consumers. A multistakeholder participant recommended using MU as a lever. For example, since MU is not yet performance-based, a third track within MU could be offered to encourage development and testing of new innovations in data sharing, such as model policies or new constructs for data sharing. Results could be reported to the Centers for Medicare & Medicaid Services as part of meeting MU requirements and results shared with other stakeholders.

Most of the focus groups indicated that issues around data ownership, proprietary data, governance, privacy, and cost, in addition to technical constraints, remain barriers to data sharing and aggregation. Participants in the multistakeholder focus group suggested that issues around policy (i.e., privacy, governance, data ownership) are more challenging than technology issues. For example, the medical community is still in need of a strategy to address receipt of sensitive information. Participants suggested that there is a reluctance to share data with competitors. Moreover, some participants suggested that not all stakeholders understand the value of sharing data. One participant suggested that there needs to be some neutral third-party to assist with aggregation to ensure that multiple payers are more likely to participate. However, some groups who have tried to do this, such as the work done to set up one all-payer database, found that they cannot be leveraged for another, because each payer database is proprietary. Thus, all-payer databases are not using the same protocols.

Multistakeholder focus group participants also discussed the issue of a common patient identifier. It was agreed that a national patient index is unlikely to occur due to political challenges; however, a means by which to identify a patient across systems and care settings is needed before data can be accurately integrated.

https://www.bja.gov/ProgramDetails.aspx%3FProgram_ID%3D72

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It is also important to be able to identify a patient for safety reasons. Data sharing and patient identification is essential, particularly in local geographic areas. HIEs that have succeeded have a master patient index to cross organizational boundaries. Some entities have created private databases of patient identifiers that can be used to look at patient data across payer lines. Some organizations are using mobile telephone numbers as a patient identifier. Payers are also starting to create private databases to help with private networks and sharing.

Standardization. Standardization was frequently mentioned as another barrier to data sharing as well as health IT-enabled quality measurement in general. For example, hospital labs do not always use standardized coding formats. New standards are also needed for patient reported data. Stakeholders need to be consistently measuring things the same way (e.g., tobacco use or cigarette use) so that outputs are comparable. This may also require data to be codified in a standard way so that it may be measured. One participant recommended using HL7 (Health Level Seven) as a means by which to standardize. Another suggested that professional associations may help drive measurement development and consistency.

Standardization was discussed as both a data entry issue and a workflow issue; there need to be tools to ease data entry. Measure standardization and data element standardization are both needed. It was suggested that vendors do not have a real incentive to build standards into their products. Moreover, multistakeholder focus group participants suggested that more consistency is required within vendors’ own platforms. However, participants recognized that a standardized interface may be difficult to achieve when vendors are developing a competitive advantage for their products.

Focus group participants suggested that retooling paper measures into eMeasures requires the use of the Quality Data Model (QDM); however, EHRs do not align with how the QDM recommends collecting data. One participant recommended standardizing eSpecifications and code sets as well as promoting mapping software for commercial, proprietary codes sets to eSpecification code sets. Multistakeholder focus group par-ticipants suggested that the value sets need to be harmonized. From a measure development perspective, gaps in the value set still need to be identified. What are the most commonly used codes for ordered tests? One participant used the example of the standardized vocabulary around allergies; there are a host of allergies that cannot be reliable captured in exchanges that are described in MU. There is still much work that is needed in the data dictionary that is aligned to MU standards and categories of information.

Additionally, one multistakeholder focus group participant noted that payer requirements and regulation often drive data usage and how data is collected. Some of the participants suggested that the focus should be on the critical data elements; however, there does not seem to be agreement on what the critical elements are for measurement. Some participants suggested that critical data elements should be those that impact care and should be captured through the provision of care. Others believe that measures should be developed based on what data is available within the EHR. Still others feel that measures should be based on a framework such as the “Triple Aim” and then determination made as to from where data will come. Moreover, if measures are not hard coded into EHRs there would be more flexibility to change what is measured.

Participants discussed the value of using both structured and unstructured data and weighing the balance between more nuanced personalized data and standardization. It was generally agreed that unstructured data offered much promise to illuminate the complexity and multidimensional health and treatment factors, given

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the rich, clinical data that it contains. However, in order to be useful, this data would need to be standardized in some way. One participant recommended that speech recognition software might be used to ease data entry by filling in structured data fields. Alternatively, natural language processing (NLP) was suggested as a way to structure the unstructured data and use it for quality measurement. However, there were concerns that there is a lack of standards for NLP, which makes it difficult to translate into various systems. Moreover, multistake-holder focus group participants suggested that there needs to be a longer term vision for NLP and other such products for use in quality measurement. It was suggested that AHRQ might look into evidence around use of NLP and suggest a path forward via an Evidence-Based Practice Center report. Multistakeholder participants also indicated that usability can be a larger issue than whether data is structured or unstructured. For example, some structured data is not useable. In order to move forward, it needs to be determined which data is feasible and usable so that the useable and feasible data can be enhanced upon.

A.3.1.3. Stakeholder Engagement

Collaboration. All stakeholder groups agreed that collaboration is a critical component of effective quality measurement and that all stakeholder groups need to be involved throughout the measure development and implementation lifecycle. Vendors and measure developers agreed that collaboration between the two groups is particularly important; vendors and measure developers should work together throughout the process. However, this level of involvement can be costly as incentives are not currently aligned between the groups. Moreover, it was suggested that a common language between measure developers and vendors will improve collaboration. A resource that summarizes vendor capabilities for measure development would be very useful to developers. Transparency was frequently mentioned as a means for fostering greater understanding and acceptance, although it was noted that competition can inhibit transparency within a particular stakeholder group. Partnership agreements may also be needed between these two parties.

Practitioners at the point of care, from all care settings, were mentioned as a group important to engage throughout the measurement lifecycle. Given the lack of availability of providers for nonclinical activities, it was suggested that medical specialty societies may be a way to gain provider insight. However, some par-ticipants emphasized that direct communication with frontline providers would be best. One participant suggested that providers could be engaged in activities that they believe will reduce overall burden (i.e., collaboratives that work to align measures across programs). Such collaboratives would have a direct impact on future burden. Additionally, clinical setting personnel should be incentivized to use newly developed and validated data elements to facilitate quality measurement.

Focus group participants suggested that consumers and employers could be an important addition to the conversation of quality measurement enabled by health IT. Multistakeholder group participants agreed on the importance of including such groups, but cautioned that getting participation could be difficult given availability and other responsibilities. Moreover, the two groups may have differing priorities and should not be substitutes for each other. For example, consumers may be most concerned with access and coverage, while employers may be primarily focused on managing benefits, leaving little additional time to engage here.

It was suggested that AHRQ, or other party, could host a workshop to bring these stakeholders together. The agenda for this workshop could potentially build a foundation for collaboration between vendors and

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measure developers and would include discussion of definition of roles, common vocabulary, issues with data elements, measure testing, and challenges in development and implementation. Multistakeholder partici-pants suggested that collaboration is important, but to be sensitive to participants’ limited bandwidth for workshops or conferences. They suggested that having a Federal-level host would be preferable and that engaging consumers as soon as possible is important. Participants suggested that these types of activities need to be the right size and valuable to the invitees. It would help if the activities were linked to a national level program or initiative instead of focused solely on measures.

The participants suggested that a curriculum for consumers and purchasers would be useful to creating a lexicon for future engagement of these stakeholders. One of the participants mentioned the Robert Wood Johnson Foundation Consumer Purchaser Disclosure Project as one of the few organizations that were focused on this issue and could provide some insights in how to proceed to engage consumers in these types of collaborations. The NQF’s e-Measure Learning Collaborative was also recommended as a potential model for collaboration. Moreover, a collaboration would be beneficial to participants if suggestions on how to build infrastructure in the payer environment were included. Stakeholders often struggle with State-specific processes; collaboratives could provide an opportunity to engage in this area.

Consumer engagement. Participants across all stakeholder focus groups discussed the importance of engaging consumers and their families. It was suggested that patient engagement can lead to shared decisionmaking and can enhance successful provider and patient relationships. Additionally, collecting information prior to an appointment can facilitate the appointment and should also be linked to the EHR. Multistakeholder participants suggested that patients’ personal goals should be integrated with clinical goals in the care record. Moreover, shared decisionmaking needs to be incorporated into workflow. Some partici-pants also recommended that the government could provide support to ensure that the Consumer Assessment of Healthcare Providers and Systems (CAHPS®) surveys are implemented more widely.

It was suggested that there are two types of consumers: (1) those currently seeking care and (2) those not currently seeking care. Individuals with chronic conditions or who use technology for managing other aspects of their lives are more likely to use personal health records (PHR). However, despite the availability of PHRs, portals, and other technologies for engaging patients, issues remain around motivating patients to use these technologies. Even when incentivized, consumer adoption of such technologies remains low. To improve use of technologies, participants suggested that systems should not be cumbersome and should require minimal manual entry. Some participants indicated that while privacy issues may be a perceived barrier, lessons may be learned from other industries, such as banking, on how to reassure consumers.

Some participants suggested that education and outreach may be limited to making consumers not currently seeking care aware of what tools and services are available should they require care. This could be similar to knowing about available services in other industries, but only accessing them when needed (e.g., TripAdvisor for travel or OpenTable for dining). Overall, a better understanding of consumer behavior is needed. Additionally, consumers should be engaged in ways they already receive information on other topics (e.g. magazines, Web sites) or using familiar tools positioned in places where they naturally frequent (e.g., iPads in the doctor’s office, kiosks at grocery stores or pharmacies). Faith-based organizations and employers may also be channels for distributing information and engaging consumers.

http://healthcaredisclosure.org/

http://www.qualityforum.org/Topics/HIT/eMeasure_Learning_Collaborative/eMeasure_Learning_Collaborative.aspx

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Participants in the stakeholder-specific focus group and in the multistakeholder focus group suggested that there are two levels on which consumers seek quality information: 1) choosing a provider and 2) decision-making around options in treatment. Multistakeholder participants stated that it is important to find out from patients and consumers what type of information they would want for choosing a provider or decision-making around treatment options. Information around selecting a provider should be offered using some understandable framework or categories that are meaningful to consumers. Though provider selection occurs predominately by word of mouth (e.g., recommendations of friends, family), consumers will use online rating systems when they are easily understood and believed to be a reliable source. When it comes to decision-making around care options, consumers need to better understand how to use quality measures to facilitate collaboration with their providers on care decisions (see discussion on New Measures for further information on quality measures that matter to consumers). The multistakeholder participants suggested that consumer views on quality are different from those of clinicians. Quality for consumers needs to be defined through the eyes of the patient and presented in a way that is useful to them. Information needs to come from a trusted resource; consumers do not often trust the quality information from payers. Though health plan benefit designs or tiering has been used by payers to encourage use of high-quality providers or treatment options with the best proven outcomes, these have not always been shown to change consumer behavior because of these trust issues. An individual’s primary care physician or other familiar clinician tends to be the most relied on source of information.

Multistakeholder participants also suggested that more needs to be done on how to present information to patients. Much of the information available to patients is incomplete. Quality information cannot be designed for patients, unless designed with patients. Participants suggested that this is an important area of further research. Information is needed from consumers to determine how information is used and what information is important for decisionmaking. This is critical to providing information that is meaningful and useful to patients. Participants mentioned current work in this area being conducted by the Informed Medical Decisions Foundation and the Centers for Aging Services Technologies. Moreover, participants suggested that there is a need to look at post acute and long-term care settings. Multistakeholder participants suggested that there would be great value in looking specifically at consumer engagement in nursing homes and other post acute care settings (e.g., home health care, skilled nursing) and hospice. These settings would be valuable places to obtain input from patients and families and it would enable testing of new episodic measures.

http://informedmedicaldecisions.org/


http://www.leadingage.org/CAST.aspx

B-1



Appendix B. Methods and ApproachesThe Pathways to Quality project began with the development of an Environmental Snapshot released in the summer of 2012, which reviewed current literature to provide a brief overview of the historical and current state of quality measurement, described possibilities for the next generation of quality measurement enabled by health information technology (IT), and illustrated some of the challenges facing the advancement of quality measurement enabled by health IT. Simultaneously, research was conducted to identify projects and programs to be included in the Partial Catalog of Current Activities To Improve Quality Measurement Enabled by Health IT. This catalog was featured in the 2012 Environmental Snapshot and was subsequently updated for this report.

The final report—Health IT-Enabled Quality Measurement: Perspectives, Pathways, and Practical Guidance—represents a consolidation of the findings from stakeholder engagement activities as well as supporting research. Challenges identified in the Environmental Snapshot were used as the foundation for developing questions for a Request for Information and Public Comment (RFI). Using the information gathered from the RFI, a set of “deeper-dive” questions emerged for six stakeholder-specific focus groups. The RFI, with additional feedback from the stakeholder focus groups, also informed the questions for a final focus group—a heterogeneous (cross-stakeholder) group. Analyses from each of the stakeholder activities were used in the development of the final report. The Partial Catalog of Current Activities To Improve Quality Measurement Enabled by Health IT was continually updated throughout the project. Exhibit B-1 illustrates activities toward completion of this final report.

Exhibit B-1. Approach to Final Report Development

B.1. Environmental Snapshot

In preparation for stakeholder activities, an Environmental Snapshot was developed to identify what is known about the relationship between health IT and quality measurement. This snapshot was developed by reviewing published literature and publically available information related to this topic, including existing articles, published reports, work group testimony, and other publically available documents and Web sites released in the previous 4 years to identify current or recently completed initiatives. Literature from the previous 5 years was considered for more general background information on the current state of performance measurement. A four-step approach was used to conduct the research for the Environmental Snapshot: establish research criteria, identify key research questions, perform literature review, and synthesize key findings. A more detailed explanation of methodology is available within the Environmental Snapshot.


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B.2. Stakeholder Engagement

Stakeholder engagement activities were used to build on the findings from the Environmental Snapshot to capture a vision for the ideal future state and recommendations for how to achieve that vision. A broad group of 127 stakeholders were engaged through a variety of mechanisms (e.g., RFI, focus groups). The RFI was available to the general public for response. Stakeholders for the focus groups were selected based on varied backgrounds and perspectives; different organizational sizes, structures, and representing different care settings; and based on having either historically represented quality measurement or health IT in the field or whose emerging methodologies or perspectives were brought to the attention of the project team. As resources precluded use of even a larger number of stakeholders, stakeholders were classified based on the primary stakeholder group that they represented, including payers, providers, consumers, measure developers and endorsers, and health IT vendors and technology specialists.

B.2.1. Request for Information and Public Comment

Following the Environmental Snapshot, areas where additional input was needed were identified. Based on these areas, an RFI was developed that summarized the Pathways to Quality through Health IT project and key aspects of the Environmental Snapshot and invited the public to provide input to 15 questions on electronic quality measurement and its associated challenges.

Questions in the RFI were structured to be broad in nature and open ended. There were no requirements for respondents to answer all of the questions. Additionally, no specific individual or organization was required to respond to the RFI. The RFI instructions included a disclaimer indicating that the RFI was issued solely for information and planning purposes. There were no questions asking for input on potential areas of legislative or regulatory requirements.

The RFI was submitted for publication in the Federal Register on June 20, 2012, with an initial 30-day response period. Following requests from the public, an additional 30-day comment period was added. Respondents were instructed to submit to an AHRQ email account, which was set up for the purposes of capturing RFI responses.

All RFI responses were logged upon receipt. Initial analyses were conducted for each question (i.e., examined all responses to a given question). Each question was examined to determine the distribution of stakeholders that responded to that particular question (i.e., which stakeholder groups responded to each question). In addition to the by-question analyses, each RFI response was also examined in its entirety to identify broader themes which allowed for analysis across questions and respondents. Responses that could not be associated with a particular question were categorized as “other.” All of these responses were reviewed and key concepts and themes were identified from these responses and included in the across question analysis. A summary of these insights can be found in Appendix A.

B-3



B.2.2. Stakeholder-Specific Focus Groups

Based on the RFI responses, key themes were identified to be further explored through a series of focus groups. Five nongovernment, stakeholder-specific, 2.5 hour focus groups were held in January 2013 via Webinar. Each focus group consisted of five to nine nongovernment participants. The focus groups were organized by stakeholder group (e.g., vendors, providers, payers, measure developers, and consumers). Potential participants were identified as a result of their RFI response or through various channels, including recommendations from Booz Allen Hamilton subject matter experts, recommendations from AHRQ staff, and recommendations of other industry experts with whom Booz Allen Hamilton or AHRQ has relation-ships. Participants were sought that represented a variety of organization types and sizes, geographic regions, and experiences. Additionally, a combination of established voices and lesser known voices were invited to participate in the focus groups. All participants signed confidentiality and nondisclosure agreements to ensure that participants were able to freely contribute.

Prior to holding the nongovernment, stakeholder-specific focus groups, the initial findings from the RFI and the objectives for the forthcoming focus groups were presented to government stakeholders. Participants were invited to provide feedback on areas needing further information or activities of which they wanted the team to be aware. Government participants were also invited to recommend individuals for the stakeholder-specific focus groups. Two separate government meetings were held due to scheduling constraints.

In preparation for each focus group, the RFI responses were examined on a question-by-question basis. The content to be discussed in each stakeholder-specific focus group was informed in part by RFI responses from similar stakeholders or responses from other stakeholders who mentioned an issue particularly relevant to stakeholders in another planned focus group. Review of the RFI responses documented key observations and areas where additional knowledge was needed. These areas for additional knowledge were then used as the basis for the questions asked in the focus groups. Questions were stakeholder-specific to ensure that the unique perspective of a particular stakeholder group could be pursued in its associated focus group. The same questions were not asked across different stakeholder groups; however, common themes overlapped the different focus groups. A focus group guide with a script was developed for each focus group along with a PowerPoint presentation that contained the planned questions. Questions were shared with participants prior to their focus group. Participants were also invited to optional 1 hour, pre-focus group information sessions in which findings from the RFI were presented via Webinar. Each stakeholder-specific focus group had its own set of objectives and talking points. Broadly, the objectives were as follows:

�� Identify actionable, incremental advancements needed to move toward the next generation of health IT-enabled quality measurement.

�� Learn the primary challenges participants of each stakeholder group experience today and foresee in the future in accelerating the use of health IT for quality measurement.

�� Elaborate on successful strategies for engaging other stakeholders.

B-4



While the focus groups were not recorded, notetakers captured the key themes and responses to each of the focus group questions. Each set of themes captured in the focus group notes were organized by topic. This facilitated analysis of the key themes from the focus groups across all stakeholder groups. A summary of these insights can be found in Appendix A.

B.2.3. Multistakeholder Focus Group

Potential participants for a final focus group—which included voices from multiple stakeholder groups (e.g., payers, providers, vendors, measure developers/endorsers, consumer advocates)—were identified through various channels, including RFI respondents (e.g., recommendations of specific individuals to include or types of individuals to include), recommendations of stakeholder-specific focus group participants, recom-mendations from Booz Allen Hamilton subject matter experts, recommendations from AHRQ staff, and recommendations from other industry experts with whom Booz Allen Hamilton or AHRQ has relationships. Participants were sought that represented a variety of organization types and sizes, geographic regions, and experiences. All participants signed confidentiality and nondisclosure agreements to ensure that participants were able to freely contribute.

In preparation for the multistakeholder focus group, actions and activities recommended by stakehold-er-specific focus group participants to address some of the key challenges identified by RFI respondents were re-examined. Recommendations where further information or discussion was needed were selected as topics for the multistakeholder focus group. Due to time constraints (session was 3.5 hours), topics were prioritized (e.g., topics most discussed by RFI respondents and stakeholder-specific focus group participants, topics where further conversation could most benefit all stakeholders in making recommendations actionable). Areas chosen for discussion included:

�� Data Elements and Data Capture

�� Data Access, Sharing, Aggregation, and Integration

�� Patient/Consumer Engagement

�� New Measures

�� Tools to Process Unstructured Data

�� Third Party-Facilitated Collaboration Sessions

A focus group guide with a script was developed along with a PowerPoint® presentation that contained the planned questions. Questions were shared with participants prior to the focus group. The objectives of the multistakeholder focus group were as follows:

�� Elaborate on identified actionable, pro-active, incremental advancements needed to move toward the next generation of health IT-enabled quality measurement

B-5



�� Identify the chronological order of those incremental advancements in the near (1–2 years) and mid-term (3–5 years)

�� Discuss the requisite stakeholders—their particular challenges, needs, perspectives, and roles

�� Identify risks or challenges toward accomplishing incremental advancements and possible mitigations.

While the focus groups were not recorded, notetakers captured the key themes and responses to each of the focus group questions. Each set of themes captured in the focus group notes were organized by topic. This facilitated analysis of the key themes from this final focus group. A summary of these insights can be found in Appendix A.

B.3. Additional Research, Synthesis, and Writing of the Final Report

Key themes were identified during the RFI and stakeholder-specific focus groups. A review of the literature was then conducted to provide additional material and context for these findings from the stakeholder engagement activities (Exhibit B-2). The multistakeholder focus group was held last and focused on a set of six key themes. In addition to these themes (measure development, implementation, and testing; data elements and data capture; data access, sharing, aggregation, and integration; patient engagement; and collaboration) four perspectives emerged from the stakeholder engagement activities. These perspectives were described in relation to the theme topic areas. Recommended activities across all stakeholder engagement activities were organized into the Practical Guidance: Table of Suggested Steps Toward Enhanced Health IT-Enabled Quality Measurement to illustrate the full breadth and depth of recommendations offered by RFI respondents and focus group participants.

Exhibit B-2. Table of Representative Literature Review Search Terms

Representative Literature Review Search Terms

Health IT, EHR Using electronic health records to collect performance measures/quality measures

Data capture, health IT/EHRw

Performance measure/quality measure using EHR

EHR, quality measure(s)/performance measure(s)

Patient engagement, health IT/EHR

Performance measure/quality measure using Health IT

Health IT, quality measurement/performance measurement

Data sharing, health IT/EHR

Automation, quality measurement/performance measurement

Data requirements, EHR Natural language processing/unstructured data tools, health IT/EHR

Data aggregation, health IT Interoperability, health IT Health information exchange/HIE

B-6



B.4. Update of Partial Catalog of Current Activities To Improve Quality Measurement Enabled by Health IT

The Partial Catalog of Current Activities To Improve Quality Measurement Enabled by Health IT, originally presented in the Environmental Snapshot, was updated based on information provided through the RFI, during the focus groups, or suggested by other means subsequent to the writing of the Environmental Snapshot. Projects and programs where further information could be found in publically available resources were included. The updated Partial Catalog of Current Activities To Improve Quality Measurement Enabled by Health IT is located in Appendix C of this report, Health IT-Enabled Quality Measurement: Perspectives, Pathways, and Practical Guidance. Appendix C includes projects and programs identified during the creation of the Environmental Snapshot as well as those identified subsequently. Though great effort was made to include as many projects or programs as possible, the Catalog should not be considered comprehensive or exhaustive. To assist readers in finding programs relevant to their own work, each project or program has been tagged with a few key words that illustrate attributes of the program. This tagging, however, should also not be considered all inclusive; tags are based on general program descriptions and may be limited by the ability to obtain information on the program from public sources.

C-1

Hea

lth IT

-Ena

bled

Qua

lity

Mea

sure

men

t:

Pers

pect

ives,

Pa

thw

ays

,

an

d P

ract

ica

l G

uid

an

ce

Ap

pen

dix

C.

Pa

rtia

l C

ata

log

of

Cu

rren

t A

ctiv

itie

s To

Im

pro

ve

Qu

ality

Mea

sure

men

t En

ab

led

by

Hea

lth

IT

The

follo

win

g ta

ble

(Exh

ibit

C-1

) des

crib

es a

subs

et o

f pro

gram

s and

initi

ativ

es a

t the

Fed

eral

and

Sta

te le

vels

as w

ell a

s effo

rts b

y pr

ivat

e se

ctor

stak

e-ho

lder

s, w

hich

are

inte

nded

to im

prov

e qu

ality

mea

sure

men

t thr

ough

hea

lth in

form

atio

n te

chno

logy

(IT

). Th

is ca

talo

g is

not e

xhau

stiv

e, b

ut ra

ther

ill

ustr

ates

the

brea

dth

and

dept

h of

the

wor

k be

ing

cond

ucte

d in

this

area

by

a va

riety

of s

take

hold

ers.

The

list i

s sor

ted

into

thre

e ca

tego

ries,

Fede

ral p

rogr

ams,

Stat

e/re

gion

al, a

nd p

rivat

e, a

nd th

en so

rted

alp

habe

tical

ly b

y na

me

of th

e or

gani

zatio

n.

Illus

trat

ive

tags

hav

e be

en a

dded

to in

crea

se th

e ea

se o

f find

ing

prog

ram

s or i

nitia

tives

that

may

be

of m

ost i

nter

est.

Thou

gh m

any

prog

ram

s and

in

itiat

ives

cov

ered

a g

reat

bre

adth

of t

opic

s, ea

ch m

ay n

ot b

e ta

gged

with

all

corr

espo

ndin

g to

pics

, but

the

ones

that

seem

ed to

mak

e th

e pr

ogra

m

or in

itiat

ive

mos

t dist

inct

from

oth

ers.

Tags

are

bro

ken

into

thre

e ca

tego

ries:

Func

tiona

l (i.e

., th

e pr

imar

y fu

nctio

n[s]

of th

e pr

ogra

m o

r ini

tiativ

e),

Topi

cal (

i.e.,

any

key

topi

cs a

ddre

ssed

by

the

prog

ram

or i

nitia

tive)

, and

Spe

cial

, whi

ch d

enot

es u

niqu

enes

s to

care

sett

ings

, spe

cific

dise

ases

, or c

erta

in

popu

latio

ns. A

com

plet

e lis

t of t

ags u

sed

can

be fo

und

at th

e en

d of

this

appe

ndix

.

Exhi

bit C

-1. P

artia

l Cat

alog

of C

urre

nt A

ctiv

ities

To Im

prov

e Qua

lity M

easu

rem

ent E

nabl

ed b

y Hea

lth IT

*

Nam

eO

rgan

izat

ion

Des

crip

tion

Func

tion

al T

agTo

pica

l Tag

Spec

ial T

ag

Fede

ral P

rogr

ams (

alph

abet

ized

by

agen

cy)

Am

bula

tory

Saf

ety

and

Qua

lity

Prog

ram

19H

HS/

AH

RQD

evel

ops s

afet

y an

d qu

ality

mea

sure

s in

ambu

lato

ry c

are

sett

ings

, aut

omat

es

qual

ity m

easu

rem

ent,

dem

onst

rate

s the

abi

lity

of e

lect

roni

c da

ta sy

stem

s (su

ch a

s EH

Rs o

r cla

ims d

ata

mer

ged

with

EH

R d

ata)

to e

xpan

d po

tent

ial

safe

ty a

nd q

ualit

y m

easu

res,

and

exhi

bits

impr

oved

abi

lity

to e

xpor

t dat

a fo

r re

port

ing

perf

orm

ance

on

mea

sure

s and

impr

ovem

ent.

Qua

lity

Impr

ovem

ent

Patie

nt S

afet

y;

EHR

Am

bula

tory

C

are

Atla

s of I

nteg

rate

d Be

havi

oral

Hea

lth C

are

Qua

lity

Mea

sure

s20

HH

S/A

HRQ

Supp

orts

the

field

of i

nteg

rate

d be

havi

oral

hea

lth c

are

mea

sure

men

t by

pres

entin

g a

fram

ewor

k fo

r und

erst

andi

ng m

easu

rem

ent o

f int

egra

ted

care

, pr

ovid

ing

a lis

t of e

xist

ing

mea

sure

s rel

evan

t to

inte

grat

ed b

ehav

iora

l hea

lth

care

, and

org

aniz

ing

the

mea

sure

s by

the

fram

ewor

k an

d by

use

r goa

ls to

fa

cilit

ate

sele

ctio

n of

mea

sure

s.

Qua

lity

Impr

ovem

ent

Car

e C

oord

inat

ion;

St

anda

rds

Beha

vior

al

Hea

lth

* To

avo

id re

dund

ancy

, ref

eren

ces f

or th

is A

ppen

dix

are

incl

uded

in th

e R

efer

ence

s for

the

mai

n bo

dy o

f thi

s rep

ort.

http://www.ahrq.gov/qual/ambsafety/ambsafety.htm

http://www.ahrq.gov/qual/ambsafety/ambsafety.htm

http://integrationacademy.ahrq.gov/atlas



C-2

Hea

lth IT

-Ena

bled

Qua

lity

Mea

sure

men

t:

Pers

pect

ives,

Pa

thw

ays

,

an

d P

ract

ica

l G

uid

an

ce

Nam

eO

rgan

izat

ion

Des

crip

tion

Func

tion

al T

agTo

pica

l Tag

Spec

ial T

ag

Con

sum

er A

sses

smen

t of

Hea

lthca

re

Prov

ider

s and

Sys

tem

s (C

AH

PS®)21

HH

S/A

HRQ

Supp

orts

and

pro

mot

es th

e as

sess

men

t of c

onsu

mer

s' ex

perie

nces

with

hea

lth

care

thro

ugh

the

deve

lopm

ent o

f sta

ndar

dize

d pa

tient

que

stio

nnai

res t

hat c

an

be u

sed

to c

ompa

re re

sults

acr

oss s

pons

ors o

ver t

ime

and

gene

ratio

n of

tool

s an

d re

sour

ces t

hat c

an b

e us

ed b

y sp

onso

rs to

pro

duce

und

erst

anda

ble

and

usab

le co

mpa

rativ

e in

form

atio

n fo

r bot

h co

nsum

ers a

nd h

ealth

car

e pr

ovid

ers.

Con

sum

er

Enga

gem

ent

Patie

nts/

Con

sum

er

Enab

ling

Qua

lity

Mea

sure

men

t (EQ

M)

Thro

ugh

Hea

lth IT

R

FA22

HH

S/A

HRQ

Awar

ded

gran

ts to

dev

elop

safe

ty a

nd q

ualit

y m

easu

res i

n am

bula

tory

car

e se

ttin

gs, a

utom

ate

qual

ity m

easu

rem

ent,

dem

onst

rate

the

abili

ty o

f ele

ctro

nic

data

syst

ems,

expa

nd p

oten

tial s

afet

y an

d qu

ality

mea

sure

s, an

d de

mon

stra

te

impr

oved

abi

lity

to e

xpor

t dat

a fo

r rep

ortin

g pe

rfor

man

ce o

n m

easu

res a

nd

impr

ovem

ent.

Of t

he 1

7 to

tal g

rant

s aw

arde

d th

roug

h th

is R

FA in

200

7, tw

o gr

ants

end

ed in

200

9 an

d th

e re

mai

ning

15

proj

ects

wer

e aw

arde

d no

-cos

t ex

tens

ions

. Of t

hese

, 13

ende

d in

201

0 an

d tw

o ar

e sch

edul

ed to

clo

se in

201

1.

Gra

nt

Prog

ram

; Q

ualit

y Im

prov

emen

t

EHR

; Pat

ient

Sa

fety

Am

bula

tory

C

are

Lear

ning

Net

wor

ks23

HH

S/A

HRQ

Diss

emin

ates

and

impl

emen

ts p

rodu

cts,

tool

s, an

d re

sear

ch to

spec

ific

targ

et g

roup

s as p

art o

f AH

RQ’s

Kno

wle

dge

Tran

sfer (

KT

)/Im

plem

enta

tion

prog

ram

.

Trai

ning

&

Tech

nica

l A

ssist

ance

Info

rmat

ion

Exch

ange

MO

NA

HRQ

24H

HS/

AH

RQA

desk

top

soft

war

e to

ol th

at e

nabl

es o

rgan

izat

ions

—su

ch a

s sta

te a

nd

loca

l dat

a or

gani

zatio

ns, r

egio

nal r

epor

ting

colla

bora

tives

, hos

pita

ls an

d ho

spita

l sys

tem

s, an

d he

alth

pla

ns—

to q

uick

ly a

nd e

asily

gen

erat

e a

heal

th

care

repo

rtin

g W

ebsit

e. M

ON

AH

RQ® a

naly

zes,

sum

mar

izes

, and

pre

sent

s in

form

atio

n in

a fo

rmat

read

y fo

r use

by

cons

umer

s and

oth

er d

ecisi

onm

aker

s.

Tool

s/Pr

oduc

ts

Nat

iona

l Gui

delin

e C

lear

ingh

ouse

(NG

C)25

HH

S/A

HRQ

A pu

blic

reso

urce

for e

vide

nce-

base

d cl

inic

al p

ract

ice

guid

elin

es.

Mea

sure

s C

lear

ingh

ouse

Car

e C

oord

inat

ion

Nat

iona

l Qua

lity

Mea

sure

s C

lear

ingh

ouse

™ (N

QM

C)26

HH

S/A

HRQ

A da

taba

se a

nd W

eb si

te th

at h

olds

info

rmat

ion

on sp

ecifi

c ev

iden

ce-b

ased

he

alth

car

e qu

ality

mea

sure

s and

mea

sure

sets

. NQ

MC

is sp

onso

red

by

AH

RQ to

pro

mot

e w

ides

prea

d ac

cess

to q

ualit

y m

easu

res b

y th

e he

alth

car

e co

mm

unity

and

oth

er in

tere

sted

indi

vidu

als.

Mea

sure

s C

lear

ingh

ouse

Dat

a R

epos

itory

Nat

iona

l Qua

lity

Stra

tegy

13H

HS/

AH

RQPr

omot

es q

ualit

y he

alth

car

e fo

r pat

ient

s, fa

mili

es, a

nd c

omm

uniti

es a

nd

guid

es th

e ac

tions

of a

ll th

ose

who

del

iver

and

pay

for c

are.

Inco

rpor

ates

th

e ev

iden

ce-b

ased

resu

lts o

f res

earc

h an

d sc

ient

ific

adva

nces

in c

linic

al

med

icin

e, p

ublic

hea

lth, a

nd h

ealth

car

e de

liver

y.

Adv

isory

Bo

dy; Q

ualit

y Im

prov

emen

t; Pu

blic

Hea

lth

Nat

iona

l Res

ourc

e C

ente

r for

Hea

lth IT

(N

RC)27

HH

S/A

HRQ

A pu

blic

reso

urce

for s

harin

g re

sear

ch fi

ndin

gs, b

est p

ract

ices

, les

sons

le

arne

d, a

nd fu

ndin

g op

port

uniti

es w

ith h

ealth

IT re

sear

cher

s, im

plem

ente

rs,

and

polic

ymak

ers.

The

NRC

has

mor

e th

an 1

0,00

0 do

cum

ents

, pr

esen

tatio

ns, a

rtic

les,

and

tool

s ava

ilabl

e.

Trai

ning

&

Tech

nica

l A

ssist

ance

EHR

Ado

ptio

n/

Impl

emen

tatio

n

http://cahps.ahrq.gov/




http://healthit.ahrq.gov/portal/server.pt/community/ahrq-funded_projects/654/health_it_portfolio_annual_report/16758




http://www.ahrq.gov/policymakers/measurement/quality-by-state/ktnetworks.html

http://www.monahrq.ahrq.gov/

http://guideline.gov/

http://guideline.gov/

http://www.qualitymeasures.ahrq.gov/about/index.aspx




http://www.healtbhcare.gov/center/reports/quality03212011a.html

http://www.healtbhcare.gov/center/reports/quality03212011a.html

http://healthit.ahrq.gov/portal/server.pt/community/about/562/national_resource_center_for_health_it/5531



C-3

Hea

lth IT

-Ena

bled

Qua

lity

Mea

sure

men

t:

Pers

pect

ives,

Pa

thw

ays

,

an

d P

ract

ica

l G

uid

an

ce

Nam

eO

rgan

izat

ion

Des

crip

tion

Func

tion

al T

agTo

pica

l Tag

Spec

ial T

ag

Sele

ctin

g Q

ualit

y an

d R

esou

rce

Use

Mea

sure

s: A

Dec

ision

Gui

de fo

r C

omm

unity

Qua

lity

Col

labo

rativ

e28

HH

S/A

HRQ

Info

rms r

eade

rs a

bout

the

mos

t crit

ical

issu

es to

con

sider

whe

n se

lect

ing

and

adop

ting

perf

orm

ance

mea

sure

s. C

olla

bora

tive;

Tr

aini

ng &

Te

chni

cal

Ass

istan

ce

Dec

ision

mak

ing

Tran

sform

ing

Hea

lthca

re Q

ualit

y Th

roug

h H

ealth

IT

(TH

QIT

) Pro

gram

29

HH

S/A

HRQ

Awar

ded

gran

ts fr

om 2

004-

2009

to 1

18 g

rant

ees t

hat p

lann

ed, i

mpl

emen

ted,

an

d st

udie

d he

alth

IT im

plem

enta

tions

acr

oss a

wid

e ra

nge

of c

are

sett

ings

, co

mm

uniti

es, a

nd h

ealth

IT sy

stem

s. [L

ink

to th

e le

ft off

ers i

nfor

mat

ion

abou

t man

y of

the

118

prog

ram

s.]

Gra

nt

Prog

ram

EHR

Ado

ptio

n/

Impl

emen

tatio

n;

Inno

vatio

n;

Info

rmat

ion

Exch

ange

Rur

al H

ealth

; Po

st A

cute

C

are;

Acu

te

Car

e H

ospi

tal;

Com

mun

ity

Hea

lth C

ente

rs

Port

al C

ompa

res

APC

D, A

SC X

12, a

nd

NC

PDP

Stan

dard

s D

ata30

HH

S/A

HRQ

/C

DC

A m

etad

ata

regi

stry

of h

ealth

car

e-re

late

d da

ta e

lem

ents

and

thei

r sou

rces

. In

clud

es a

cor

e se

t of d

ata

elem

ents

from

eac

h st

ate’s

All-

Paye

r Cla

ims

Dat

abas

e (A

PCD

) and

the

corr

espo

ndin

g da

ta e

lem

ents

in (1

) the

Acc

redi

ted

Stan

dard

s Com

mitt

ee (A

SC) X

12 5

010

stan

dard

, (2)

the

ASC

X12

Pos

t-A

djud

icat

ed D

ata

Rep

ortin

g G

uide

s (in

dev

elop

men

t), a

nd (3

) the

Nat

iona

l C

ounc

il fo

r Pre

scrip

tion

Dru

g Pr

ogra

ms (

NC

PDP)

Uni

form

Hea

lthca

re

Paye

r Sta

ndar

d Im

plem

enta

tion

Gui

de V

ersio

n 1.

0.

Qua

lity

Impr

ovem

ent

Port

al; U

sing

Cla

ims D

ata;

St

anda

rds

Hea

lth IT

Por

tfolio

27H

HS/

AH

RQ/

NRC

Dev

elop

s and

diss

emin

ates

evi

denc

e an

d ev

iden

ce b

ased

tool

s abo

ut th

e im

pact

of h

ealth

IT o

n he

alth

car

e qu

ality

Q

ualit

y Im

prov

emen

t; EH

R

CD

C’s

Info

rmat

ion

Tech

nolo

gy S

trat

egic

Pl

an (C

ITSP

)31

HH

S/C

DC

Gui

des C

DC

’s pu

blic

hea

lth p

riorit

ies t

o m

axim

ize

the

valu

e of

hea

lth IT

to

CD

C p

rogr

ams.

Prom

otes

impr

oved

hea

lth o

utco

mes

thro

ugh

the

prov

ision

of

hig

h-qu

ality

, tim

ely,

rele

vant

info

rmat

ion

to C

DC

, its

par

tner

s, an

d its

cus

tom

ers.

Adv

isory

Bo

dyEH

R A

dopt

ion/

Im

plem

enta

tion

Nat

iona

l Com

mitt

ee

on V

ital a

nd H

ealth

St

atist

ics32

HH

S/C

DC

Serv

es a

s an

advi

sory

bod

y to

HH

S on

hea

lth d

ata,

stat

istic

s, an

d na

tiona

l he

alth

info

rmat

ion

polic

y. It

fulfi

lls im

port

ant r

evie

w a

nd a

dviso

ry fu

nctio

ns

rela

tive

to h

ealth

dat

a an

d st

atist

ical

pro

blem

s of n

atio

nal a

nd in

tern

atio

nal

inte

rest

, stim

ulat

es o

r con

duct

s stu

dies

of s

uch

prob

lem

s, an

d pr

opos

es

impr

ovem

ents

of t

he N

atio

n’s h

ealth

stat

istic

s and

info

rmat

ion

syst

ems.

Adv

isory

Bo

dy; Q

ualit

y Im

prov

emen

t

EHR

Nat

iona

l Hea

lthca

re

Safe

ty N

etw

ork

(NH

SN)33

HH

S/C

DC

A pu

blic

hea

lth su

rvei

llanc

e sy

stem

that

pro

vide

s dat

a re

port

ed fr

om h

ealth

ca

re fa

cilit

ies t

o th

e C

DC

to im

prov

e pa

tient

safe

ty a

t loc

al a

nd n

atio

nal

leve

ls. H

ealth

car

e fa

cilit

ies r

epor

t inf

ectio

ns d

irect

ly in

to N

HSN

eith

er

volu

ntar

ily o

r bec

ause

of t

heir

stat

e’s re

quire

men

ts. Th

e C

DC

pre

pare

s H

ospi

tal A

cqui

red

Infe

ctio

n Su

mm

ary

Dat

a R

epor

ts th

at in

clud

e bo

th

natio

nal a

nd S

tate

-spe

cific

dat

a.

Publ

ic H

ealth

Hos

pita

l Acq

uire

d In

fect

ions

; Pat

ient

Sa

fety

Acu

te C

are

Hos

pita

l; A

mbu

lato

ry

Car

e;

Com

mun

ity

Hea

lth C

ente

rs

http://www.ahrq.gov/qual/perfmeasguide/perfmeasintro.htm





http://healthit.ahrq.gov/ahrq-funded-projects/transforming-healthcare-quality-through-health-it




hhttp://ushik.org/index_sdo.jsp%3Fsystem%3Dsdo%26enableAsynchronousLoading%3Dtrue





http://www.cdc.gov/od/ocio/strat_plan.htm



http://www.ncvhs.hhs.gov/wg-qual.htm



http://www.cdc.gov/nhsn/



C-4

Hea

lth IT

-Ena

bled

Qua

lity

Mea

sure

men

t:

Pers

pect

ives,

Pa

thw

ays

,

an

d P

ract

ica

l G

uid

an

ce

Nam

eO

rgan

izat

ion

Des

crip

tion

Func

tion

al T

agTo

pica

l Tag

Spec

ial T

ag

Publ

ic H

ealth

In

form

atio

n N

etw

ork

(PH

IN)34

HH

S/C

DC

A na

tiona

l ini

tiativ

e to

incr

ease

the

capa

city

of p

ublic

hea

lth a

genc

ies t

o ex

chan

ge d

ata

and

info

rmat

ion

elec

tron

ical

ly a

cros

s org

aniz

atio

ns a

nd

juris

dict

ions

(e.g

., cl

inic

al c

are

to p

ublic

hea

lth, p

ublic

hea

lth to

pub

lic h

ealth

an

d pu

blic

hea

lth to

oth

er F

eder

al a

genc

ies).

To

do so

, PH

IN p

rom

otes

the

use

of st

anda

rds a

nd d

efine

s fun

ctio

nal a

nd te

chni

cal r

equi

rem

ents

for p

ublic

he

alth

info

rmat

ion

exch

ange

.

Publ

ic H

ealth

EHR

Ado

ptio

n/

Impl

emen

tatio

n;

Stan

dard

s; In

nova

tion;

In

form

atio

n Ex

chan

ge

Cen

ter f

or M

edic

are

and

Med

icai

d In

nova

tion

(CM

MI)

35

HH

S/C

MS

Foste

rs h

ealth

car

e tra

nsfo

rmat

ion

by fi

ndin

g ne

w w

ays t

o pa

y fo

r and

del

iver

ca

re th

at im

prov

e car

e and

hea

lth w

hile

low

erin

g co

sts.

The C

ente

r ide

ntifi

es,

deve

lops

, sup

port

s, an

d ev

alua

tes i

nnov

ativ

e mod

els o

f pay

men

t and

car

e ser

vice

de

liver

y fo

r Med

icar

e, M

edic

aid,

and

the C

hild

ren'

s Hea

lth In

sura

nce P

rogr

am

(CH

IP) b

enefi

ciar

ies u

sing

an o

pen,

tran

spar

ent,

and

com

petit

ive p

roce

ss.

Res

earc

h;

Paye

rIn

nova

tion;

Car

e Tr

ansfo

rmat

ion;

Pa

ymen

t/

Rei

mbu

rsem

ent

Med

icar

e;

Med

icai

d

Com

preh

ensiv

e Pr

imar

y C

are

Initi

ativ

e36H

HS/

CM

SA

mul

tipay

er in

itiat

ive

fost

erin

g co

llabo

ratio

n be

twee

n pu

blic

and

priv

ate

heal

th c

are

paye

rs to

stre

ngth

en p

rimar

y ca

re. M

edic

are

wor

ks w

ith

com

mer

cial

and

Sta

te h

ealth

insu

ranc

e pl

ans a

nd o

ffers

bon

us p

aym

ents

and

ot

her r

esou

rces

to p

rimar

y ca

re d

octo

rs w

ho b

ette

r coo

rdin

ate

care

for t

heir

Med

icar

e pa

tient

s.

Col

labo

rativ

eC

are

Coo

rdin

atio

n;

Info

rmat

ion

Exch

ange

Prim

ary

Car

e,

Med

icar

e

Elec

tron

ic H

ealth

R

ecor

d (E

HR

) Inc

entiv

e 20

13 R

epor

ting

Pilo

t Pr

ogra

m37

HH

S/C

MS

A vo

lunt

ary

elec

tron

ic re

port

ing

optio

n to

satis

fy th

e C

linic

al Q

ualit

y M

easu

res (

CQ

M) o

bjec

tive

for t

he E

HR

Ince

ntiv

e Pr

ogra

m. P

artic

ipat

ion

in

the

EHR

201

2 R

epor

ting

Pilo

t is h

ighl

y en

cour

aged

and

allo

ws h

ospi

tals

an

oppo

rtun

ity to

pio

neer

effo

rts f

or su

bmitt

ing

CQ

Ms e

lect

roni

cally

. Elig

ible

H

ospi

tals

and

criti

cal a

cces

s hos

pita

ls (C

AH

s) at

test

, usin

g th

e at

test

atio

n to

ol p

rovi

ded

by C

MS

to re

port

CQ

Ms,

subm

it Q

ualit

y R

epor

ting

Doc

umen

t Arc

hite

ctur

e (Q

RD

A) C

ateg

ory

I dat

a du

ring

the

subm

issio

n pe

riod

for e

piso

des o

f car

e oc

curr

ing

in F

eder

al F

iscal

Yea

r 201

2, o

r bot

h at

test

and

subm

it C

QM

s ele

ctro

nica

lly th

roug

h th

e pi

lot.

Ince

ntiv

e Pr

ogra

m;

Qua

lity

Impr

ovem

ent;

Publ

ic

Rep

ortin

g

Mea

ning

ful U

se;

EHR

Acu

te C

are

Hos

pita

l; M

edic

are

The

Hos

pita

l En

gage

men

t Net

wor

ks38

HH

S/C

MS

Thro

ugh

CM

S’ P

artn

ersh

ip fo

r Pat

ient

s, 26

Sta

te, r

egio

nal,

natio

nal a

nd

hosp

ital s

yste

m o

rgan

izat

ions

serv

e as

Hos

pita

l Eng

agem

ent N

etw

orks

to

help

iden

tify

succ

essf

ul so

lutio

ns to

redu

ce h

ospi

tal-a

cqui

red

cond

ition

s and

di

ssem

inat

e th

ese

best

pra

ctic

es to

oth

er h

ospi

tals

and

heal

th c

are

prov

ider

s.

Qua

lity

Impr

ovem

ent;

Col

labo

rativ

e;

Trai

ning

&

Tech

nica

l A

ssist

ance

Hos

pita

l Acq

uire

d In

fect

ions

Acu

te C

are

Hos

pita

l; M

edic

are

Hos

pita

l Inp

atie

nt

Qua

lity

Rep

ortin

g Pr

ogra

m39

HH

S/C

MS

Prov

ides

hos

pita

ls a

finan

cial

ince

ntiv

e to

repo

rt th

e qu

ality

of t

heir

serv

ices

an

d pr

ovid

es C

MS

with

dat

a to

hel

p co

nsum

ers m

ake

mor

e in

form

ed

deci

sions

abo

ut th

eir h

ealth

car

e.

Publ

ic

Rep

ortin

g;

Qua

lity

Impr

ovem

ent

Dec

ision

mak

ing;

Pa

tient

s/C

onsu

mer

s

Acu

te C

are

Hos

pita

l; M

edic

are;

M

edic

are

http://www.cdc.gov/phin/



http://www.innovations.cms.gov/



http://innovation.cms.gov/initiatives/Comprehensive-Primary-Care-Initiative/index.html

http://innovation.cms.gov/initiatives/Comprehensive-Primary-Care-Initiative/index.html

http://www.qualitynet.org/dcs/ContentServer%3Fc%3DPage%26pagename%3DQnetPublic%252FPage%252FQnetTier2%26cid%3D1228771190900




http://partnershipforpatients.cms.gov/about-the-partnership/hospital-engagement-networks/thehospitalengagementnetworks.html

http://partnershipforpatients.cms.gov/about-the-partnership/hospital-engagement-networks/thehospitalengagementnetworks.html

https://www.cms.gov/HospitalQualityInits/08_HospitalRHQDAPU.asp



C-5

Hea

lth IT

-Ena

bled

Qua

lity

Mea

sure

men

t:

Pers

pect

ives,

Pa

thw

ays

,

an

d P

ract

ica

l G

uid

an

ce

Nam

eO

rgan

izat

ion

Des

crip

tion

Func

tion

al T

agTo

pica

l Tag

Spec

ial T

ag

Inte

grat

ion

of th

e Ph

ysic

ian

Qua

lity

Rep

ortin

g Sy

stem

(P

QR

S) a

nd M

edic

are

and

Med

icai

d EH

R

Ince

ntiv

e Pr

ogra

m40

HH

S/C

MS

Intr

oduc

es a

com

mon

set o

f clin

ical

qua

lity

mea

sure

s and

a re

port

ing

met

hod

thro

ugh

whi

ch e

ligib

le p

rofe

ssio

nals

can

repo

rt to

one

pro

gram

and

qu

alify

for b

oth

ince

ntiv

e pr

ogra

ms.

Und

er th

is pi

lot,

indi

vidu

al e

ligib

le

prof

essio

nals

who

mee

t PQ

RS

repo

rtin

g re

quire

men

ts a

nd u

se c

ertifi

ed E

HR

te

chno

logy

, also

mee

t the

requ

irem

ents

for t

he m

eani

ngfu

l use

obj

ectiv

e to

re

port

clin

ical

qua

lity

mea

sure

s.

Publ

ic

Rep

ortin

g;

Ince

ntiv

e Pr

ogra

m

EHR

; M

eani

ngfu

l Use

Med

icar

e;

Med

icai

d

Mea

sure

s Man

agem

ent

Syst

em (M

MS)

41H

HS/

CM

SC

ompo

sed

of a

set o

f bus

ines

s pro

cess

es a

nd d

ecisi

on c

riter

ia th

at C

MS

fund

ed m

easu

re d

evel

oper

s (or

con

trac

tors)

follo

w w

hen

deve

lopi

ng,

impl

emen

ting

and

mai

ntai

ning

qua

lity

mea

sure

s. Th

e m

ajor

goa

l of t

he M

MS

is to

pro

vide

suffi

cien

t inf

orm

atio

n to

hel

p m

easu

re d

evel

oper

s pro

duce

hig

h ca

liber

qua

lity

mea

sure

s tha

t are

app

ropr

iate

for a

ccou

ntab

ility

pur

pose

s.

Tool

s/Pr

oduc

ts;

Qua

lity

Impr

ovem

ent

EHR

; Inn

ovat

ion

Med

icar

e/M

edic

aid

EHR

Ince

ntiv

e Pr

ogra

m40

HH

S/C

MS

Prov

ides

a fi

nanc

ial i

ncen

tive

for t

he "m

eani

ngfu

l use

" of

cer

tified

EH

R

tech

nolo

gy to

ach

ieve

hea

lth a

nd e

ffici

ency

goa

ls. B

y pu

ttin

g in

to a

ctio

n an

d m

eani

ngfu

lly u

sing

an E

HR

syst

em, p

rovi

ders

will

gai

n be

nefit

s bey

ond

finan

cial

ince

ntiv

es—

such

as r

educ

tion

in e

rror

s, av

aila

bilit

y of

reco

rds

and

data

, rem

inde

rs a

nd a

lert

s, cl

inic

al d

ecisi

on su

ppor

t, an

d e-

pres

crib

ing/

refil

l aut

omat

ion.

Ince

ntiv

e pr

ogra

mM

eani

ngfu

l Use

; EH

R; P

aym

ent/

R

eim

burs

emen

t; D

ecisi

onm

akin

g

Med

icar

e;

Med

icai

d

Nur

sing

Hom

e Q

ualit

y In

itiat

ive42

HH

S/C

MS

Prov

ides

con

sum

ers a

reso

urce

for t

he le

vel o

f car

e nu

rsin

g ho

mes

are

pr

ovid

ing

for t

heir

resid

ents

’ phy

sical

and

clin

ical

nee

ds b

y co

llect

ing

asse

ssm

ent d

ata

that

has

bee

n co

nver

ted

into

qua

lity

mea

sure

s.

Publ

ic

Rep

ortin

gPa

tient

/C

onsu

mer

s; Pa

tient

Saf

ety

Agi

ng S

ervi

ces;

Post

Acu

te

Car

e

Phys

icia

n Q

ualit

y R

epor

ting

Syst

em

(PQ

RS)

43

HH

S/C

MS

A re

port

ing

prog

ram

that

use

s a c

ombi

natio

n of

ince

ntiv

e pa

ymen

ts a

nd

paym

ent a

djus

tmen

ts to

pro

mot

e re

port

ing

of q

ualit

y in

form

atio

n by

el

igib

le p

rofe

ssio

nals

(EPs

). Th

e pr

ogra

m p

rovi

des a

n in

cent

ive

paym

ent t

o pr

actic

es w

ith E

Ps (i

dent

ified

on

clai

ms b

y th

eir i

ndiv

idua

l Nat

iona

l Pro

vide

r Id

entifi

er [N

PI] a

nd T

ax Id

entifi

catio

n N

umbe

r [T

IN]).

EPs

satis

fact

orily

re

port

dat

a on

qua

lity

mea

sure

s for

cov

ered

Phy

sicia

n Fe

e Sc

hedu

le (P

FS)

serv

ices

furn

ished

to M

edic

are

Part

B F

ee-fo

r-Se

rvic

e (F

FS) b

enefi

ciar

ies

(incl

udin

g R

ailro

ad R

etire

men

t Boa

rd a

nd M

edic

are

Seco

ndar

y Pa

yer).

Publ

ic

Rep

ortin

g;

Ince

ntiv

e Pr

ogra

m

Patie

nt S

afet

y;

Paym

ent/

R

eim

burs

emen

t

Med

icar

e

Qua

lity

Impr

ovem

ent

Org

aniz

atio

ns (Q

IOs)44

HH

S/C

MS

Mon

itors

the

effec

tiven

ess a

nd q

ualit

y of

car

e pr

ovid

ed to

Med

icar

e be

nefic

iarie

s by

impr

ovin

g th

e eff

ectiv

enes

s, effi

cien

cy, e

cono

my,

and

qual

ity

of se

rvic

es.

Qua

lity

Impr

ovem

ent

Car

e C

oord

inat

ion

Med

icar

e

Bure

au o

f Prim

ary

Hea

lth C

are

(BPH

C)45

HH

S/H

RSA

Supp

orts

Hea

lth C

ente

r Con

trol

led N

etw

orks

with

gra

nts t

hat w

ill im

prov

e th

e qua

lity

of c

are a

t com

mun

ity h

ealth

cen

ters

thro

ugh

impl

emen

tatio

n an

d ad

optio

n of

hea

lth IT

. The g

rant

s, to

talin

g m

ore t

han

$18

mill

ion

in A

fford

able

Car

e Act

fund

s, su

ppor

t 37

heal

th c

ente

r net

wor

ks a

cros

s the

cou

ntry

.

Gra

nt

Prog

ram

; Q

ualit

y Im

prov

emen

t

EHR

Ado

ptio

n/

Impl

emen

tatio

nPr

imar

y C

are;

C

omm

unity

H

ealth

Cen

ters

http://www.cms.gov/Regulations-and-Guidance/Legislation/EHRIncentivePrograms/Basics.html






https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/MMS/MeasuresManagementSystemBlueprint.html

https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/MMS/MeasuresManagementSystemBlueprint.html




http://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/NursingHomeQualityInits/index.htm

http://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/NursingHomeQualityInits/index.htm

http://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/PQRS/index.html



http://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/QualityImprovementOrgs/index.html%3Fredirect%3D/QualityImprovementOrgs/

http://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/QualityImprovementOrgs/index.html%3Fredirect%3D/QualityImprovementOrgs/

http://www.hrsa.gov/about/news/pressreleases/121220healthcenternetworks.html

http://www.hrsa.gov/about/news/pressreleases/121220healthcenternetworks.html

C-6

Hea

lth IT

-Ena

bled

Qua

lity

Mea

sure

men

t:

Pers

pect

ives,

Pa

thw

ays

,

an

d P

ract

ica

l G

uid

an

ce

Nam

eO

rgan

izat

ion

Des

crip

tion

Func

tion

al T

agTo

pica

l Tag

Spec

ial T

ag

CA

REW

are46

HH

S/H

RSA

Free

, sca

labl

e so

ftw

are

that

man

ages

and

mon

itors

HIV

clin

ical

and

su

ppor

tive

care

and

qui

ckly

pro

duce

s a c

ompl

eted

Rya

n W

hite

HIV

/AID

S Se

rvic

es R

epor

t (R

SR).

A nu

mbe

r of S

tate

s and

citi

es ru

n C

AR

EWar

e w

ith

mul

tiple

pro

vide

rs c

onne

ctin

g in

to o

ne c

entr

al d

atab

ase.

Thes

e ne

twor

k se

tups

allo

w th

e ab

ility

to tr

ack

serv

ice

usag

e an

d m

onito

r the

qua

lity

of c

are

acro

ss m

ultip

le a

genc

ies.

Tool

s/Pr

oduc

tsEH

RH

IV

IT T

oolb

oxes

47H

HS/

HR

SAPr

ovid

es e

lect

roni

c/on

line

reso

urce

s and

tech

nica

l ass

istan

ce to

hea

lth

cent

ers,

othe

r saf

ety

net p

rovi

ders

, and

am

bula

tory

car

e pr

ovid

ers t

o im

prov

e pa

tient

car

e.

Trai

ning

&

Tech

nica

l A

ssist

ance

; Q

ualit

y Im

prov

emen

t

Patie

nt S

afet

y;

Mea

ning

ful U

seA

mbu

lato

ry

Car

e;

Com

mun

ity

Hea

lth C

ente

rs

Offi

ce o

f Hea

lth

Info

rmat

ion

Tech

nolo

gy

and

Qua

lity

(OH

ITQ

)48

HH

S/H

RSA

Thro

ugh

June

of 2

013,

pro

vide

d tr

ans-

HR

SA e

xper

tise

and

lead

ersh

ip o

n he

alth

IT a

nd q

ualit

y iss

ues.

Rep

rese

ntin

g al

l of H

RSA

and

its d

iver

se

gran

tees

, OH

ITQ

soug

ht to

impr

ove

the

qual

ity o

f hea

lth c

are

for s

afet

y ne

t pop

ulat

ions

and

stre

ngth

en th

e he

alth

wor

kfor

ce th

at se

rves

thes

e po

pula

tions

. OH

ITQ

wor

ked

in c

olla

bora

tion

with

oth

er H

HS

agen

cies

(e

.g.,

ON

C, A

HRQ

) and

ext

erna

l sta

keho

lder

s (e.

g., N

QF)

on

impo

rtan

t he

alth

IT a

nd q

ualit

y in

itiat

ives

.

Trai

ning

&

Tech

nica

l A

ssist

ance

EHR

Ado

ptio

n/

Impl

emen

tatio

nD

isadv

anta

ged

Popu

latio

ns;

Com

mun

ity

Hea

lth C

ente

rs

Rur

al H

ealth

In

form

atio

n Te

chno

logy

N

etw

ork

Dev

elop

men

t Pr

ogra

m (R

HIT

ND

)49

HH

S/H

RSA

Enha

nces

hea

lth c

are

deliv

ery

in ru

ral A

mer

ica

by su

ppor

ting

rura

l hea

lth

netw

orks

in th

e ad

optio

n an

d m

eani

ngfu

l use

of e

lect

roni

c he

alth

reco

rds/

elec

tron

ic m

edic

al re

cord

s (EH

Rs/

EMR

s) as

a -t

ime

fund

ing

oppo

rtun

ity

to a

ssist

net

wor

ks in

ach

ievi

ng E

HR

/EM

R m

eani

ngfu

l use

requ

irem

ents

by

201

4. A

ctiv

ities

supp

orte

d by

RH

ITN

D g

rant

fund

s inc

lude

: wor

kflow

an

alys

is, E

HR

/EM

R st

rate

gic

plan

dev

elop

men

t, EH

R/E

MR

trai

ning

, pu

rcha

se o

f hea

lth IT

equ

ipm

ent,

iden

tifica

tion

of c

ertifi

ed h

ealth

IT

equi

pmen

t ven

dors

, and

inst

alla

tion

of b

road

band

.

Trai

ning

&

Tech

nica

l A

ssist

ance

; G

rant

Pr

ogra

m

Mea

ning

ful U

se;

EHR

Ado

ptio

n/

Impl

emen

tatio

n

Rur

al H

ealth

Indi

an H

ealth

Se

rvic

es (I

HS)

Hea

lth

Info

rmat

ion

Exch

ange

(H

IE)50

HH

S/ IH

SIm

prov

es c

oord

inat

ed h

ealth

car

e of

the

Nat

ive

Am

eric

an p

opul

atio

n by

en

ablin

g co

nnec

tivity

to in

tern

al fa

cilit

ies a

nd e

xter

nal a

genc

ies i

n th

e N

atio

nwid

e H

ealth

Info

rmat

ion

Net

wor

k (N

wH

IN) f

or th

e se

cure

exc

hang

e of

rele

vant

pat

ient

dat

a. A

llow

s fac

ilitie

s to

mai

ntai

n ow

ners

hip

and

cont

rol

over

thei

r pat

ient

dat

a. A

ssist

s in

mee

ting

mea

ning

ful u

se c

riter

ia to

exc

hang

e he

alth

info

rmat

ion

amon

g pr

ovid

ers a

nd p

atie

nts t

o im

prov

e qu

ality

, saf

ety,

and

effici

ency

of c

are,

pat

ient

eng

agem

ent,

and

popu

latio

n he

alth

.

Hea

lth

Info

rmat

ion

Exch

ange

Mea

ning

ful

Use

; Car

e C

oord

inat

ion;

In

form

atio

n Ex

chan

ge; P

atie

nt

Safe

ty

Nat

ive

Am

eric

ans

http://hab.hrsa.gov/manageyourgrant/careware.html

http://www.hrsa.gov/healthit/index.html

http://www.hrsa.gov/healthit/



http://www.hrsa.gov/ruralhealth/about/community/rhitnd.html




http://www.ihs.gov/hie/index.cfm%3Fmodule%3Ddsp_hie_overviewhttp://www.ihs.gov/hie/index.cfm%3Fmodule%3Ddsp_hie_overview




C-7

Hea

lth IT

-Ena

bled

Qua

lity

Mea

sure

men

t:

Pers

pect

ives,

Pa

thw

ays

,

an

d P

ract

ica

l G

uid

an

ce

Nam

eO

rgan

izat

ion

Des

crip

tion

Func

tion

al T

agTo

pica

l Tag

Spec

ial T

ag

Nat

iona

l Dat

a W

areh

ouse

(ND

W)51

HH

S/ IH

SA

stat

e-of

-the

-art

, ent

erpr

ise-w

ide

data

war

ehou

se e

nviro

nmen

t tha

t was

in

stitu

ted

by th

e N

atio

nal P

atie

nt In

form

atio

n R

epor

ting

Syst

em (N

PIR

S)

in 2

006

for I

HS’

nat

iona

l dat

a re

posit

ory

datin

g ba

ck to

Oct

ober

200

0. It

ga

ther

s, st

ores

, rep

orts

, and

allo

ws e

asy

acce

ss to

acc

urat

e hi

stor

ical

dat

a in

an

effo

rt to

mee

t the

adm

inist

rativ

e an

d cl

inic

al n

eeds

of I

ndia

n H

ealth

end

us

ers n

atio

nwid

e.

Publ

ic

Rep

ortin

g;

Dat

a R

epos

itory

; Tr

aini

ng &

Te

chni

cal

Ass

istan

ce

Info

rmat

ion

Exch

ange

Nat

ive

Am

eric

ans

Res

ourc

e an

d Pa

tient

M

anag

emen

t Sys

tem

(R

PMS)

52

HH

S/ IH

SA

dece

ntra

lized

, int

egra

ted

solu

tion

that

com

bine

s flex

ible

hard

war

e co

nfigu

ratio

ns, o

ver 5

0 so

ftw

are a

pplic

atio

ns, a

nd n

etw

ork

com

mun

icat

ion

com

pone

nts t

o m

anag

e clin

ical

, fina

ncia

l and

adm

inist

rativ

e inf

orm

atio

n in

he

alth

car

e fac

ilitie

s. U

sed

daily

by

Am

eric

an In

dian

/nat

ive A

lask

an h

ealth

pr

ofes

siona

ls an

d pr

ivat

e sec

tor h

ealth

faci

litie

s to

effici

ently

man

age p

rogr

ams,

max

imiz

e rev

enue

gen

erat

ion,

and

pro

vide

hig

h-qu

ality

car

e for

pat

ient

s.

Tool

s/Pr

oduc

tsEH

R; D

ata

Agg

rega

tion/

In

tegr

atio

n;

Info

rmat

ion

Exch

ange

Nat

ive

Am

eric

ans

Stan

dard

Cod

e Bo

ok

(SC

B)53

HH

S/ IH

SC

onta

ins a

list

ing

of c

odes

and

term

s int

ende

d to

ens

ure

stan

dard

ized

do

cum

enta

tion

of m

edic

al in

form

atio

n.To

ols/

Prod

ucts

Stan

dard

sN

ativ

e A

mer

ican

s

Patie

nt R

epor

ted

Out

com

es M

easu

rem

ent

Info

rmat

ion

Syst

em

(PRO

MIS

)54

HH

S/N

IHA

syst

em o

f hig

hly

relia

ble,

pre

cise

mea

sure

s of p

atie

nt-r

epor

ted

heal

th st

atus

fo

r phy

sical

, men

tal,

and

soci

al w

ell-b

eing

. It m

easu

res w

hat p

atie

nts a

re a

ble

to d

o an

d ho

w th

ey fe

el b

y as

king

que

stio

ns. P

ROM

IS’ m

easu

res c

an b

e us

ed

as p

rimar

y or

seco

ndar

y en

dpoi

nts i

n cl

inic

al st

udie

s of t

he e

ffect

iven

ess o

f tr

eatm

ent.

Tool

s/Pr

oduc

tsC

are

Tran

sform

atio

n;

Patie

nts/

Con

sum

ers

Valu

e Se

t Aut

horit

y C

ente

r (V

SAC

)55H

HS/

NIH

Prov

ides

dow

nloa

dabl

e ac

cess

to a

ll offi

cial

ver

sions

of v

ocab

ular

y va

lue

sets

con

tain

ed in

the

2014

Clin

ical

Qua

lity

Mea

sure

s (C

QM

s). Th

e va

lue

sets

des

crib

e th

e sp

ecifi

c in

clud

ed a

nd e

xclu

ded

popu

latio

ns in

ord

er to

pr

oper

ly c

alcu

late

eac

h 20

14 C

QM

. Eac

h va

lue

set c

onsis

ts o

f the

num

eric

al

valu

es a

nd h

uman

-rea

dabl

e na

mes

dra

wn

from

stan

dard

voc

abul

arie

s (e.

g.,

SNO

MED

CT

® and

ICD

-10-

CM

) whi

ch a

re u

sed

to d

efine

clin

ical

con

cept

s us

ed in

clin

ical

qua

lity

mea

sure

s. V

SAC

is p

rovi

ded

by th

e N

atio

nal L

ibra

ry

of M

edic

ine

(NLM

), in

col

labo

ratio

n w

ith O

NC

and

CM

S.

Dat

a R

epos

itory

Stan

dard

s

AH

IC Q

ualit

y W

orkg

roup

9H

HS/

ON

CA

Fede

ral a

dviso

ry b

ody

char

tere

d in

200

5 to

mak

e re

com

men

datio

ns to

th

e H

HS

Secr

etar

y on

how

to a

ccel

erat

e th

e de

velo

pmen

t and

ado

ptio

n of

he

alth

IT.

Adv

isory

Bo

dyEH

R; I

nnov

atio

n

Beac

on C

omm

unity

C

oope

rativ

e A

gree

men

t Pr

ogra

m56

HH

S/O

NC

Prov

ides

fund

ing

to c

omm

uniti

es th

at h

ave

begu

n de

velo

pmen

t of s

ecur

e,

priv

ate,

and

acc

urat

e sy

stem

s of E

HR

ado

ptio

n an

d he

alth

info

rmat

ion

exch

ange

. Thes

e co

mm

uniti

es h

ave

spec

ific

and

mea

sura

ble

goal

s for

hea

lth

syst

ems i

mpr

ovem

ent:

qual

ity, c

ost-e

ffici

ency

, and

pop

ulat

ion

heal

th.

Trai

ning

&

Tech

nica

l A

ssist

ance

; Be

acon

C

omm

uniti

es

EHR

Ado

ptio

n/

Impl

emen

tatio

n

http://www.ihs.gov/NDW/

http://www.ihs.gov/NDW/

http://www.ihs.gov/RPMS/



http://www.ihs.gov/SCB/

http://www.ihs.gov/SCB/

http://www.nihpromis.org/






http://healthit.hhs.gov/portal/server.pt%3Fopen%3D512%26objID%3D1199%26parentname%3DCommunityPage%26parentid%3D23%26mode%3D%25202%26in_hi_userid%3D11113%26cached%3Dtrue

http://healthit.hhs.gov/portal/server.pt%3Fopen%3D512%26objID%3D1199%26parentname%3DCommunityPage%26parentid%3D23%26mode%3D%25202%26in_hi_userid%3D11113%26cached%3Dtrue

http://healthit.hhs.gov/portal/server.pt%3Fopen%3D512%26objID%3D1805%26parentname%3DCommunityPage%26parentid%3D2%26mode%3D2%26cached%3Dtrue



C-8

Hea

lth IT

-Ena

bled

Qua

lity

Mea

sure

men

t:

Pers

pect

ives,

Pa

thw

ays

,

an

d P

ract

ica

l G

uid

an

ce

Nam

eO

rgan

izat

ion

Des

crip

tion

Func

tion

al T

agTo

pica

l Tag

Spec

ial T

ag

Beac

on E

vide

nce

and

Inno

vatio

n N

etw

ork

(BEI

N)56

HH

S/O

NC

Prov

ides

the

Beac

on C

omm

uniti

es w

ith g

uida

nce

in d

ocum

entin

g an

d di

ssem

inat

ing

less

ons l

earn

ed a

nd re

sults

of t

heir

indi

vidu

al e

ffort

s in

a sy

stem

atic

way

in o

rder

to g

ener

ate

actio

nabl

e, ri

goro

us e

vide

nce,

and

top

iden

tify

stra

tegi

es fo

r lev

erag

ing

heal

th IT

to im

prov

e pa

tient

car

e an

d re

duce

cos

ts.

Trai

ning

&

Tech

nica

l A

ssist

ance

; Be

acon

C

omm

uniti

es

EHR

; In

form

atio

n Ex

chan

ge

Cyp

ress

57H

HS/

ON

CA

rigor

ous a

nd re

peat

able

test

ing

tool

of E

HR

s and

EH

R m

odul

es fo

r ca

lcul

atin

g M

eani

ngfu

l Use

(MU

) Sta

ge 2

Clin

ical

Qua

lity

Mea

sure

s (C

QM

s), a

s par

t of t

he 2

014

EHR

Cer

tifica

tion

prog

ram

supp

orte

d by

the

Offi

ce o

f the

Nat

iona

l Coo

rdin

ator

for H

ealth

IT (O

NC

). Th

e to

ol is

ope

n so

urce

and

free

ly a

vaila

ble

for u

se o

r ado

ptio

n by

the

heal

th IT

com

mun

ity,

incl

udin

g EH

R v

endo

rs a

nd A

utho

rized

Tes

ting

Labs

and

incl

udes

a

stan

dard

test

dat

a of

synt

hetic

pat

ient

reco

rds t

hat e

xerc

ises a

ll of

the

MU

St

age

2 C

QM

s for

Elig

ible

Pro

fess

iona

ls an

d El

igib

le H

ospi

tals.

Tool

s/Pr

oduc

ts;

Test

ing

Mea

ning

ful U

se;

EHR

; Inn

ovat

ion;

St

anda

rds

Hea

lth e

Dec

ision

s In

itiat

ive

(HeD

)58H

HS/

ON

CA

n in

itiat

ive

usin

g th

e St

anda

rds a

nd In

tero

pera

bilit

y (S

&I)

Fra

mew

ork

to d

evel

op a

new

har

mon

ized

stan

dard

for u

se in

kno

wle

dge

artif

acts

in a

cl

inic

al d

ecisi

on su

ppor

t (C

DS)

env

ironm

ent.

Col

labo

rativ

eD

ecisi

onm

akin

g;

Stan

dard

s

HIT

Pol

icy

Com

mitt

ee59

HH

S/O

NC

A Fe

dera

l adv

isory

com

mitt

ee th

at p

rovi

des r

ecom

men

datio

ns o

n po

licy

fram

ewor

k fo

r the

dev

elop

men

t and

ado

ptio

n of

hea

lth in

form

atio

n in

fras

truc

ture

for h

ealth

info

rmat

ion

exch

ange

to th

e N

atio

nal C

oord

inat

or

for c

onsid

erat

ion.

Adv

isory

Bo

dyEH

R A

dopt

ion/

Im

plem

enta

tion

HIT

Sta

ndar

ds

Com

mitt

ee60

HH

S/O

NC

Mak

es re

com

men

datio

ns to

the

Nat

iona

l Coo

rdin

ator

on

stan

dard

s, im

plem

enta

tion

spec

ifica

tions

, and

cer

tifica

tion

crite

ria fo

r the

ele

ctro

nic

exch

ange

and

use

of h

ealth

info

rmat

ion.

Adv

isory

Bo

dyEH

R A

dopt

ion/

Im

plem

enta

tion;

St

anda

rds;

Info

rmat

ion

Exch

ange

Mea

ning

ful U

se (M

U)61

HH

S/O

NC

/C

MS

Defi

nes t

he u

se o

f EH

Rs a

nd re

late

d te

chno

logy

with

in a

hea

lth c

are

orga

niza

tion.

To

achi

eve

mea

ning

ful u

se a

nd c

olle

ct e

ligib

le in

cent

ives

, el

igib

le p

rovi

ders

and

hos

pita

ls m

ust a

dopt

cer

tified

EH

R te

chno

logy

and

use

it

to a

chie

ve sp

ecifi

c ob

ject

ives

, bro

ken

dow

n in

to a

serie

s of s

tage

s.

Ince

ntiv

e Pr

ogra

m;

Qua

lity

Impr

ovem

ent

Mea

ning

ful U

se;

Stan

dard

s

Nat

iona

l Cen

ter f

or

Cog

nitiv

e In

form

atic

s an

d D

ecisi

on M

akin

g in

H

ealth

care

(NC

CD

)62

HH

S/O

NC

A na

tionw

ide

colla

bora

tive

esta

blish

ed in

resp

onse

to th

e ur

gent

and

lo

ng-te

rm c

ogni

tive

chal

leng

es in

hea

lth IT

ado

ptio

n an

d m

eani

ngfu

l use

. N

CC

D’s

visio

n is

to b

ecom

e a

natio

nal r

esou

rce

that

pro

vide

s str

ateg

ic

lead

ersh

ip in

pat

ient

-cen

tere

d co

gniti

ve su

ppor

t res

earc

h an

d ap

plic

atio

ns in

he

alth

car

e.

Col

labo

rativ

e R

esea

rch

Mea

ning

ful

Use

; Pat

ient

s/C

onsu

mer

s

http://www.academyhealth.org/Programs/ProgramsDetail.cfm%3FItemNumber%3D7282%26navItemNumber%3D7283



http://projectcypress.org/prog_info.html

http://wiki.siframework.org/Health%2BeDecisions%2BHomepage

http://wiki.siframework.org/Health%2BeDecisions%2BHomepage

http://healthit.hhs.gov/portal/server.pt/community/healthit_hhs_gov__policy_recommendations/1815

http://healthit.hhs.gov/portal/server.pt/community/healthit_hhs_gov__policy_recommendations/1815

http://healthit.hhs.gov/portal/server.pt%3Fopen%3D512%26objID%3D1271%26parentname%3DCommunityPage%26parentid%3D6%26mode%3D2

http://healthit.hhs.gov/portal/server.pt%3Fopen%3D512%26objID%3D1271%26parentname%3DCommunityPage%26parentid%3D6%26mode%3D2

http://healthit.hhs.gov/portal/server.pt%3Fopen%3D512%26objID%3D2996%26mode%3D2

http://www.healthit.gov/policy-researchers-implementers/national-center-cognitive-informatics-and-decision-making-healthcare




C-9

Hea

lth IT

-Ena

bled

Qua

lity

Mea

sure

men

t:

Pers

pect

ives,

Pa

thw

ays

,

an

d P

ract

ica

l G

uid

an

ce

Nam

eO

rgan

izat

ion

Des

crip

tion

Func

tion

al T

agTo

pica

l Tag

Spec

ial T

ag

Nat

iona

l Sur

vey

on H

IE in

Clin

ical

La

bora

torie

s63

HH

S/O

NC

A N

atio

nal S

urve

y on

hea

lth in

form

atio

n ex

chan

ge in

clin

ical

labo

rato

ries t

o pr

ovid

e ne

w d

ata

on a

stat

e an

d na

tiona

l lev

el to

pro

vide

pol

icym

aker

s with

a

com

preh

ensiv

e un

ders

tand

ing

of e

lect

roni

c la

bora

tory

info

rmat

ion

exch

ange

ca

paci

ty a

nd a

ctiv

ity a

mon

g cl

inic

al la

bora

torie

s acr

oss t

he n

atio

n.

Res

earc

hIn

form

atio

n Ex

chan

geLa

bora

tory

ON

C S

&I F

ram

ewor

k Q

uery

Hea

lth P

roje

ct64

HH

S/O

NC

A pu

blic

-priv

ate

colla

bora

tive

to d

evel

op st

anda

rds a

nd se

rvic

es fo

r po

pula

tion

leve

l que

ries a

nd to

und

erst

and

popu

latio

n le

vel t

rend

s. Th

e O

NC

S&

I Fra

mew

ork

Que

ry H

ealth

Pro

ject

is u

se th

e H

ealth

Qua

lity

Mea

sure

Fra

mew

ork

(HQ

MF)

and

Qua

lity

Rep

ortin

g D

ocum

ent

Arc

hite

ctur

e (Q

RD

A) in

thei

r dev

elop

men

t.

Col

labo

rativ

e Pu

blic

Hea

lthSt

anda

rds

Reg

iona

l Ext

ensio

n C

ente

rs65

HH

S/O

NC

Offe

rs te

chni

cal a

ssist

ance

, gui

danc

e, a

nd in

form

atio

n on

bes

t pra

ctic

es to

su

ppor

t hea

lth c

are

prov

ider

s’ eff

orts

to b

ecom

e m

eani

ngfu

l use

rs o

f EH

Rs.

Prov

ides

trai

ning

and

supp

ort t

o as

sist d

octo

rs a

nd o

ther

pro

vide

rs in

EH

R

adop

tion

and

guid

ance

and

tech

nica

l ass

istan

ce fo

r EH

R im

plem

enta

tion.

Trai

ning

&

Tech

nica

l A

ssist

ance

EHR

Ado

ptio

n/

Impl

emen

tatio

n;

Mea

ning

ful U

se

Stra

tegi

c H

ealth

care

IT

Adv

ance

d R

esea

rch

Proj

ects

(SH

AR

P)

Prog

ram

61

HH

S/O

NC

A co

llabo

rativ

e re

sear

ch p

rogr

am th

at a

ddre

sses

pro

blem

s tha

t im

pede

the

adop

tion

of h

ealth

IT. T

rans

late

s res

earc

h in

to p

atie

nt-c

ente

red

heal

th IT

pr

oduc

ts a

nd se

rvic

es to

cre

ate

impr

ovem

ents

in c

ritic

al a

reas

resu

lting

in

hig

h-pe

rfor

min

g, le

arni

ng h

ealth

car

e sy

stem

. SH

AR

P is

led

by m

ajor

co

llabo

rativ

e eff

orts

at t

he U

nive

rsity

of I

llino

is at

Urb

ana-

Cha

mpa

ign,

the

Uni

vers

ity o

f Tex

as a

t Hou

ston

, Har

vard

Uni

vers

ity, t

he M

ayo

Clin

ic o

f M

edic

ine,

and

Mas

sach

uset

ts G

ener

al H

ospi

tal.

Res

earc

h;

Tool

s/Pr

oduc

ts;

Qua

lity

Impr

ovem

ent

EHR

Ado

ptio

n/

Impl

emen

tatio

n;

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vatio

n

Stat

e H

ealth

In

form

atio

n Ex

chan

ge

(Sta

te H

IE) C

oope

rativ

e A

gree

men

t Pro

gram

66

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NC

Supp

orts

Sta

tes i

n es

tabl

ishin

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alth

info

rmat

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exch

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abili

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amon

g he

alth

car

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with

in a

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cros

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effor

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mov

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tow

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tero

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re re

spon

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ncre

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nnec

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ena

blin

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info

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flow

to im

prov

e th

e qu

ality

and

effi

cien

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&

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ssist

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are

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rmat

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Exch

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pita

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Test

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rove

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the

Nat

iona

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oord

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or 2

014

Editi

on67

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S/O

NC

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ides

test

pro

cedu

res,

test

dat

a, a

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or u

se in

test

ing

and

cert

ifyin

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chno

logy

und

er th

e O

NC

HIT

Cer

tifica

tion

Prog

ram

(fo

rmer

ly re

ferr

ed to

as t

he P

erm

anen

t Cer

tifica

tion

Prog

ram

). C

ertifi

catio

n of

hea

lth IT

pro

vide

s ass

uran

ce to

pur

chas

ers a

nd o

ther

use

rs th

at a

n EH

R

syst

em o

ffers

the

nece

ssar

y te

chno

logi

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apab

ility

, fun

ctio

nalit

y, an

d se

curit

y to

hel

p th

em m

eet M

U o

bjec

tives

and

mea

sure

s.

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ing;

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ols/

Prod

ucts

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ptio

n/

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emen

tatio

n;

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ning

ful U

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Beha

vior

al H

ealth

C

linic

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ualit

y eM

easu

res P

roje

ct68

HH

S/SA

MH

SA/

ON

CD

evel

ops a

por

tfolio

of b

ehav

iora

l hea

lth c

linic

al q

ualit

y m

easu

res s

uita

ble

for i

nclu

sion

in th

e EH

R in

cent

ive

prog

ram

for M

eani

ngfu

l Use

of H

ealth

In

form

atio

n Te

chno

logy

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Qua

lity

Impr

ovem

ent

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ning

ful U

se;

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ptio

n/

Impl

emen

tatio

n

Beha

vior

al

Hea

lth

http://www.4medapproved.com/hitanswers/onc-sponsores-national-survey/



http://wiki.siframework.org/Query%2BHealth%2B-%2BProject%2BCharter

http://wiki.siframework.org/Query%2BHealth%2B-%2BProject%2BCharter

http://healthit.hhs.gov/portal/server.pt/community/healthit_hhs_gov__rec_program/1495

http://healthit.hhs.gov/portal/server.pt/community/healthit_hhs_gov__rec_program/1495

http://healthit.hhs.gov/portal/server.pt/community/healthit_hhs_gov__sharp_program/1806




http://healthit.hhs.gov/portal/server.pt/community/healthit_hhs_gov__state_health_information_exchange_program/1488




http://www.healthit.gov/policy-researchers-implementers/2014-edition-final-test-method




http://healthit.hhs.gov/portal/server.pt/community/healthit_hhs_gov__behavioral_health/3866



C-1

0

Hea

lth IT

-Ena

bled

Qua

lity

Mea

sure

men

t:

Pers

pect

ives,

Pa

thw

ays

,

an

d P

ract

ica

l G

uid

an

ce

Nam

eO

rgan

izat

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ag

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A de

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arkl

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s Blu

e Bu

tton

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tiona

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avai

labl

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Oct

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sum

er

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gem

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lity

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nt S

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Qua

lity

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ERI)

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es th

e qu

ality

and

out

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es o

f VA

heal

th c

are

by sy

stem

atic

ally

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plem

entin

g cl

inic

al re

sear

ch fi

ndin

gs a

nd e

vide

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base

d re

com

men

datio

ns

into

rout

ine

clin

ical

pra

ctic

e. In

eva

luat

ing

qual

ity o

f car

e, th

e Q

UER

I pr

oces

s foc

uses

on

thre

e el

emen

ts: s

truc

ture

, pro

cess

, and

out

com

es.

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earc

hIn

nova

tion;

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tient

Saf

ety

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rans

VA M

edic

al C

ente

r in

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anap

olis

to

exch

ange

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atio

n us

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the

Nat

ionw

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lth

Info

rmat

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Net

wor

k72

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pilo

t, de

velo

ped

in p

artn

ersh

ip b

etw

een

the

VA M

edic

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ente

r in

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anap

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Indi

ana

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form

atio

n Ex

chan

ge (I

HIE

), to

secu

rely

exc

hang

e EH

R in

form

atio

n us

ing

the

Nat

ionw

ide

Hea

lth

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rmat

ion

Net

wor

k. A

llow

s for

safe

r and

mor

e se

cure

acc

ess o

f ele

ctro

nic

heal

th in

form

atio

n th

at w

ill e

nhan

ce q

ualit

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car

e fo

r Vet

eran

s.

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lth

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rmat

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ange

: C

olla

bora

tive

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lity

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ovem

ent

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e C

oord

inat

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form

atio

n Ex

chan

ge

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rans

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rans

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lth

Info

rmat

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Syst

ems

and

Tech

nolo

gy

Arc

hite

ctur

e (V

istA)

73

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ovid

es a

n in

tegr

ated

inpa

tient

and

out

patie

nt e

lect

roni

c he

alth

reco

rd fo

r VA

pat

ient

s and

offe

rs a

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istra

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tool

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help

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deliv

er th

e be

st q

ualit

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edic

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edic

al p

ract

ices

.

Beac

on

Com

mun

ities

; Q

ualit

y Im

prov

emen

t

EHR

Ado

ptio

n/

Impl

emen

tatio

n;

Car

e Tr

ansfo

rmat

ion

http://www.va.gov/BLUEBUTTON/Blue_Button_Getting_Started.asp

http://www.patientsafety.gov/patients.html

http://www.patientsafety.gov/patients.html

http://www.queri.research.va.gov/about/default.cfm



http://www.indianapolis.va.gov/features/Indianapolis_VAMC_Launches_Health_Information_Exchange.asp






http://www.ehealth.va.gov/VistA.asp




http://www.iha.org/p4p_california.html

http://www.iha.org/p4p_california.html

http://www.coloradobeaconconsortium.org/





C-1

1

Hea

lth IT

-Ena

bled

Qua

lity

Mea

sure

men

t:

Pers

pect

ives,

Pa

thw

ays

,

an

d P

ract

ica

l G

uid

an

ce

Nam

eO

rgan

izat

ion

Des

crip

tion

Func

tion

al T

agTo

pica

l Tag

Spec

ial T

ag

Col

orad

o A

ssoc

iate

d C

omm

unity

Hea

lth

Info

rmat

ion

Ente

rpris

e (C

ACH

IE) (

AH

RQ

EQM

Gra

nt)76

Col

orad

o C

omm

unity

M

anag

ed

Car

e N

etw

ork

(CC

MC

N):

Non

-Pro

fit

An

inte

rope

rabl

e qua

lity

info

rmat

ion

syste

m fo

r a c

olla

bora

tive n

etw

ork

of

seve

n co

mm

unity

hea

lth c

ente

rs (C

HC

s) w

ithin

the C

olor

ado

Com

mun

ity

Man

aged

Car

e Net

wor

k (C

CM

N).

CC

MN

is a

non-

profi

t net

wor

k or

gani

zatio

n th

at su

ppor

ts Fe

dera

lly Q

ualifi

ed H

ealth

Cen

ters

in im

prov

ing

qual

ity o

f car

e and

hea

lth o

utco

mes

thro

ugh

data

driv

en im

prov

emen

t pr

oces

ses a

nd su

ppor

ts C

HC

s in

achi

evin

g m

eani

ngfu

l use

of E

HR

tech

nolo

gy.

Qua

lity

Impr

ovem

ent

Info

rmat

ion

Exch

ange

; M

eani

ngfu

l Use

Com

mun

ity

Hea

lth C

ente

rs

eHea

lth C

onne

ctic

ut77

eHea

lth

Con

nect

icut

Faci

litat

es th

e st

atew

ide

adop

tion

of E

HR

s, he

alth

info

rmat

ion

exch

ange

, an

d qu

ality

repo

rtin

g to

dra

mat

ical

ly im

prov

e th

e qu

ality

, saf

ety,

and

effici

ency

of h

ealth

car

e in

Con

nect

icut

.

Col

labo

rativ

e;

Hea

lth

Info

rmat

ion

Exch

ange

; Q

ualit

y Im

prov

emen

t;

EHR

Ado

ptio

n/

Impl

emen

tatio

n;

Patie

nt S

afet

y;

Info

rmat

ion

Exch

ange

Haw

ai‘i

Isla

nd B

eaco

n C

omm

unity

(HIB

C)78

Col

lege

of

Phar

mac

y at

the

Uni

vers

ity o

f H

awai

i at H

ilo

A $6

80,0

00 c

ontr

act a

war

ded

to N

orth

Haw

ai‘i

Com

mun

ity H

ospi

tal

(NH

CH

) to

impl

emen

t an

HIE

syst

em th

roug

hout

the

Nor

th H

awai

‘i re

gion

. This

HIE

will

impa

ct m

ore

than

32,

000

patie

nts a

nd m

ark

the

first

st

ep to

war

d an

isla

nd-w

ide

HIE

.

Hea

lth

Info

rmat

ion

Exch

ange

; Be

acon

C

omm

uniti

es

EHR

Ado

ptio

n/

Impl

emen

tatio

n;

Info

rmat

ion

Exch

ange

Cen

tral

Indi

ana

Beac

on

Com

mun

ity79

Indi

ana

Hea

lth

Info

rmat

ion

Exch

ange

Prov

ides

info

rmat

ion

in a

secu

re, s

tand

ardi

zed,

and

ele

ctro

nic

form

at,

enab

ling

info

rmat

ion

to fo

llow

the

patie

nt, r

athe

r tha

n be

ing

hous

ed in

one

ph

ysic

ian

office

or a

sing

le h

ospi

tal s

yste

m. A

ssem

bles

this

heal

th d

ata

in

a m

eani

ngfu

l way

to h

elp

prov

ider

s ach

ieve

impr

oved

hea

lth o

utco

mes

for

thei

r pat

ient

s, w

ith a

spec

ific

focu

s on

canc

er sc

reen

ings

, dia

bete

s car

e, h

eart

he

alth

, ast

hma

care

, wel

l-chi

ld v

isits

, and

oth

er c

are

inte

rven

tions

.

Hea

lth

Info

rmat

ion

Exch

ange

; Be

acon

C

omm

uniti

es

Car

e C

oord

inat

ion;

Pa

tient

s/C

onsu

mer

s; In

form

atio

n Ex

chan

ge; D

ata

Agg

rega

tion/

In

tegr

atio

n

Ast

hma;

D

iabe

tes;

Car

diac

Car

e;

Can

cer

The

Qua

lity

Hea

lth

Firs

t Pro

gram

79In

dian

a H

ealth

In

form

atio

n Ex

chan

ge

Hel

ps id

entif

y, pr

even

t, an

d m

anag

e ch

roni

c co

nditi

ons i

n pa

tient

s. Pa

tient

s ne

edin

g in

terv

entio

ns a

re fl

agge

d fo

r the

ir pr

ovid

ers,

allo

win

g th

em to

mak

e th

e be

st c

are

deci

sions

pos

sible

. The

prog

ram

use

s inf

orm

atio

n fr

om th

e In

dian

a N

etw

ork

for P

atie

nt C

are

to a

ssem

ble

lab

resu

lts, i

mm

uniz

atio

ns,

diag

nose

s, ca

rdio

logy

and

radi

olog

y re

port

s, pr

oced

ures

, and

med

icat

ion

info

rmat

ion.

This

info

rmat

ion

is th

en o

rgan

ized

in e

asy-

to-r

ead

patie

nt

sum

mar

ies t

hat c

an b

e us

ed b

efor

e, d

urin

g, o

r aft

er p

atie

nt v

isits

to m

ake

the

best

use

of p

hysic

ians

' tim

e.

Qua

lity

Impr

ovem

ent

Info

rmat

ion

Exch

ange

; Dat

a A

ggre

gatio

n/

Inte

grat

ion;

D

ecisi

onm

akin

g

http://www.cachie.org/





http://www.ehealthconnecticut.org/

http://www.hibeacon.org/

http://www.hibeacon.org/

http://www.ihie.org/


http://www.ihie.org/quality-health-first-program

http://www.ihie.org/quality-health-first-program

C-1

2

Hea

lth IT

-Ena

bled

Qua

lity

Mea

sure

men

t:

Pers

pect

ives,

Pa

thw

ays

,

an

d P

ract

ica

l G

uid

an

ce

Nam

eO

rgan

izat

ion

Des

crip

tion

Func

tion

al T

agTo

pica

l Tag

Spec

ial T

ag

Indi

ana

Hea

th

Info

rmat

ion

Exch

ange

79In

dian

a St

ate

and

loca

l hea

lth

depa

rtm

ents

, R

egen

strie

f In

stitu

te, p

rivat

e ho

spita

ls,

BioC

ross

road

s an

d ot

her

heal

th c

are

and

com

mun

ity

orga

niza

tions

in

Indi

ana

Ope

rate

s the

Nat

ion’

s lar

gest

HIE

, par

tner

ing

with

com

mun

ities

thro

ugho

ut

Indi

ana

to e

nsur

e he

alth

info

rmat

ion

is w

here

it n

eeds

to b

e w

hen

it ne

eds t

o be

ther

e to

impr

ove

care

coo

rdin

atio

n an

d pa

tient

out

com

es.

Hea

lth

Info

rmat

ion

Exch

ange

Car

e co

ordi

natio

n;

Patie

nt S

afet

y;

Info

rmat

ion

Exch

ange

Iow

a e-

Hea

lth80

Iow

a D

epar

tmen

t of

Hea

lthC

olla

bora

tion

of c

onsu

mer

s, he

alth

car

e pr

ovid

ers,

paye

rs, a

nd o

ther

s to

esta

blish

an

elec

tron

ic H

IE fo

r Iow

a. Th

e Io

wa

Hea

lth In

form

atio

n N

etw

ork

will

allo

w c

onsu

mer

s, he

alth

car

e pr

ovid

ers,

paye

rs a

nd o

ther

par

ticip

ants

se

cure

ly a

cces

s vita

l rea

l-tim

e pa

tient

hea

lth in

form

atio

n th

roug

hout

the

stat

e of

Iow

a an

d be

yond

.

Hea

lth

Info

rmat

ion

Exch

ange

Patie

nts/

Con

sum

ers;

Car

e C

oord

inat

ion;

In

form

atio

n Ex

chan

ge

Cre

scen

t City

Bea

con

Com

mun

ity (C

CBC

) an

d th

e Pr

imar

y C

are

Dev

elop

men

t C

orpo

ratio

n (P

CD

C)

Tran

sform

atio

n In

itiat

ive81

CC

BC, P

CD

C

(Lou

isian

a)Se

eks t

o st

reng

then

the

New

Orle

ans h

ealth

car

e in

fras

truc

ture

and

impr

ove

care

for m

ore

than

50,

000

unde

rser

ved

patie

nts b

y in

trod

ucin

g in

nova

tive

heal

th IT

syst

ems a

nd to

ols d

esig

ned

to im

prov

e qu

ality

, car

e co

ordi

natio

n,

and

popu

latio

n he

alth

.

Publ

ic H

ealth

; Q

ualit

y Im

prov

emen

t

Inno

vatio

n;

Info

rmat

ion

Exch

ange

; Car

e C

oord

inat

ion

Prim

ary

Car

e;

Disa

dvan

tage

d Po

pula

tions

Loui

siana

Hea

lth C

are

Red

esig

n C

olla

bora

tive

and

Loui

siana

Hea

lth

Info

rmat

ion

Exch

ange

(L

aHIE

)82

Loui

siana

N

atio

nal Q

ualit

y Fo

rum

Supp

orts

the

exch

ange

of h

ealth

info

rmat

ion

amon

g pr

ovid

ers a

nd

orga

niza

tions

in th

e St

ate,

acc

ordi

ng to

nat

iona

lly re

cogn

ized

stan

dard

s. Th

e Fo

rum

col

lect

s and

use

s dat

a to

gui

de im

prov

emen

ts in

hea

lth c

are

qual

ity.

Hea

lth

Info

rmat

ion

Exch

ange

; Q

ualit

y Im

prov

emen

t

Patie

nts/

Con

sum

ers;

Car

e C

oord

inat

ion;

In

form

atio

n Ex

chan

ge

All-

Paye

r Cla

ims

Dat

abas

e83M

assa

chus

etts

G

over

nmen

tSt

ream

lines

requ

ired

data

subm

issio

ns fo

r pay

ers a

nd a

lso a

fford

s a d

eep

unde

rsta

ndin

g of

the

Mas

sach

uset

ts h

ealth

car

e sy

stem

by

prov

idin

g ac

cess

to

timel

y, co

mpr

ehen

sive,

and

det

aile

d da

ta. Th

e A

PCD

is c

ompr

ised

of m

edic

al,

phar

mac

y, an

d de

ntal

cla

ims,

as w

ell a

s inf

orm

atio

n ab

out m

embe

r elig

ibili

ty,

bene

fit d

esig

n, a

nd p

rovi

ders

for a

ll pa

yers

cov

erin

g M

assa

chus

etts

resid

ents

.

Paye

rU

sing

Cla

ims

Dat

a; D

ata

Agg

rega

tion/

In

tegr

atio

n

East

ern

Mon

tana

Te

lem

edic

ine

Net

wor

k (E

MT

N)84

East

ern

Mon

tana

Te

lem

edic

ine

Net

wor

k

Prom

otes

tele

med

icin

e se

rvic

es a

t the

loca

l, St

ate,

regi

onal

, nat

iona

l, an

d in

tern

atio

nal l

evel

. Use

s tel

ecom

mun

icat

ion

tech

nolo

gies

to tr

ansm

it re

al-t

ime

vide

o, a

udio

, and

med

ical

imag

es. P

atie

nts c

an re

mai

n in

thei

r co

mm

uniti

es a

nd se

e sp

ecia

lists

from

Mon

tana

and

acr

oss t

he N

atio

n.

Qua

lity

Impr

ovem

ent;

Tool

s/Pr

oduc

ts

Tele

med

icin

e



http://www.iowaehealth.org/

http://www.pcdc.org/news/press-releases/crescent-city-beacon-and-pcdc-partner.html%3Futm_source%3DCrescent%2520City%2520Beacon%2520-%2520STAKEHOLDER%2520MSG%26utm_campaign%3Dccbcstakeholder%26utm_medium%3Demail







http://www.lhcqf.org/





http://www.mass.gov/chia/researcher/health-care-delivery/hcf-data-resources/apcd/

http://www.mass.gov/chia/researcher/health-care-delivery/hcf-data-resources/apcd/

http://www.emtn.org/medical.html



C-1

3

Hea

lth IT

-Ena

bled

Qua

lity

Mea

sure

men

t:

Pers

pect

ives,

Pa

thw

ays

,

an

d P

ract

ica

l G

uid

an

ce

Nam

eO

rgan

izat

ion

Des

crip

tion

Func

tion

al T

agTo

pica

l Tag

Spec

ial T

ag

Hea

lthSh

are

Mon

tana

(H

SM)—

Con

tinui

ty o

f C

are

Rec

ord/

Doc

umen

t (C

CR

/CC

D)-

Base

d H

IE P

roje

ct85

Hea

lthSh

are

Mon

tana

Impl

emen

ts a

stat

ewid

e H

IE u

sing

aggr

egat

ed C

CR

/CC

D st

anda

rd d

ata

unde

r a c

olla

bora

tive

agre

emen

t with

ON

C. Th

e C

CR

or C

CD

con

tain

s an

ext

ract

of a

per

son’

s med

ical

info

rmat

ion

and

can

incl

ude

the

core

dat

a re

quire

d in

the

CM

S Fi

nal R

ule

for E

HR

pro

gram

s. It

has b

een

stan

dard

ized

in

term

s of c

onte

nt a

nd te

chni

cal s

peci

ficat

ions

, can

be

popu

late

d as

a

stan

d-al

one

docu

men

t or e

xtra

cted

from

an

EHR

, can

be

exch

ange

d el

ectr

onic

ally,

vie

wed

usin

g an

y W

eb b

row

ser,

copi

ed to

ele

ctro

nic

med

ia

or p

rinte

d on

pap

er; a

nd c

an b

e us

ed in

a c

linic

al d

ata

repo

sitor

y fo

r in

form

atio

n an

alys

is th

at c

an p

rovi

de k

ey p

erfo

rman

ce m

easu

re re

port

s to

prov

ider

s and

oth

er u

sers

.

Hea

lth

Info

rmat

ion

Exch

ange

EHR

; In

form

atio

n Ex

chan

ge; D

ata

Agg

rega

tion/

In

tegr

atio

n

Min

neso

ta e

Hea

lth

Initi

ativ

e86M

inne

sota

D

epar

tmen

t of

Hea

lth

Allo

ws c

onsu

mer

s, he

alth

car

e pr

ovid

ers,

paye

rs a

nd o

ther

par

ticip

ants

to

secu

rely

acc

ess v

ital r

eal-t

ime

patie

nt h

ealth

info

rmat

ion

thro

ugho

ut th

e st

ate

of Io

wa

and

beyo

nd to

hel

p m

ake

best

dec

ision

s. Fu

nded

thro

ugh

the

Stat

e H

ealth

Info

rmat

ion

Exch

ange

Coo

pera

tive

Agr

eem

ent P

rogr

am, e

stab

lishe

d un

der t

he H

ITEC

H A

ct a

nd a

dmin

ister

ed b

y O

NC

.

Hea

lth

Info

rmat

ion

Exch

ange

Patie

nts/

Con

sum

ers;

Car

e C

oord

inat

ion;

In

form

atio

n Ex

chan

ge;

Dec

ision

mak

ing

Alig

ning

For

ces f

or

Qua

lity

(AF4

Q)

Initi

ativ

e in

Min

neso

ta87

Rob

ert W

ood

John

son

Foun

datio

n (M

inne

sota

)

A st

ate

initi

ativ

e, p

art o

f a b

road

er st

akeh

olde

r gro

up, l

ed b

y M

inne

sota

C

omm

unity

Mea

sure

men

t (M

NC

M) t

o ac

cele

rate

the

impr

ovem

ent o

f he

alth

by

publ

icly

repo

rtin

g he

alth

car

e in

form

atio

n. It

striv

es to

be

a tr

uste

d so

urce

for p

erfo

rman

ce m

easu

rem

ent a

nd p

ublic

repo

rtin

g of

qua

lity

data

an

d to

serv

e as

a re

sour

ce fo

r pro

vide

rs to

impr

ove

care

and

for p

atie

nts t

o m

ake

bett

er h

ealth

dec

ision

s.

Publ

ic

Rep

ortin

g;

Qua

lity

Impr

ovem

ent

Dec

ision

mak

ing;

Pa

tient

s/C

onsu

mer

s

Brin

ging

Mea

sure

men

t to

the

Poin

t of C

are

(AH

RQ E

QM

Gra

nt)22

New

Yor

k C

ity

Dep

artm

ent

of H

ealth

and

M

enta

l Hyg

iene

Impl

emen

ts m

eani

ngfu

l mea

sure

men

ts o

f the

qua

lity

of c

are

that

focu

s on

publ

ic h

ealth

prio

rity

issue

s, di

sadv

anta

ged

popu

latio

ns, a

nd sm

all o

ffice

pr

actic

es. Th

e N

ew Y

ork

City

Dep

artm

ent o

f Hea

lth a

nd M

enta

l Hyg

iene

(D

OH

MH

) Tak

e C

are

New

Yor

k in

itiat

ive

has a

rtic

ulat

ed 1

0 pr

iorit

y pu

blic

he

alth

issu

es th

at re

quire

coo

rdin

ated

act

ion

betw

een

heal

th c

are

prov

ider

s, pa

tient

s, co

mm

unity

org

aniz

atio

ns, a

nd g

over

nmen

t age

ncie

s. Th

e D

OH

MH

Pr

imar

y C

are

Info

rmat

ion

Proj

ect (

PCIP

) use

s hea

lth IT

for p

opul

atio

n-w

ide

mea

sure

men

t and

impr

ovem

ent o

f clin

ical

car

e in

thes

e 10

dom

ains

, pa

rtic

ular

ly a

mon

g di

sadv

anta

ged

popu

latio

ns.

Publ

ic H

ealth

C

are

Coo

rdin

atio

nD

isadv

anta

ged

Popu

latio

ns;

Prim

ary

Car

e

New

York

City

’s H

ealth

eH

eart

s Ini

tiativ

e88N

ew Y

ork

City

D

epar

tmen

t of

Hea

lth a

nd

Men

tal H

ygie

ne

A on

e-ye

ar p

ilot t

o re

war

d an

d re

cogn

ize

prim

ary

care

phy

sicia

ns w

ho u

se

elec

tron

ic h

ealth

reco

rds t

o he

lp a

dult

patie

nts a

chie

ve e

xcel

lent

hea

rt h

ealth

an

d im

prov

e ou

tcom

es in

four

spec

ific

area

s: as

pirin

, blo

od p

ress

ure

cont

rol,

chol

este

rol c

ontr

ol a

nd sm

okin

g ce

ssat

ion

(ABC

’s).

Ince

ntiv

e Pr

ogra

mPa

tient

s/C

onsu

mer

s; EH

R; C

are

Tran

sform

atio

n

Prim

ary

Car

e;

Car

diac

Car

e

Tri-S

tate

Reg

iona

l Ex

tens

ion

Cen

ter

(REC

)89

Hea

lthBr

idge

(O

hio,

Indi

ana,

K

entu

cky)

Prov

ides

supp

ort f

or m

edic

al p

ract

ices

to im

plem

ent a

nd u

se E

HR

s eff

ectiv

ely

to e

xcha

nge

patie

nt in

form

atio

n se

cure

ly, m

eet m

eani

ngfu

l use

re

quire

men

ts, a

nd q

ualif

y fo

r inc

entiv

e pa

ymen

ts.

Trai

ning

&

Tech

nica

l A

ssist

ance

EHR

Ado

ptio

n/

Impl

emen

tatio

n;

Mea

ning

ful U

se

http://www.healthsharemontana.org/





http://www.health.state.mn.us/e-health/hie.html

http://www.health.state.mn.us/e-health/hie.html

http://forces4quality.org/alliance/minnesota%23facebook






http://www.nyc.gov/html/doh/downloads/pdf/tcny/tcny-2012.pdf

http://www.nyc.gov/html/doh/downloads/pdf/tcny/tcny-2012.pdf

http://www.tristaterec.org/



C-1

4

Hea

lth IT

-Ena

bled

Qua

lity

Mea

sure

men

t:

Pers

pect

ives,

Pa

thw

ays

,

an

d P

ract

ica

l G

uid

an

ce

Nam

eO

rgan

izat

ion

Des

crip

tion

Func

tion

al T

agTo

pica

l Tag

Spec

ial T

ag

Med

ical

Ass

istan

ce

Prov

ider

Ince

ntiv

e R

epos

itory

(MA

PIR

)90

Penn

sylv

ania

D

epar

tmen

t of

Publ

ic W

elfa

re

A St

ate-

leve

l inf

orm

atio

n sy

stem

for t

he E

HR

ince

ntiv

e pr

ogra

m th

at tr

acks

an

d ac

ts a

s a re

posit

ory

for i

nfor

mat

ion

rela

ted

to p

aym

ent,

appl

icat

ions

, at

test

atio

ns, o

vers

ight

func

tions

, and

inte

rfac

e w

ith C

MS’

Nat

iona

l Le

vel R

epos

itory

.

Dat

a R

epos

itory

; In

cent

ive

Prog

ram

EHR

; M

eani

ngfu

l Use

The

Verm

ont B

luep

rint

for H

ealth

91Ve

rmon

tA

stat

e-le

d he

alth

car

e re

form

pro

gram

ded

icat

ed to

ach

ieve

wel

l-coo

rdin

ated

an

d se

amle

ss h

ealth

serv

ices

to th

e re

siden

ts o

f Ver

mon

t by

impl

emen

ting

a no

vel m

odel

des

igne

d to

impr

ove

the

heal

th o

f the

pop

ulat

ion,

to e

nhan

ce

the

patie

nt e

xper

ienc

e of

car

e, a

nd to

redu

ce th

e pe

r cap

ita c

ost o

f car

e.

Qua

lity

Impr

ovem

ent

Car

e Tr

ansfo

rmat

ion

Pri

vate

Pro

gram

s (al

phab

etiz

ed b

y or

gani

zati

on)

Alta

Med

Pro

vide

r Po

rtal

92A

ltaM

edA

bidi

rect

iona

l pro

vide

r por

tal o

fferin

g co

nnec

tivity

acr

oss A

ltaM

ed p

rovi

ders

. Fo

cuse

s on

thre

e int

egra

ted

IT p

latfo

rms t

o en

gage

with

com

mun

ity

phys

icia

ns to

impl

emen

t con

stan

t pro

cess

impr

ovem

ents

to im

prov

e qua

lity,

enha

nce p

atie

nt e

xper

ienc

e, a

nd p

rovi

de v

alue

. Cur

rent

ly su

ppor

ts A

ltaM

ed’s

PCM

H a

nd h

as p

lans

to e

xpan

d be

yond

Alta

Med

pro

vide

rs.

Car

e Pr

ovid

er;

Tool

s/Pr

oduc

ts;

Dat

a R

epos

itory

Patie

nts/

Con

sum

ers;

EHR

; Dat

a A

ggre

gatio

n/

Inte

grat

ion

Patie

nt

Cen

tere

d M

edic

al H

ome

Am

eric

an M

edic

al

Ass

ocia

tion

(AM

A)

Phys

icia

n C

onso

rtiu

m

for P

erfo

rman

ce

Impr

ovem

ent (

PCPI

)93

AM

AA

phys

icia

n-le

d pr

ogra

m d

edic

ated

to e

nhan

cing

qua

lity

and

patie

nt sa

fety

w

ith th

e on

goin

g m

issio

n to

alig

n pa

tient

-cen

tere

d ca

re, p

erfo

rman

ce

mea

sure

men

t, an

d qu

ality

impr

ovem

ent.

As o

ne o

f the

lead

ing

mea

sure

de

velo

pers

in th

e U

nite

d St

ates

, PC

PI id

entifi

es, d

evel

ops,

test

s, an

d im

plem

ents

mea

sure

s with

the

goal

of i

mpr

ovin

g ca

re a

nd a

ccou

ntab

ility

. PC

PI is

also

a le

adin

g fo

rce

in e

nabl

ing

the

use

of m

easu

res i

n EH

Rs,

whi

ch

ofte

n ne

ed to

be

re-s

peci

fied

from

the

pape

r mea

sure

defi

nitio

n.

Qua

lity

Impr

ovem

ent

EHR

; Pat

ient

Sa

fety

Col

labo

rativ

e fo

r Pe

rfor

man

ce M

easu

re

Inte

grat

ion

with

EH

R

Syst

ems94

AM

AA

colla

bora

tive

co-s

pons

ored

by

the

Am

eric

an M

edic

al A

ssoc

iatio

n (A

MA)

, the

HIM

SS E

lect

roni

c H

ealth

Rec

ord

Ass

ocia

tion

(ER

HA)

, and

N

CQ

A an

d co

mpr

ised

of a

gro

up o

f sta

keho

lder

s—pe

rfor

man

ce m

easu

re

deve

lope

rs, E

HR

ven

dors

, exp

ert E

HR

use

rs, n

atio

nal q

ualit

y im

prov

emen

t or

gani

zatio

ns a

nd te

chni

cal e

xper

ts in

phy

sicia

n pe

rfor

man

ce m

easu

rem

ent

and

qual

ity im

prov

emen

t—w

ho h

ave

a sh

ared

goa

l of f

acili

tatin

g th

e in

tegr

atio

n of

per

form

ance

mea

sure

s with

EH

R sy

stem

s.

Col

labo

rativ

e Q

ualit

y Im

prov

emen

t

EHR

Nat

iona

l Qua

lity

Reg

istry

Net

wor

k (N

QR

N) C

oord

inat

ing

Task

For

ce95

AM

A D

evel

ops d

raft

reco

mm

enda

tions

for c

onsid

erat

ion

by in

tere

sted

or

gani

zatio

ns (e

.g.,

gove

rnm

ent a

genc

ies s

uch

as C

MS)

for w

ays i

n w

hich

ex

istin

g re

gist

ries c

an b

e be

tter

use

d an

d ne

w re

gist

ries i

ncen

tiviz

ed.

Adv

isory

Bo

dyEH

R; I

nnov

atio

n;

Patie

nts/

Con

sum

ers

The

Test

ing

and

Prot

otyp

ing

Qua

lity

Elec

tron

ic M

easu

res

(eM

easu

res)

Net

wor

k (T

PQeN

et)96

AM

A/N

CQ

AA

colla

bora

tive

led

by P

CPI

with

NC

QA

to c

reat

e an

d de

ploy

qua

lity

mea

sure

s in

EHR

s. Th

is in

itiat

ive

test

s cur

rent

and

new

qua

lity

mea

sure

s to

hel

p bu

ild c

onfid

ence

in q

ualit

y re

port

s and

dem

onst

rate

s how

to ru

n a

test

ing

netw

ork.

Test

ing;

C

olla

bora

tive

Inno

vatio

n; E

HR

http://www.dpw.state.pa.us/provider/healthcaremedicalassistance/medicalassistancehealthinformationtechnologyinitiative/maprovincentiverepos/index.htm



http://hcr.vermont.gov/blueprint

http://hcr.vermont.gov/blueprint

http://www.altamed.org/

http://www.altamed.org/

http://www.ama-assn.org/ama/pub/physician-resources/physician-consortium-performance-improvement.page





http://www.ama-assn.org/ama/pub/physician-resources/physician-consortium-performance-improvement/about-pcpi/collaborative-performance-measure-integration.page




http://www.ama-assn.org/resources/doc/cqi/pcpi-102111-shiahan.pdf




http://www.ama-assn.org/resources/doc/cqi/pcpi-0311-testing-presentation.pdf





C-1

5

Hea

lth IT

-Ena

bled

Qua

lity

Mea

sure

men

t:

Pers

pect

ives,

Pa

thw

ays

,

an

d P

ract

ica

l G

uid

an

ce

Nam

eO

rgan

izat

ion

Des

crip

tion

Func

tion

al T

agTo

pica

l Tag

Spec

ial T

ag

Phys

icia

n Q

ualit

y R

epor

ting

Syst

em

(PQ

RS)

Aca

dem

ic

Test

ing

Net

wor

k96

AM

A/U

HC

A co

llabo

rativ

e pr

ojec

t with

the

Uni

vers

ity H

ealth

Syst

em C

onso

rtiu

m

(UH

C) a

nd th

e A

ssoc

iatio

n of

Am

eric

an M

edic

al C

olle

ges (

AA

MC

). PC

PI-

deve

lope

d, in

divi

dual

phy

sicia

n m

easu

res u

sed

in th

e 20

10 P

QR

S an

d th

is co

llabo

rativ

e fo

cus o

n m

easu

res t

hat a

re N

QF

time-

limite

d en

dors

ed o

r N

QF-

endo

rsed

® and

due

for M

aint

enan

ce.

Col

labo

rativ

e;

Qua

lity

Impr

ovem

ent;

Test

ing

Patie

nt

Safe

ty; C

are

Coo

rdin

atio

n

Am

bula

tory

Sur

gery

C

ente

r Qua

lity

Col

labo

76

ASC

Qua

lity

Col

labo

ratio

nA

grou

p of

lead

ers a

nd e

xper

ts fr

om th

e am

bula

tory

surg

ery

indu

stry

and

am

bula

tory

surg

ery

orga

niza

tions

who

focu

s on

heal

th c

are

qual

ity a

nd

safe

ty to

dev

elop

stan

dard

ized

qua

lity

mea

sure

s, pu

blic

ally

repo

rt q

ualit

y da

ta, a

dvan

ce A

SC q

ualit

y, an

d to

adv

ocat

e fo

r a sy

stem

that

pro

vide

s m

eani

ngfu

l inf

orm

atio

n fo

r sta

keho

lder

s.

Publ

ic

Rep

ortin

g;

Col

labo

rativ

e;

Qua

lity

Impr

ovem

ent

EHR

Ado

ptio

n/

Impl

emen

tatio

n;

Patie

nt S

afet

y

Am

bula

tory

C

are

Bipa

rtisa

n Po

licy

Cen

ter98

Bipa

rtisa

n Po

licy

Cen

ter (

BPC

)A

nonp

rofit

org

aniz

atio

n th

at d

rives

prin

cipl

ed so

lutio

ns th

roug

h rig

orou

s an

alys

is, re

ason

ed n

egot

iatio

n, a

nd re

spec

tful

dia

logu

e. F

ound

ed in

200

7 by

form

er S

enat

e M

ajor

ity L

eade

rs H

owar

d Ba

ker,

Tom

Das

chle

, Bob

Dol

e,

and

Geo

rge

Mitc

hell,

BPC

com

bine

s pol

itica

lly b

alan

ced

polic

ymak

ing

with

st

rong

, pro

activ

e ad

voca

cy a

nd o

utre

ach.

Adv

isory

Bo

dy; Q

ualit

y Im

prov

emen

t

Inno

vatio

n

Bipa

rtisa

n Po

licy

Cen

ter:

Tran

sform

ing

Hea

lth C

are:

The

Rol

e of

Hea

lth IT

98

Bipa

rtisa

n Po

licy

Cen

ter

A st

udy

of c

omm

on a

ttrib

utes

of t

he n

atio

n’s h

ighe

st p

erfo

rmin

g he

alth

car

e or

gani

zatio

ns to

hel

p sh

ape

publ

ic p

olic

y an

d in

vest

men

t dec

ision

s reg

ardi

ng

heal

th IT

.

Adv

isory

Bo

dy; Q

ualit

y Im

prov

emen

t

EHR

Im

plem

enta

tion;

In

nova

tion;

Pa

tient

s/C

onsu

mer

s

Bipa

rtisa

n Po

licy

Cen

ter,

Hea

lth

Inno

vatio

n In

itiat

ive99

Bipa

rtisa

n Po

licy

Cen

ter

Con

duct

s res

earc

h an

d co

llabo

rate

s with

exp

erts

and

stak

ehol

ders

acr

oss

ever

y se

ctor

of h

ealth

car

e to

dev

elop

reco

mm

enda

tions

that

pro

mot

e in

nova

tion

and

the

use

of IT

to su

ppor

t im

prov

emen

ts in

the

cost

, qua

lity,

and

patie

nt e

xper

ienc

e of

car

e.

Adv

isory

; C

olla

bora

tive;

R

esea

rch

Inno

vatio

n

Boun

dary

Med

ical

100

Boun

dary

M

edic

alA

scal

able

, clo

ud-b

ased

soft

war

e sy

stem

use

d to

supp

ort t

he m

edic

al sp

ecia

lty

outc

omes

net

wor

k m

odel

. It c

an a

lso b

e us

ed fo

r oth

er o

utco

mes

trac

king

pr

ogra

ms a

s wel

l as f

or tr

aditi

onal

regi

strie

s or i

ndep

ende

nt c

linic

al st

udie

s.

Tool

s/Pr

oduc

ts

Mon

itorin

g In

tens

ifica

tion

of T

reat

men

t for

H

yper

glyc

emia

and

H

yper

lipid

emia

(AH

RQ

EQM

Gra

nt)22

Brig

ham

and

W

omen

’s H

ospi

tal

A ne

w, v

alid

ated

met

hod

to m

onito

r dia

bete

s qua

lity

of c

are

mea

sure

ut

ilizi

ng n

atur

al la

ngua

ge p

roce

ssin

g (N

LP) t

ools

to a

naly

ze c

linic

al fr

ee-te

xt

note

s in

an E

MR

. A p

revi

ous a

pplic

atio

n, w

hich

ana

lyze

s clin

ical

not

es in

or

der t

o id

entif

y tr

eatm

ent i

nten

sifica

tion

of h

yper

tens

ion,

will

be

furt

her

deve

lope

d to

iden

tify

trea

tmen

t int

ensifi

catio

n in

the

man

agem

ent o

f hy

perg

lyce

mia

and

hyp

erlip

idem

ia.

Tool

s/Pr

oduc

tsEH

RD

iabe

tes





http://ascquality.org/



http://bipartisanpolicy.org/library/report/transforming-health-care-role-health-it






http://bipartisanpolicy.org/projects/health-innovation-initiative



http://www.boundarymedical.com/about.html

http://healthit.ahrq.gov/ahrq-funded-projects/monitoring-intensification-treatment-hyperglycemia-and-hyperlipidemia






C-1

6

Hea

lth IT

-Ena

bled

Qua

lity

Mea

sure

men

t:

Pers

pect

ives,

Pa

thw

ays

,

an

d P

ract

ica

l G

uid

an

ce

Nam

eO

rgan

izat

ion

Des

crip

tion

Func

tion

al T

agTo

pica

l Tag

Spec

ial T

ag

Cal

iforn

ia C

linic

al

Dat

a Pr

ojec

t: C

ALI

NX

St

anda

rds10

1

Cal

iforn

ia

Hea

lthca

re

Foun

datio

n

A co

llabo

rativ

e eff

ort o

f ind

ustr

y st

akeh

olde

rs to

dev

elop

and

impl

emen

t la

bora

tory

and

pha

rmac

y da

ta st

anda

rds t

hrou

ghou

t Cal

iforn

ia to

faci

litat

e da

ta in

tegr

atio

n of

clin

ical

info

rmat

ion

syst

ems.

This

proj

ect i

mpr

oves

ch

roni

c ca

re b

y pr

omot

ing

acce

ss to

, and

inte

grat

ion

of, c

linic

al d

ata

at th

e po

int o

f car

e.

Col

labo

rativ

eSt

anda

rds

Labo

rato

ry;

Phar

mac

y

Build

ing

Mod

ular

Pe

diat

ric C

hron

ic

Dise

ase

Reg

istrie

s for

Q

I and

CE

Res

earc

h (A

HRQ

ED

M G

rant

)102

Chi

ldre

n’s

Hos

pita

l M

edic

al C

ente

r C

inci

nnat

i

Supp

orts

a le

arni

ng h

ealth

car

e sy

stem

by

build

ing

an o

pen-

acce

ss d

ata

shar

ing

netw

ork

focu

sing

on a

ped

iatr

ic p

opul

atio

n to

impr

ove

care

and

in

crea

se p

atie

nt p

artic

ipat

ion.

The

mod

ular

, ver

satil

e, a

nd sc

alab

le re

gist

ry

will

be

popu

late

d by

EH

Rs a

nd u

sed

to su

ppor

t qua

lity

impr

ovem

ent a

nd

com

para

tive

effec

tiven

ess r

esea

rch.

Col

labo

rativ

e;

Tool

s/Pr

oduc

ts;

Res

earc

h

Dat

a R

epos

itory

Ope

n A

cces

s for

Pat

ient

s to

thei

r EM

Rs t

hrou

gh

MyC

hart

18

Cle

vela

nd C

linic

A ne

w tr

ansp

aren

t MyC

hart

EM

R g

ives

Cle

vela

nd C

linic

pat

ient

s acc

ess t

o pa

thol

ogy

reco

rds,

X-ra

y re

port

s, ph

ysic

ian

note

s, an

d th

e lis

t of t

heir

curr

ent

heal

th is

sues

, whi

ch p

hysic

ians

use

to b

riefly

des

crib

e a

patie

nt’s

heal

th st

atus

, re

cent

con

cern

s, an

d kn

own

diag

nose

s. Pa

tient

s will

be

able

to v

iew

onl

ine

near

ly e

very

thin

g th

eir d

octo

r see

s in

thei

r EM

R, e

xcep

t for

beh

avio

ral

heal

th in

form

atio

n, w

hich

is p

rohi

bite

d fr

om re

leas

e by

stat

e la

w.

Tool

s/Pr

oduc

tsEH

R; P

atie

nts/

Con

sum

ers

Com

mon

Wel

l Hea

lth

Alli

ance

103

Com

mon

Wel

l H

ealth

Alli

ance

An

inde

pend

ent n

ot-fo

r-pro

fit tr

ade

asso

ciat

ion

that

is o

pen

to a

ll he

alth

in

form

atio

n te

chno

logy

ven

dors

dev

oted

to b

ette

r exc

hang

e pa

tient

dat

a be

twee

n sy

stem

s and

car

e se

ttin

gs. I

t pla

ns to

pro

mot

e an

d ce

rtify

a n

atio

nal

infr

astr

uctu

re w

ith c

omm

on st

anda

rds a

nd p

olic

ies t

hat w

ill e

nsur

e th

at

prod

ucts

that

disp

lay

its se

al h

ave

been

cer

tified

to w

ork

on th

e na

tiona

l in

fras

truc

ture

. Ini

tial p

artic

ipat

ion

incl

udes

Cer

ner,

McK

esso

n, A

llscr

ipts

, at

hena

heal

th, G

reen

way

, and

Rel

ayH

ealth

.

Tool

s/Pr

oduc

tsSt

anda

rds;

EHR

; In

form

atio

n Ex

chan

ge

Dev

elop

ing

and

Usin

g Va

lid C

linic

al

Qua

lity

Met

rics f

or

Hea

lth In

form

atio

n Te

chno

logy

with

Hea

lth

Info

rmat

ion

Exch

ange

(A

HRQ

EQ

M G

rant

)22

Cor

nell

Uni

vers

ity, J

oan

and

Sanf

ord

I. W

eill

Med

ical

C

olle

ge

Iden

tifies

exi

stin

g, a

nd c

reat

es n

ew, c

linic

al q

ualit

y of

car

e m

etric

s abl

e to

be

relia

bly

retr

ieve

d fr

om e

lect

roni

c sy

stem

s, su

ch a

s EH

Rs a

nd H

IEs.

Res

earc

hIn

form

atio

n Ex

chan

ge; E

HR

; St

anda

rds;

Inno

vatio

n

The

Pers

onal

Out

com

e M

easu

res10

4C

ounc

il on

Q

ualit

y an

d Le

ader

ship

A qu

ality

mea

sure

syst

em w

ith in

dica

tors

that

pro

vide

inve

stig

ator

s with

th

e bu

ildin

g bl

ocks

tow

ard

deve

lopm

ent o

f qua

lity

mea

sure

s tha

t cou

ld

asce

rtai

n w

heth

er se

rvic

es e

nsur

e co

nsum

er c

hoic

e, p

artic

ipan

t-dire

ctio

n, a

nd

indi

vidu

al sa

tisfa

ctio

n.

Qua

lity

Impr

ovem

ent;

Tool

s/Pr

oduc

ts

Patie

nts/

Con

sum

ers

http://www.chcf.org/projects/2008/california-clinical-data-project-calinx-standards



http://www.edm-forum.org/publicgrant/About/projectprofiles/cincireg/





http://www.healthcareitnews.com/news/cleveland-clinic-opens-emr-patients



http://www.commonwellalliance.org/

http://www.commonwellalliance.org/

http://healthit.ahrq.gov/ahrq-funded-projects/developing-and-using-valid-clinical-quality-metrics-health-information







http://www.thecouncil.org/Personal_Outcome_Measures.aspx

http://www.thecouncil.org/Personal_Outcome_Measures.aspx

C-1

7

Hea

lth IT

-Ena

bled

Qua

lity

Mea

sure

men

t:

Pers

pect

ives,

Pa

thw

ays

,

an

d P

ract

ica

l G

uid

an

ce

Nam

eO

rgan

izat

ion

Des

crip

tion

Func

tion

al T

agTo

pica

l Tag

Spec

ial T

ag

The

Dire

ct P

roje

ct10

5Th

e D

irect

Pr

ojec

tD

evel

ops s

peci

ficat

ions

for a

secu

re, s

cala

ble,

stan

dard

s-ba

sed

way

to

esta

blish

uni

vers

al h

ealth

add

ress

ing

and

tran

spor

t for

par

ticip

ants

(in

clud

ing

prov

ider

s, la

bora

torie

s, ho

spita

ls, p

harm

acie

s and

pat

ient

s) to

send

en

cryp

ted

heal

th in

form

atio

n di

rect

ly to

kno

wn

and

trus

ted

reci

pien

ts o

ver

the

Inte

rnet

.

Tool

s/Pr

oduc

tsIn

form

atio

n Ex

chan

ge;

Stan

dard

s

eHea

lth In

itiat

ive8

eHea

lth In

itiat

ive

A m

ultis

take

hold

er o

rgan

izat

ion

that

seek

s to

driv

e im

prov

emen

t in

the

qual

ity, s

afet

y, an

d effi

cien

cy o

f hea

lth c

are

thro

ugh

info

rmat

ion

and

tech

nolo

gy. C

reat

es to

ols a

nd st

rate

gies

to d

rive

impr

ovem

ent i

n th

e qu

ality

of

hea

lth c

are

by p

rom

otin

g th

e us

e of

tech

nolo

gy a

nd in

form

atio

n. A

lso

enga

ges i

n re

sear

ch, e

duca

tion,

and

adv

ocac

y.

Col

labo

rativ

e;

Qua

lity

Impr

ovem

ent;

Tool

s/Pr

oduc

ts;

Trai

ning

&

Tech

nica

l A

ssist

ance

; R

esea

rch

EHR

; Inn

ovat

ion;

Pa

tient

Saf

ety

The

CPM

Fr

amew

ork™

106

Else

vier

/Clin

ical

Pr

actic

e M

odel

s (C

PM) R

esou

rce

Cen

ter

Con

sists

of s

ix c

linic

al p

ract

ice

mod

els t

hat c

an b

e ap

plie

d ac

ross

the

cont

inuu

m o

f car

e. O

ffers

an

upda

ted

and

unifi

ed re

latio

nal d

atab

ase

that

st

ores

tagg

ed d

ata

elem

ents

for c

ompr

ehen

sive

care

pla

nnin

g/co

ordi

natio

n an

d do

cum

enta

tion

of c

linic

al se

rvic

es b

y a

dive

rse

inte

rdisc

iplin

ary

team

. Th

e re

sult

is st

anda

rdiz

ed c

linic

al d

ocum

enta

tion

supp

ort t

hat i

s evi

denc

e-ba

sed

and

desig

ned

to c

aptu

re in

divi

dual

det

ails

at th

e pa

tient

leve

l.

Tool

s/Pr

oduc

tsC

are

Tran

sform

atio

n;

Car

e C

oord

inat

ion

Qua

lity

Alli

ance

St

eerin

g C

omm

ittee

107

Enge

lber

g C

ente

r fo

r Hea

lth

Car

e R

efor

m a

t th

e Br

ooki

ngs

Inst

itutio

n

A co

llabo

rativ

e eff

ort a

imed

at i

mpl

emen

ting

mea

sure

s to

impr

ove

the

qual

ity

and

effici

ency

of h

ealth

car

e ac

ross

the

Uni

ted

Stat

es. I

t com

bine

s the

effo

rts

of th

e A

mbu

lato

ry C

are

Qua

lity

Alli

ance

(AQ

A) a

nd th

e H

ospi

tal Q

ualit

y A

llian

ce (H

QA)

to b

ette

r coo

rdin

ate

the

prom

otio

n of

qua

lity

mea

sure

men

t, tr

ansp

aren

cy, a

nd im

prov

emen

t in

care

.

Adv

isory

Bo

dy;

Col

labo

rativ

e

Dec

ision

mak

ing;

EH

R; P

atie

nts/

C

onsu

mer

s

Acu

te C

are

Hos

pita

l; A

mbu

lato

ry

Car

e

Elec

tron

ic S

uppo

rt

for P

ublic

Hea

lth–

Vacc

ine

Adv

erse

Eve

nt

Rep

ortin

g Sy

stem

(ESP

: VA

ERS)

(AH

RQ E

QM

G

rant

)108

Har

vard

Pilg

rim

Hea

lth C

are,

Inc.

A ge

nera

lizab

le sy

stem

to fa

cilit

ate

dete

ctio

n an

d cl

inic

ian

repo

rtin

g of

va

ccin

e ad

vers

e ev

ents

to th

e N

atio

nal V

acci

ne A

dver

se E

vent

Rep

ortin

g Sy

stem

(VA

ERS)

usin

g EM

R d

ata

avai

labl

e fr

om a

ll am

bula

tory

car

e en

coun

ters

in a

larg

e m

ultis

peci

alty

pra

ctic

e. S

erve

d as

an

exte

nsio

n of

the

Elec

tron

ic S

uppo

rt fo

r Pub

lic H

ealth

pro

ject

, an

auto

mat

ed sy

stem

usin

g EH

R d

ata

to d

etec

t and

secu

rely

repo

rt c

ases

of s

tatu

tory

not

ifiab

le d

iseas

es

to a

loca

l pub

lic h

ealth

aut

horit

y.

Publ

ic H

ealth

; To

ols/

Prod

ucts

Patie

nt S

afet

y;

Patie

nts/

Con

sum

ers

Am

bula

tory

C

are

Adv

ance

d H

ospi

tal C

are

in H

awai

i109

Haw

aii M

edic

al

Serv

ices

A

ssoc

iatio

n (H

MSA

) and

Pr

emie

r

A na

tiona

l pro

gram

spon

sore

d by

HM

SA fo

r elig

ible

Haw

aii h

ospi

tals

that

se

ts th

e st

anda

rd fo

r pat

ient

-cen

tere

d, h

igh

qual

ity, a

nd e

ffici

ent h

ealth

car

e.

The

prog

ram

sets

targ

ets a

nd h

elps

hos

pita

ls m

easu

re a

nd sp

eed

perf

orm

ance

im

prov

emen

ts. Th

e N

atio

n’s fi

rst s

tate

wid

e pa

rtne

rshi

p be

twee

n a

heal

th p

lan

and

hosp

itals,

this

proj

ect u

ses P

rem

ier Q

UES

T b

est p

ract

ices

to m

easu

re

thei

r pro

gres

s.

Qua

lity

Impr

ovem

ent;

Col

labo

rativ

e

Info

rmat

ion

Exch

ange

; Pa

ymen

t/

Rei

mbu

rsem

ent

Acu

te-C

are

Hos

pita

l

http://wiki.directproject.org/

http://www.ehealthinitiative.org/about-us.html

http://www.cpmrc.com/2010/10/the-journey-of-the-evolving-cpm-framework%25E2%2584%25A2-and-six-clinical-practice-models/

http://www.cpmrc.com/2010/10/the-journey-of-the-evolving-cpm-framework%25E2%2584%25A2-and-six-clinical-practice-models/

http://www.healthqualityalliance.org/about-qasc

http://www.healthqualityalliance.org/about-qasc

http://healthit.ahrq.gov/ahrq-funded-projects/electronic-support-public-health-vaccine-adverse-event-reporting-system






http://www.hmsa.com/mediacenter/press/2011/110512_premier.aspx

http://www.hmsa.com/mediacenter/press/2011/110512_premier.aspx

C-1

8

Hea

lth IT

-Ena

bled

Qua

lity

Mea

sure

men

t:

Pers

pect

ives,

Pa

thw

ays

,

an

d P

ract

ica

l G

uid

an

ce

Nam

eO

rgan

izat

ion

Des

crip

tion

Func

tion

al T

agTo

pica

l Tag

Spec

ial T

ag

Hea

lthQ

x Pl

atfo

rm11

0H

ealth

Qx

A pl

atfo

rm th

at c

an a

naly

ze la

rge

amou

nts o

f dat

a to

supp

ort v

alue

-bas

ed

heal

th c

are

deci

sions

.To

ols/

Prod

ucts

EHR

; D

ecisi

onm

akin

g

Patie

nt-C

ente

red

Med

ical

Hom

e (P

CM

H) I

nitia

tive11

1

Hig

hmar

k, In

c.A

n in

itiat

ive

that

incl

udes

nea

rly 1

,050

prim

ary

care

doc

tors

in m

ore

than

10

0 ph

ysic

ian

prac

tices

that

cov

er a

bout

171

,000

Hig

hmar

k m

embe

rs in

w

este

rn a

nd c

entr

al P

enns

ylva

nia

and

Wes

t Virg

inia

. The

initi

ativ

e w

as

expa

nded

aft

er a

succ

essf

ul o

ne-y

ear p

ilot a

nd is

bas

ed o

n th

e PC

MH

pr

emise

that

phy

sicia

ns ta

ke g

reat

er a

ccou

ntab

ility

in c

oord

inat

ing

the

care

fo

r pat

ient

s in

a PC

MH

mod

el. Th

is ac

coun

tabi

lity

mea

ns a

ssist

ing

patie

nts

and

thei

r fam

ilies

with

trea

tmen

t and

trea

tmen

t opt

ions

and

pro

vidi

ng b

ette

r pa

tient

edu

catio

n an

d us

e of

tool

s suc

h as

EH

Rs.

Paye

rPa

tient

s/C

onsu

mer

s; Pa

ymen

t/

Rei

mbu

rsem

ent

Patie

nt

Cen

tere

d M

edic

al

Hom

e;

Prim

ary

Car

e

Elec

tron

ic H

ealth

R

ecor

d A

ssoc

iatio

n (E

HR

A)11

2

HIM

SSA

trad

e as

soci

atio

n of

EH

R c

ompa

nies

, add

ress

ing

natio

nal e

ffort

s to

crea

te

inte

rope

rabl

e EH

Rs i

n ho

spita

l and

am

bula

tory

car

e se

ttin

gs. E

HR

A op

erat

es o

n th

e pr

emise

that

the

rapi

d, w

ides

prea

d ad

optio

n of

EH

Rs w

ill

help

impr

ove

the

qual

ity o

f pat

ient

car

e as

wel

l as t

he p

rodu

ctiv

ity a

nd

sust

aina

bilit

y of

the

heal

th c

are

syst

em.

Col

labo

rativ

e;

Adv

isory

Bo

dy

EHR

Ado

ptio

n/

Impl

emen

tatio

n;

Info

rmat

ion

Exch

ange

Acu

te C

are

Hos

pita

l; A

mbu

lato

ry

Car

e

Clin

ical

Qua

lity

Info

rmat

ion

(CQ

I)

Wor

kgro

up11

3

HL7

C

reat

es a

nd m

aint

ains

info

rmat

ion

tech

nolo

gy st

anda

rds i

n su

ppor

t of

impr

ovin

g he

alth

car

e qu

ality

, inc

ludi

ng c

linic

al c

are

and

fost

ers

colla

bora

tion

betw

een

qual

ity m

easu

rem

ent,

outc

omes

and

impr

ovem

ent

stak

ehol

ders

. Coo

rdin

ates

ext

erna

lly w

ith o

rgan

izat

ions

such

as:

Qua

lity

Mea

sure

Dev

elop

ing

and

Endo

rsem

ent O

rgan

izat

ions

, Hea

lth C

are

Acc

redi

ting

Org

aniz

atio

ns, H

ealth

Car

e D

eliv

ery

and

Man

agem

ent

Org

aniz

atio

ns, P

atie

nt S

afet

y O

rgan

izat

ions

, Qua

lity

Impr

ovem

ent

Org

aniz

atio

ns, p

rofe

ssio

nal s

ocie

ties a

nd g

over

nmen

t age

ncie

s.

Col

labo

rativ

e;

Qua

lity

Impr

ovem

ent

Stan

dard

s

Info

rmed

Med

ical

D

ecisi

ons F

ound

atio

n114

Info

rmed

M

edic

al

Dec

ision

s Fo

unda

tion

Adv

ance

s evi

denc

e-ba

sed

shar

ed d

ecisi

onm

akin

g th

roug

h re

sear

ch, p

olic

y, cl

inic

al m

odel

s, an

d pa

tient

dec

ision

aid

s. Th

eir m

issio

n is

to in

form

and

am

plify

the

patie

nt’s

voic

e in

hea

lth c

are

deci

sions

by

deve

lopi

ng h

igh

qual

ity

deci

sion

aids

, fun

ding

rese

arch

and

shar

ed d

ecisi

onm

akin

g de

mon

stra

tion

sites

, and

adv

anci

ng th

eir m

issio

n th

roug

h ou

trea

ch a

nd a

dvoc

acy.

Qua

lity

Impr

ovem

ent;

Col

labo

rativ

e;

Res

earc

h

Patie

nts/

Con

sum

ers;

Dec

ision

mak

ing

Inte

grat

ing

the

Hea

lthca

re E

nter

prise

115

Inte

grat

ing

the

Hea

lthca

re

Ente

rpris

e

An

initi

ativ

e by

hea

lth c

are

prof

essio

nals

and

indu

stry

to im

prov

e th

e w

ay c

ompu

ter s

yste

ms i

n he

alth

car

e sh

are

info

rmat

ion.

Pro

mot

es th

e co

ordi

nate

d us

e of

est

ablis

hed

stan

dard

s suc

h as

DIC

OM

and

HL7

to

addr

ess s

peci

fic c

linic

al n

eeds

in su

ppor

t of o

ptim

al p

atie

nt c

are

on th

e pr

emise

that

syst

ems d

evel

oped

in a

ccor

danc

e w

ith IH

E co

mm

unic

ate

with

on

e an

othe

r bet

ter,

are

easie

r to

impl

emen

t, an

d en

able

car

e pr

ovid

ers t

o us

e in

form

atio

n m

ore

effec

tivel

y.

Col

labo

rativ

eIn

form

atio

n Ex

chan

ge;

Stan

dard

s

http://healthqx.com/

https://www.highmark.com/hmk2/newsroom/pressreleases/2013/pr012313.shtml



http://www.himssehra.org/ASP/index.asp



http://www.hl7.org/Special/committees/cqi/overview.cfm





http://www.ihe.net/

http://www.ihe.net/

C-1

9

Hea

lth IT

-Ena

bled

Qua

lity

Mea

sure

men

t:

Pers

pect

ives,

Pa

thw

ays

,

an

d P

ract

ica

l G

uid

an

ce

Nam

eO

rgan

izat

ion

Des

crip

tion

Func

tion

al T

agTo

pica

l Tag

Spec

ial T

ag

Veno

us

Thro

mbo

embo

lism

(V

TE)

Col

labo

rativ

e116

John

s Hop

kins

M

edic

ine

Impl

emen

ted

a m

anda

tory

risk

ass

essm

ent t

ool a

nd p

roph

ylax

is or

der s

et in

to

the

com

pute

rized

pro

vide

r ord

er e

ntry

(CPO

E) sy

stem

at J

ohns

Hop

kins

H

ospi

tal.

Beca

use

of th

e el

ectr

onic

form

at o

f the

risk

ass

essm

ent t

ool a

nd

prop

hyla

xis o

rder

set,

they

wer

e ab

le to

est

ablis

h a

data

base

that

cap

ture

s pa

tient

-spe

cific

risk

stra

ta a

nd p

roph

ylax

is pr

escr

ibed

. This

data

base

is u

sed

to li

nk p

roce

ss m

easu

res,

scre

enin

g, ri

sk st

ratu

m a

nd p

resc

riptio

n to

clin

ical

ou

tcom

es in

ord

er to

eva

luat

e an

d im

prov

e m

etho

ds fo

r VT

E pr

even

tion

and

to b

ette

r defi

ne th

e co

ncep

t of V

TE

asso

ciat

ed p

reve

ntab

le h

arm

.

Col

labo

rativ

e;

Dat

a R

epos

itory

; To

ols/

Prod

ucts

EHR

; Pat

ient

Sa

fety

; Hos

pita

l A

cqui

red

Infe

ctio

ns

Acu

te C

are

Hos

pita

l

Med

icat

ion

Mon

itorin

g fo

r Vu

lner

able

Pop

ulat

ions

vi

a In

form

atio

n Te

chno

logy

: A

mbu

lato

ry a

nd S

afet

y (A

HRQ

EQ

M G

rant

)117

John

s Hop

kins

U

nive

rsity

A pr

actic

e-ba

sed,

cro

ss-s

ectio

nal d

emon

stra

tion

of th

e ab

ility

of i

nter

oper

able

he

alth

info

rmat

ion

exch

ange

and

a C

ertifi

catio

n C

omm

issio

n fo

r Hea

lth

Info

rmat

ion

Tech

nolo

gy-c

ertifi

ed E

HR

to p

rovi

de u

sefu

l qua

lity

and

safe

ty m

easu

res f

or th

e vu

lner

able

pop

ulat

ions

serv

ed b

y tw

o Ba

ltim

ore

Med

ical

Sys

tem

(BM

S) C

omm

unity

Hea

lth C

ente

r (C

HC

) clin

ics.

The

qual

ity a

nd sa

fety

mea

sure

s eva

luat

ed w

ere

deve

lope

d fo

r am

bula

tory

ca

re b

y N

CQ

A, s

uppo

rted

by

NQ

F, a

nd fo

cuse

d on

safe

ty m

onito

ring

for

chro

nic

med

icat

ions

com

mon

ly u

sed

by p

atie

nts w

ith h

eart

dise

ase

and

diab

etes

mel

litus

.

Res

earc

h;

Car

e Pr

ovid

erEH

R;

Info

rmat

ion

Exch

ange

; M

edic

atio

n M

anag

emen

t; Pa

tient

Saf

ety

Am

bula

tory

C

are;

Car

diac

C

are;

Dia

bete

s; C

omm

unity

H

ealth

C

ente

rs;

Phar

mac

y;

Disa

dvan

tage

d Po

pula

tions

Cor

e eM

easu

re P

ilot

Proj

ect11

8Th

e Jo

int

Com

miss

ion

A pi

lot p

roje

ct th

at te

sted

the

feas

ibili

ty, u

sefu

lnes

s, an

d co

sts a

ssoc

iate

d w

ith th

e im

plem

enta

tion

of c

ore

mea

sure

s. Pa

rtic

ipan

ts w

ere

draw

n fr

om

elev

en S

tate

hos

pita

l ass

ocia

tions

that

exp

ress

ed a

n in

tere

st in

par

ticip

atin

g in

a p

ilot p

roje

ct. F

ive

wer

e ra

ndom

ly se

lect

ed to

par

ticip

ate

(Con

nect

icut

, M

ichi

gan,

Miss

ouri,

Geo

rgia

and

Rho

de Is

land

). Ea

ch h

ospi

tal a

ssoc

iatio

n th

en id

entifi

ed a

sing

le p

erfo

rman

ce m

easu

rem

ent s

yste

m a

nd h

ospi

tals

to

part

icip

ant,

for a

tota

l of 8

3 ho

spita

l par

ticip

ants

acr

oss n

ine

stat

es.

Test

ing;

R

esea

rch;

C

olla

bora

tive

EHR

Acu

te C

are

Hos

pita

l

Ory

x119

The

Join

t C

omm

issio

nIn

tegr

ates

out

com

es a

nd o

ther

per

form

ance

mea

sure

dat

a in

to th

e ac

cred

itatio

n pr

oces

s. O

RY

X m

easu

rem

ent r

equi

rem

ents

are

inte

nded

to

supp

ort J

oint

Com

miss

ion-

accr

edite

d or

gani

zatio

ns in

thei

r qua

lity

impr

ovem

ent e

ffort

s. Pe

rfor

man

ce m

easu

res a

re e

ssen

tial t

o th

e cr

edib

ility

of

any

mod

ern

eval

uatio

n ac

tivity

for h

ealth

car

e or

gani

zatio

ns.

Tool

s/Pr

oduc

ts;

Qua

lity

Impr

ovem

ent

Patie

nts/

Con

sum

ers;

Stan

dard

s; D

ata

Agg

rega

tion/

In

tegr

atio

n

Auto

mat

ing

Ass

essm

ent

of A

sthm

a C

are

Qua

lity

(AH

RQ E

QM

Gra

nt)12

0

Kai

ser

Foun

datio

n R

esea

rch

Inst

itute

Use

d sp

ecia

lized

com

pute

r pro

gram

s to

anal

yze

the

elec

tron

ic m

edic

al

reco

rds o

f ast

hma

patie

nts a

nd u

ltim

atel

y to

det

erm

ine

(1) w

heth

er a

sthm

a pa

tient

s in

two

diffe

rent

hea

lth c

are

syst

ems a

re re

ceiv

ing

reco

mm

ende

d ca

re

and

(2) h

ow to

bet

ter m

onito

r the

del

iver

y of

ast

hma

care

.

Tool

s/Pr

oduc

ts;

Res

earc

h

EHR

Ast

hma

http://www.hopkinsmedicine.org/innovation_quality_patient_care/areas_expertise/infections_complications/dvt/preventing/hopkins_journey.html



http://healthit.ahrq.gov/ahrq-funded-projects/medication-monitoring-vulnerable-populations-information-technology-mmiti







http://www.jointcommission.org/assets/1/18/A_Comprehensive_Review_of_Development_for_Core_Measures.pdf

http://www.jointcommission.org/assets/1/18/A_Comprehensive_Review_of_Development_for_Core_Measures.pdf

http://www.jointcommission.org/facts_about_oryx_for_hospitals/

http://www.kpchr.org/research/public/StudyView.aspx%3FstdID%3D486



C-2

0

Hea

lth IT

-Ena

bled

Qua

lity

Mea

sure

men

t:

Pers

pect

ives,

Pa

thw

ays

,

an

d P

ract

ica

l G

uid

an

ce

Nam

eO

rgan

izat

ion

Des

crip

tion

Func

tion

al T

agTo

pica

l Tag

Spec

ial T

ag

Feed

back

of T

reat

men

t In

tens

ifica

tion

Dat

a to

R

educ

e C

ardi

ovas

cula

r D

iseas

e (C

VD

) Risk

(A

HRQ

EQ

M G

rant

)121

Kai

ser

Foun

datio

n R

esea

rch

Inst

itute

Leve

rage

d he

alth

IT, i

nclu

ding

Kai

ser P

erm

anen

te’s

Cer

tifica

tion

Com

miss

ion

for H

ealth

Info

rmat

ion

Tech

nolo

gy-c

ertifi

ed E

pic-

base

d EM

R

Hea

lthC

onne

ct a

nd th

e po

pula

tion

man

agem

ent s

oftw

are

tool

use

d fo

r the

Pr

even

ting

Hea

rt A

ttac

ks a

nd S

trok

es E

very

day

(PH

ASE

) pro

gram

, to

crea

te

and

deliv

er tr

eatm

ent i

nten

sifica

tion

info

rmat

ion

to p

rovi

ders

who

hav

e hi

gh C

VD

-risk

pat

ient

s. W

orke

d w

ith e

ight

prim

ary

care

faci

litie

s of K

aise

r Pe

rman

ente

Nor

ther

n C

alifo

rnia

to a

sses

s whe

ther

the

use

of sy

stem

atic

fe

edba

ck o

n th

e ne

ed fo

r tre

atm

ent i

nten

sifica

tion

in p

atie

nts w

ith p

oor

cont

rol o

f CV

D ri

sk fa

ctor

s im

prov

es ri

sk-fa

ctor

con

trol

.

Col

labo

rativ

e;

Tool

s/

Prod

ucts

; R

esea

rch

EHR

Pr

imar

y C

are;

A

mbu

lato

ry

Car

e; C

ardi

ac

Car

e

Usin

g In

form

atio

n Te

chno

logy

to

Impr

ove

the

Qua

lity

of

Car

dio¬

vasc

ular

Dise

ase

(CV

D) P

reve

ntio

n an

d M

anag

emen

t (A

HRQ

EQ

M G

rant

)122

Kai

ser

Foun

datio

n R

esea

rch

Inst

itute

Use

s EM

R d

ata

to d

eter

min

e th

e re

latio

nshi

p be

twee

n pa

tter

ns o

f pre

vent

ive

and

dise

ase

man

agem

ent c

are

for C

VD

and

the

occu

rren

ce o

f dise

ase

even

ts

that

this

care

is d

esig

ned

to p

reve

nt.

Res

earc

h;

Qua

lity

Impr

ovem

ent

EHR

Car

diac

Car

e

SUrv

eilla

nce,

PR

Even

tion,

and

M

anag

Emen

t of

Dia

bete

s Mel

litus

(S

UPR

EME-

DM

) (A

HRQ

ED

M G

rant

)102

Kai

ser

Perm

anen

te

Col

orad

o

A pr

ojec

t to

deve

lop

a co

mpr

ehen

sive,

long

itudi

nal c

linic

al re

gist

ry o

f a

popu

latio

n of

app

roxi

mat

ely

1.3

mill

ion

insu

red

patie

nts w

ith d

iabe

tes

mel

litus

and

a si

mila

r dat

abas

e of

all

mem

bers

with

out d

iabe

tes f

rom

11

inte

grat

ed h

ealth

car

e de

liver

y sy

stem

s. Th

e re

gist

ry w

ill c

over

the

perio

d fr

om

2005

-201

3, d

raw

from

dem

ogra

phic

and

clin

ical

dat

a el

emen

ts in

EH

Rs a

nd

othe

r sys

tem

dat

abas

es, c

aptu

re p

atie

nt-r

epor

ted

data

whe

re it

is a

lread

y be

ing

rout

inel

y co

llect

ed, a

nd c

alcu

late

and

add

dat

a on

med

icat

ion

adhe

renc

e.

Res

earc

h;

Qua

lity

Impr

ovem

ent

Dat

a R

epos

itory

; EH

R; D

ata

Agg

rega

tion/

In

tegr

atio

n;

Med

icat

ion

Man

agem

ent

Dia

bete

s

Enha

ncin

g C

linic

al

Effec

tiven

ess R

esea

rch

with

Nat

ural

Lan

guag

e Pr

oces

sing

of E

MR

(C

ER H

ub) (

AH

RQ

EDM

Gra

nt)10

2

Kai

ser

Perm

anen

te

Nor

thw

est

A pr

ojec

t to

crea

te a

nd e

valu

ate

an In

tern

et-b

ased

“C

ompa

rativ

e Eff

ectiv

enes

s R

esea

rch

Hub

” (C

ER H

ub) w

here

rese

arch

ers c

an c

olla

bora

tivel

y de

velo

p st

anda

rdiz

ed p

robl

em-s

peci

fic p

roce

ssor

s of e

lect

roni

c cl

inic

al d

ata

(with

m

edic

al c

lass

ifier

app

licat

ions

, or M

ediC

lass)

in su

ppor

t of C

ER st

udie

s. Th

e pr

ojec

t also

aim

s to

mak

e a

prov

en n

atur

al la

ngua

ge p

roce

ssin

g (N

LP)

tech

nolo

gy b

road

ly a

vaila

ble

for e

nhan

cing

use

of E

MR

dat

a.

Col

labo

rativ

e;

Res

earc

hSt

anda

rds

Cen

ter f

or A

ging

Se

rvic

es T

echn

olog

ies

(CA

ST)12

3

Lead

ingA

geA

n in

tern

atio

nal c

oalit

ion

of m

ore

than

400

tech

nolo

gy c

ompa

nies

, agi

ng

serv

ices

org

aniz

atio

ns, r

esea

rch

univ

ersit

ies,

and

gove

rnm

ent r

epre

sent

ativ

es

that

lead

the

char

ge to

exp

edite

the

deve

lopm

ent,

eval

uatio

n an

d ad

optio

n of

em

ergi

ng te

chno

logi

es th

at c

an im

prov

e th

e ag

ing

expe

rienc

e. It

s miss

ion

is to

unl

eash

the

pote

ntia

l of t

echn

olog

y fo

r inn

ovat

ive

deve

lopm

ent a

cros

s th

e co

ntin

uum

of h

ealth

car

e, h

ousin

g, a

nd se

rvic

es fo

r the

agi

ng in

ord

er to

im

prov

e an

d m

axim

ize

the

lives

of e

lder

Am

eric

ans.

Res

earc

h;

Qua

lity

Impr

ovem

ent

EHR

; Inn

ovat

ion;

Pa

tient

s/C

onsu

mer

s

Agi

ng S

ervi

ces

http://healthit.ahrq.gov/ahrq-funded-projects/feedback-treatment-intensification-data-reduce-cardiovascular-disease-risk





http://healthit.ahrq.gov/ahrq-funded-projects/using-it-improve-quality-cardiovascular-disease-cvd-prevention-and-management







http://www.edm-forum.org/publicgrant/About/projectprofiles/supremedm/






http://www.edm-forum.org/publicgrant/About/projectprofiles/cerhub/






http://www.leadingage.org/About_CAST.aspx



C-2

1

Hea

lth IT

-Ena

bled

Qua

lity

Mea

sure

men

t:

Pers

pect

ives,

Pa

thw

ays

,

an

d P

ract

ica

l G

uid

an

ce

Nam

eO

rgan

izat

ion

Des

crip

tion

Func

tion

al T

agTo

pica

l Tag

Spec

ial T

ag

Mar

kle

Com

mon

Fr

amew

ork

for P

rivat

e an

d Se

cure

Hea

lth

Info

rmat

ion

Exch

ange

124

Mar

kle

Foun

datio

nPr

ovid

es th

e in

itial

ele

men

ts o

f a c

ompr

ehen

sive

appr

oach

for s

ecur

e,

auth

oriz

ed, a

nd p

rivat

e he

alth

info

rmat

ion

shar

ing

base

d on

Fai

r In

form

atio

n Pr

actic

e Pr

inci

ples

(FIP

Ps),

so th

at p

atie

nts a

nd th

eir a

utho

rized

pr

ovid

ers c

an h

ave

acce

ss to

vita

l clin

ical

dat

a w

hen

and

whe

re n

eede

d.

Qua

lity

Impr

ovem

ent

Info

rmat

ion

Exch

ange

The

Miss

ouri

Cen

ter f

or

Prim

ary

Car

e Q

ualit

y an

d Ex

celle

nce12

5

Miss

ouri

Prim

ary

Car

e A

ssoc

iatio

n (M

PCA)

Cen

ter

for Q

ualit

y

Hou

ses M

PCA’

s tra

inin

g an

d te

chni

cal a

ssist

ance

pro

gram

s and

qua

lity

impr

ovem

ent i

nitia

tives

. Foc

uses

on

stre

ngth

enin

g in

divi

dual

s, or

gani

zatio

ns

and

com

mun

ities

by

faci

litat

ing

lear

ning

, lea

ders

hip

deve

lopm

ent,

qual

ity

enha

ncem

ent a

nd n

etw

orki

ng in

supp

ort o

f effo

rts t

o de

velo

p, im

plem

ent a

nd

sust

ain

com

mun

ity h

ealth

impr

ovem

ents

. Pro

duct

s and

serv

ices

cov

er q

ualit

y im

prov

emen

t, pe

rfor

man

ce im

prov

emen

t and

bes

t pra

ctic

es; d

ata

colle

ctio

n,

rese

arch

and

ana

lysis

; tec

hnic

al a

ssist

ance

; and

net

wor

k de

velo

pmen

t.

Trai

ning

&

Tech

nica

l A

ssist

ance

; Q

ualit

y Im

prov

emen

t; R

esea

rch

EHR

; Pat

ient

s/C

onsu

mer

sPr

imar

y C

are

Min

neso

ta H

ospi

tal

Ass

ocia

tion

Pilo

t Pr

ojec

t126

Min

neso

ta

Hos

pita

l A

ssoc

iatio

n

A pi

lot p

roje

ct sp

onso

red

by A

HRQ

in 2

007

to li

nk h

ospi

tal c

linic

al

info

rmat

ion

to a

dmin

istra

tive

data

. Des

pite

som

e in

itial

cha

lleng

es, t

his

proj

ect w

as su

cces

sful

. Res

ults

show

ed th

at th

e us

e of

hyb

rid d

ata

allo

wed

fo

r mor

e ac

cura

te c

ompa

rison

s of r

isk-a

djus

ted

mor

talit

y an

d ris

k-ad

just

ed

com

plic

atio

ns a

cros

s Min

neso

ta h

ospi

tals.

Thes

e in

crea

ses i

n ac

cura

cy

repr

esen

t an

impo

rtan

t ste

p to

war

d ta

rget

ing

qual

ity im

prov

emen

t effo

rts i

n M

inne

sota

and

pro

vide

impo

rtan

t les

sons

that

are

bei

ng le

vera

ged

thro

ugh

ongo

ing

proj

ects

to c

onst

ruct

add

ition

al e

nhan

ced

data

sets

.

Qua

lity

Impr

ovem

ent;

Res

earc

h

Inno

vatio

nA

cute

-Car

e H

ospi

tal

Nat

iona

l Cor

e In

dica

tors

(NC

I)127

Nat

iona

l A

ssoc

iatio

n of

St

ate

Dire

ctor

s of

Dev

elop

men

tal

Disa

bilit

ies

Serv

ices

(N

ASD

DD

S)

and

the

Hum

an

Serv

ices

Res

earc

h In

stitu

te (H

SRI)

A co

llabo

rativ

e eff

ort b

etw

een

NA

SDD

DS

and

HSR

I to

supp

ort.

NA

SDD

DS-

mem

ber a

genc

ies i

n ga

ther

ing

a st

anda

rd se

t of p

erfo

rman

ce a

nd

outc

ome

mea

sure

s for

trac

king

per

form

ance

, com

parin

g re

sults

acr

oss S

tate

s, an

d es

tabl

ishin

g na

tiona

l ben

chm

arks

and

serv

es a

s a v

alua

ble

sour

ce o

f in

form

atio

n on

indi

vidu

als w

ith d

evel

opm

enta

l disa

bilit

ies r

ecei

ving

serv

ices

.

Qua

lity

Impr

ovem

ent;

Publ

ic

Rep

ortin

g;

Res

earc

h

Stan

dard

s; Pa

tient

s/C

onsu

mer

s

Disa

bilit

y Se

rvic

es

Hea

lthca

re E

ffect

iven

ess

Dat

a an

d In

form

atio

n Se

t (H

EDIS

) & Q

ualit

y M

easu

rem

ent12

8

NC

QA

A to

ol d

evel

oped

by

NC

QA

and

used

by

mor

e th

an 9

0 pe

rcen

t of A

mer

ica's

he

alth

pla

ns to

mea

sure

per

form

ance

on

impo

rtan

t dim

ensio

ns o

f car

e an

d se

rvic

e in

clud

ing

mea

sure

s cov

erin

g ov

erus

e, u

nder

use,

val

ue, p

roce

ss, a

nd

outc

ome

mea

sure

s.

Tool

s/Pr

oduc

ts;

Paye

r; Pu

blic

R

epor

ting

Patie

nts/

Con

sum

ers

The

Nat

iona

l Cou

ncil

on In

depe

nden

t Liv

ing

(NC

IL) O

utco

mes

In

itiat

ive12

9

The

Nat

iona

l C

ounc

il on

In

depe

nden

t Li

ving

(NC

IL)

A na

tiona

l mem

bers

hip

orga

niza

tion

that

adv

ance

s ind

epen

dent

livi

ng a

nd

right

s of p

eopl

e w

ith d

isabi

litie

s thr

ough

con

sum

er-d

riven

adv

ocac

y. Th

ere

are

appr

oxim

atel

y 40

0 co

unci

ls on

inde

pend

ent l

ivin

g (C

ILs)

thro

ugho

ut

the

US.

Con

sum

er

Enga

gem

ent;

Qua

lity

Impr

ovem

ent

Patie

nts/

Con

sum

ers

Disa

bilit

y Se

rvic

es

http://www.markle.org/health/markle-common-framework/connecting-professionals




http://www.mo-pca.org/services/center-for-quality/



http://www.ncbi.nlm.nih.gov/pubmed/23297607



http://www.nationalcoreindicators.org/

http://www.nationalcoreindicators.org/

http://www.ncqa.org/tabid/59/Default.aspx




http://www.ncil.org/about/




C-2

2

Hea

lth IT

-Ena

bled

Qua

lity

Mea

sure

men

t:

Pers

pect

ives,

Pa

thw

ays

,

an

d P

ract

ica

l G

uid

an

ce

Nam

eO

rgan

izat

ion

Des

crip

tion

Func

tion

al T

agTo

pica

l Tag

Spec

ial T

ag

Nat

iona

l eH

ealth

C

olla

bora

tive

(NeH

C)13

0N

atio

nal e

Hea

lth

Col

la¬b

orat

ive

A pu

blic

-priv

ate

part

ners

hip

that

ena

bles

secu

re a

nd in

tero

pera

ble

natio

nwid

e he

alth

info

rmat

ion

exch

ange

to a

dvan

ce h

ealth

and

impr

ove

heal

th c

are.

C

olla

bora

tive

Info

rmat

ion

Exch

ange

Patie

nt E

ngag

emen

t Fr

amew

ork13

1N

atio

nal e

Hea

lth

Col

labo

rativ

eA

mod

el c

reat

ed to

gui

de h

ealth

car

e or

gani

zatio

ns in

dev

elop

ing

and

stre

ngth

enin

g th

eir p

atie

nt e

ngag

emen

t str

ateg

ies t

hrou

gh th

e us

e of

eH

ealth

to

ols a

nd re

sour

ces.

The

fram

ewor

k ha

s ste

ps to

incr

ease

pat

ient

eng

agem

ent

thro

ugh

the

colle

ctio

n of

pat

ient

info

rmat

ion

and

inte

grat

ion

with

ele

ctro

nic

heal

th re

cord

s. Th

is fr

amew

ork

info

rms,

empo

wer

s and

eng

aged

pat

ient

s. It

allo

ws p

atie

nt p

rovi

der p

artn

ersh

ip fo

r car

e m

anag

emen

t bot

h in

and

out

of

the

heal

th c

are

sett

ing.

Trai

ning

&

Tech

nica

l A

ssist

ance

EHR

; Pat

ient

s/C

onsu

mer

s

Elec

tron

ic Q

ualit

y M

easu

res (

eMea

sure

s)132

NQ

FSt

anda

rdiz

ed p

erfo

rman

ce m

easu

res i

n an

ele

ctro

nic

form

at th

at h

elp

ensu

re th

at m

easu

res a

re c

onsis

tent

ly d

efine

d, im

plem

ente

d, a

nd c

ompa

tible

ac

ross

clin

ical

IT sy

stem

s to

prom

ote

high

er q

ualit

y an

d m

ore

appr

opria

te

care

del

iver

y.

Tool

s/Pr

oduc

tsEH

R; S

tand

ards

eMea

sure

Lea

rnin

g C

olla

bora

tive13

3N

QF

Seek

s to

crea

te a

lear

ning

envi

ronm

ent f

or a

dvan

cing

kno

wled

ge a

nd p

rom

otin

g be

st pr

actic

es re

late

d to

dev

elopi

ng a

nd im

plem

entin

g eM

easu

res.

By b

ringi

ng

toge

ther

stak

ehol

ders

from

acr

oss t

he q

ualit

y en

terp

rise,

the C

olla

bora

tive w

ill

help

pro

mot

e mor

e effi

cien

t and

stan

dard

ized

ado

ptio

n of

eM

easu

res.

Col

labo

rativ

e;

Trai

ning

&

Tech

nica

l A

ssist

ance

EHR

; Inn

ovat

ion

Hea

lth IT

Exp

ert P

anel

(H

ITEP

-I)13

4N

QF

A pa

nel a

ssem

bled

by

NQ

F th

at id

entifi

ed 8

4 hi

gh-p

riorit

y qu

ality

mea

sure

s, th

eir a

ssoc

iate

d co

mm

on d

ata

type

s, an

d a

fram

ewor

k to

eva

luat

e th

e qu

ality

of

ele

ctro

nic

info

rmat

ion

requ

ired

by p

erfo

rman

ce m

easu

res t

hrou

gh E

HR

s. Th

e pa

nel e

xam

ined

the

info

rmat

ion

need

ed to

mea

sure

effe

ctiv

e he

alth

IT

use

in o

rder

to b

ette

r und

erst

and

how

hea

lth IT

tool

s can

impr

ove

the

effici

ency

, qua

lity,

and

safe

ty o

f hea

lth c

are

deliv

ery.

Adv

isory

Bo

dy;

Trai

ning

&

Tech

nica

l A

ssist

ance

EHR

; Pat

ient

Sa

fety

Hea

lth IT

Exp

ert P

anel

(H

ITEP

-II)13

5N

QF

Con

tinue

s the

wor

k of

HIT

EP-I

(see

abo

ve) b

y fo

cusin

g on

reco

mm

enda

tions

fo

r a st

anda

rdiz

ed Q

ualit

y D

ata

Mod

el (Q

DM

) and

mor

e m

eani

ngfu

l qu

ality

mea

sure

men

t thr

ough

impr

oved

clin

ical

dat

a flo

ws w

ithin

and

acr

oss

care

sett

ings

.

Adv

isory

Bo

dy;

Trai

ning

&

Tech

nica

l A

ssist

ance

Stan

dard

s; In

form

atio

n Ex

chan

ge

Mea

sure

App

licat

ions

Pa

rtne

rshi

p (M

AP)

136

NQ

FA

publ

ic-p

rivat

e pa

rtne

rshi

p co

nven

ed b

y th

e N

QF

to p

rovi

de in

put t

o H

HS

on th

e se

lect

ion

of p

erfo

rman

ce m

easu

res f

or p

ublic

repo

rtin

g an

d pe

rfor

man

ce-b

ased

pay

men

t pro

gram

s.

Col

labo

rativ

e;

Publ

ic

Rep

ortin

g

Paym

ent/

R

eim

burs

emen

t

http://www.nationalehealth.org/about-national-ehealth-collaborative

http://www.nationalehealth.org/about-national-ehealth-collaborative

http://www.nationalehealth.org/patient-engagement-framework


hhttp://www.qualityforum.org/Projects/e-g/eMeasures/Electronic_Quality_Measures.aspx

hhttp://www.qualityforum.org/Projects/e-g/eMeasures/Electronic_Quality_Measures.aspx



http://www.qualityforum.org/Projects/HIT_Utilization.aspx

http://www.qualityforum.org/Projects/HIT_Utilization.aspx

http://www.qualityforum.org/projects/hitep2.aspx

http://www.qualityforum.org/projects/hitep2.aspx

http://www.qualityforum.org/map/

http://www.qualityforum.org/map/

C-2

3

Hea

lth IT

-Ena

bled

Qua

lity

Mea

sure

men

t:

Pers

pect

ives,

Pa

thw

ays

,

an

d P

ract

ica

l G

uid

an

ce

Nam

eO

rgan

izat

ion

Des

crip

tion

Func

tion

al T

agTo

pica

l Tag

Spec

ial T

ag

Patie

nt R

epor

ted

Out

com

es (P

ROs)

in P

erfo

rman

ce

Mea

sure

men

t137

NQ

FA

proj

ect t

hat b

roug

ht to

geth

er a

div

erse

set o

f sta

keho

lder

s who

cou

ld

faci

litat

e th

e gr

ound

wor

k fo

r dev

elop

ing,

test

ing,

end

orsin

g, a

nd

impl

emen

ting

PRO

per

form

ance

mea

sure

s. PR

Os h

ave

been

defi

ned

as "a

ny

repo

rt o

f the

stat

us o

f a p

atie

nt's

heal

th c

ondi

tion

that

com

es d

irect

ly fr

om

the

patie

nt, w

ithou

t int

erpr

etat

ion

of th

e pa

tient

's re

spon

se b

y a

clin

icia

n or

an

yone

else

." PR

O to

ols e

nabl

e as

sess

men

t of p

atie

nt–r

epor

ted

heal

th st

atus

fo

r phy

sical

, men

tal,

and

soci

al w

ell–

bein

g.

Col

labo

rativ

e;

Res

earc

h;

Test

ing

Patie

nts/

Con

sum

ers

Qua

lity

Dat

a M

odel

(Q

DM

)138

NQ

FA

n “i

nfor

mat

ion

mod

el”

that

cle

arly

defi

nes c

once

pts u

sed

in q

ualit

y m

easu

res

and

clin

ical

car

e and

is in

tend

ed to

ena

ble a

utom

atio

n of

EH

R u

se. P

rovi

des a

w

ay to

des

crib

e clin

ical

con

cept

s in

a st

anda

rdiz

ed fo

rmat

so in

divi

dual

s (i.e

., pr

ovid

ers,

rese

arch

ers,

mea

sure

dev

elop

ers)

mon

itorin

g cl

inic

al p

erfo

rman

ce

and

outc

omes

can

cle

arly

and

con

cise

ly c

omm

unic

ate n

eces

sary

info

rmat

ion.

D

escr

ibes

info

rmat

ion

so th

at E

HR

and

oth

er c

linic

al e

lect

roni

c sy

stem

ve

ndor

s can

con

siste

ntly

inte

rpre

t and

eas

ily lo

cate

the d

ata

requ

ired.

Trai

ning

&

Tech

nica

l A

ssist

ance

EHR

; Sta

ndar

ds

Qua

lity

Posit

ioni

ng

Syst

em (Q

PS)13

9N

QF

A w

eb-b

ased

tool

des

igne

d to

hel

p ea

sily

find

and

sele

ct N

QF-

endo

rsed

® m

easu

res.

QPS

Ver

sion

1.0

incl

udes

new

feat

ures

that

pro

vide

the

mos

t up

-to-d

ate

info

rmat

ion

abou

t NQ

F-en

dors

ed m

easu

res.

Tool

s/Pr

oduc

tsD

ata

Rep

osito

ry

Impr

ovin

g Q

ualit

y in

C

ance

r Scr

eeni

ng: Th

e Ex

celle

nce

Rep

ort f

or

Col

onos

copy

: (A

HRQ

EQ

M G

rant

)140

Ore

gon

Hea

lth

and

Scie

nce

Uni

vers

ity

Usin

g th

e C

linic

al O

utco

mes

Res

earc

h In

itiat

ive

(CO

RI)

soft

war

e ap

plic

atio

n an

d th

e N

atio

nal E

ndos

copi

c D

atab

ase,

the

proj

ect d

evel

oped

an

d te

sted

the

Exce

llenc

e R

epor

t—a

qual

ity re

port

car

d fo

r gas

troi

ntes

tinal

en

dosc

opy

that

focu

ses o

n na

tiona

lly re

cogn

ized

qua

lity

proc

ess m

easu

res f

or

colo

nosc

opy.

Test

ing;

R

esea

rch

Patie

nt S

afet

yA

mbu

lato

ry

Car

e; C

ance

r

Phar

mac

y eH

ealth

In

form

atio

n Te

chno

logy

C

olla

bora

tive14

1

Phar

mac

y eH

ealth

In

form

atio

n Te

chno

logy

C

olla

bora

tive

A na

tiona

l pro

fess

iona

l ass

ocia

tion

of p

harm

acist

s and

oth

er h

ealth

car

e pr

actit

ione

rs w

ho se

rve

soci

ety

by th

e ap

plic

atio

n of

soun

d m

edic

atio

n m

anag

emen

t prin

cipl

es a

nd st

rate

gies

to im

prov

e he

alth

car

e fo

r all.

The

Aca

dem

y's 5

,700

mem

bers

dev

elop

and

pro

vide

a d

iver

sified

rang

e of

cl

inic

al, e

duca

tiona

l, an

d bu

sines

s man

agem

ent s

ervi

ces a

nd st

rate

gies

on

beha

lf of

the

mor

e th

an 2

00 m

illio

n A

mer

ican

s cov

ered

by

a m

anag

ed c

are

phar

mac

y be

nefit

Col

labo

rativ

eM

edic

atio

n M

anag

emen

tPh

arm

acy

CM

S/Pr

emie

r Hos

pita

l Q

ualit

y In

cent

ive

Dem

onst

ratio

n™

(HQ

ID)14

2

Prem

ier

A va

lue-

base

d pu

rcha

sing

proj

ect t

hat t

rack

ed a

nd m

easu

red

evid

ence

-bas

ed

care

rate

s to

raise

ove

rall

qual

ity b

y an

ave

rage

of 1

8.6%

in fi

ve c

linic

al a

reas

ov

er th

e co

urse

of s

ix y

ears

.

Res

earc

hPa

ymen

t/

Rei

mbu

rsem

ent

The

Hea

lthca

re

Info

rmat

ion

Tech

nolo

gy

Col

labo

rativ

e143

Prem

ier

A co

llabo

rativ

e co

nsist

ing

of m

ore

than

200

hea

lth sy

stem

s and

hos

pita

l chi

ef

info

rmat

ion

office

rs a

nd m

ore

than

300

tota

l par

ticip

ants

that

shar

e le

gal

and

regu

lato

ry e

xper

tise

and

best

pra

ctic

es o

n ac

hiev

ing

mea

ning

ful u

se o

f he

alth

IT.

Col

labo

rativ

eM

eani

ngfu

l Use

Acu

te-C

are

Hos

pita

l

http://www.qualityforum.org/Publications/2012/12/Patient-Reported_Outcomes_in_Performance_Measurement.aspx






http://www.qualityforum.org/Learn_More/About_QPS.aspx

http://www.qualityforum.org/Learn_More/About_QPS.aspx

http://healthit.ahrq.gov/ahrq-funded-projects/improving-quality-cancer-screening-excellence-report-colonoscopy





http://www.pharmacyhit.org/



https://www.premierinc.com/p4p/hqi/index.jsp




https://www.premierinc.com/wps/portal/premierinc/public/transforminghealthcare/collaboratingforresults/hit/%21ut/p/b1/04_Sj9CPykssy0xPLMnMz0vMAfGjzOJNzQzMnJwMHQ3cvQwsDDxDzP1MnX18DU0MjIEKInErMPA1JU6_AQ7gaEBIv5d-VGZSrl55cq6egZ6xoYGlsbGpmaUJkLI0NjHTD9ePwmeCRYgpAQU-xvgVgL0IVoDHD34e-bmp-gW5oRGVwZ5ZJo6KigBXMxew/dl4/d5/L2dBISEvZ0FBIS9nQSEh/



C-2

4

Hea

lth IT

-Ena

bled

Qua

lity

Mea

sure

men

t:

Pers

pect

ives,

Pa

thw

ays

,

an

d P

ract

ica

l G

uid

an

ce

Nam

eO

rgan

izat

ion

Des

crip

tion

Func

tion

al T

agTo

pica

l Tag

Spec

ial T

ag

The

Part

ners

hip

for

Car

e Tr

ansfo

rmat

ion™

(P

ACT

) Col

labo

rativ

e144

Prem

ier

A co

llabo

rativ

e in

clud

ing

hosp

itals

and

heal

th sy

stem

s tha

t are

bui

ldin

g he

alth

IT in

fras

truc

ture

that

will

be

need

ed to

fost

er g

reat

er p

rovi

der

acco

unta

bilit

y, in

clud

ing

pred

ictiv

e m

odel

ing,

cro

ss-c

ontin

uum

cla

ims a

nd

clin

ical

info

rmat

ion,

and

tran

spar

ent i

nfor

mat

ion

exch

ange

.

Hea

lth

Info

rmat

ion

Exch

ange

; C

olla

bora

tive

Car

e C

oord

inat

ion;

C

are

Tran

sform

atio

n;

Info

rmat

ion

Exch

ange

Acu

te-C

are

Hos

pita

l

QU

EST:

Hig

h Pe

rfor

min

g H

ospi

tals

prog

ram

145

Prem

ier

A pr

ogra

m th

at a

utom

atic

ally

iden

tifies

har

m a

nd c

ompl

icat

ions

, tra

cks

clin

ical

qua

lity

mea

sure

s and

pro

vide

s ind

ustr

y be

nchm

arks

.Q

ualit

y Im

prov

emen

tPa

tient

Saf

ety

Acu

te-C

are

Hos

pita

l

Safe

tyA

dviso

r/O

ccur

renc

e R

epor

t M

anag

emen

t (O

RM

) Sy

stem

146

Prem

ier/

Qua

ntro

sA

n en

terp

rise-

wid

e, w

eb-b

ased

syst

em th

at a

utom

ates

inci

dent

col

lect

ion,

re

view

, fol

low

-up,

and

regu

lato

ry re

port

ing

for h

ealth

car

e or

gani

zatio

ns

in re

al-t

ime.

The

tool

was

dev

elop

ed in

resp

onse

to re

ques

ts fr

om h

ospi

tal

risk

man

agem

ent a

nd q

ualit

y ex

ecut

ives

and

thei

r re-

insu

rers

who

saw

that

pa

per-b

ased

inci

dent

dat

a co

llect

ion,

revi

ew, a

nd fo

llow

-up

was

inco

mpl

ete,

slo

w, a

nd d

id n

ot p

rovi

de th

e st

ruct

ured

fram

ewor

k re

quire

d to

impr

ove

patie

nt sa

fety

, red

uce

risk,

and

put

in p

lace

wor

kflow

cha

nges

to p

reve

nt

adve

rse

inci

dent

s bef

ore

they

occ

ur.

Tool

s/Pr

oduc

tsPa

tient

Saf

ety

Acu

te-C

are

Hos

pita

l

Sout

h C

arol

ina

Hea

lthca

re Q

ualit

y Tr

ust (

SCH

QT

)147

Prem

ier/

Hea

lth

Scie

nces

Sou

th

Car

olin

a/So

uth

Car

olin

a H

ospi

tal

Ass

ocia

tion

A vo

lunt

ary

hosp

ital p

artn

ersh

ip a

nd c

olla

bora

tive

appr

oach

that

leve

rage

s Pr

emie

r dat

a as

sets

to im

prov

e pe

rfor

man

ce a

nd e

limin

ate

prev

enta

ble

hosp

ital a

cqui

red

infe

ctio

ns (H

AIs

).

Col

labo

rativ

ePa

tient

Saf

ety;

H

ospi

tal A

cqui

red

Infe

ctio

ns

Acu

te-C

are

Hos

pita

l

Puge

t Sou

nd H

ealth

A

llian

ce14

8Pu

get S

ound

H

ealth

Alli

ance

A no

npro

fit o

rgan

izat

ion

cons

istin

g of

mor

e tha

n 15

0 le

adin

g st

ate a

nd c

ount

y em

ploy

ers a

nd u

nion

trus

ts, h

ealth

pla

ns, h

ospi

tals

and

phys

icia

n gr

oups

, go

vern

men

t age

ncie

s, ed

ucat

iona

l ins

titut

ions

, pha

rmac

eutic

al c

ompa

nies

an

d in

divi

dual

s aim

ed to

impr

ove

heal

th c

are

qual

ity a

nd a

fford

abili

ty

in a

five

-cou

nty

regi

on: K

ing,

Kits

ap, P

ierc

e, S

noho

mish

and

Thur

ston

Cou

ntie

s. Th

e ar

eas o

f foc

us a

re p

erfo

rman

ce m

easu

rem

ent,

publ

ic re

port

ing,

pe

rfor

man

ce im

prov

emen

t, co

nsum

er e

ngag

emen

t and

pay

men

t ref

orm

.

Publ

ic

Rep

ortin

g;

Con

sum

er

Enga

gem

ent

Patie

nts/

Con

sum

ers;

Paym

ent/

R

eim

burs

emen

t

SF36

v2 H

ealth

Sur

vey14

9Q

ualit

yMet

ricA

36-q

uest

ion

heal

th su

rvey

to m

easu

re p

hysic

al a

nd m

enta

l hea

lth,

func

tiona

l sta

tus,

and

wel

l-bei

ng fr

om th

e pa

tient

's po

int o

f vie

w in

a

prac

ticab

le, r

elia

ble

and

valid

man

ner.

Prov

ides

scor

es fo

r eac

h of

the

eigh

t he

alth

dom

ains

, and

psy

chom

etric

ally

-bas

ed p

hysic

al c

ompo

nent

sum

mar

y (P

CS)

and

men

tal c

ompo

nent

sum

mar

y (M

CS)

scor

es.

Con

sum

er

Enga

gem

ent

Patie

nts/

C

onsu

mer

s

https://www.premierinc.com/quality-safety/tools-services/ACO/index.jsp



https://www.premierinc.com/about/news/12-jan/quest011812.jsp



https://www.premierinc.com/advisorlive/2005/program030905/quantrossafetyadvisor0105.pdf




http://www.healthsciencessc.org/trust.asp



http://www.pugetsoundhealthalliance.org/about/index.html

http://www.pugetsoundhealthalliance.org/about/index.html

http://www.qualitymetric.com/WhatWeDo/SFHealthSurveys/SF36v2HealthSurvey/tabid/185/Default.aspx

C-2

5

Hea

lth IT

-Ena

bled

Qua

lity

Mea

sure

men

t:

Pers

pect

ives,

Pa

thw

ays

,

an

d P

ract

ica

l G

uid

an

ce

Nam

eO

rgan

izat

ion

Des

crip

tion

Func

tion

al T

agTo

pica

l Tag

Spec

ial T

ag

Nat

iona

l Pat

ient

Saf

ety

Foun

datio

n G

rant

to

Res

earc

h Sa

fety

of

Com

pute

rized

Ord

erin

g of

Med

icat

ions

1150

Qua

ntro

s/Br

igha

m a

nd

Wom

en’s

Hos

pita

l and

H

arva

rd M

edic

al

Scho

ol

A ye

ar-lo

ng p

roje

ct w

here

Qua

ntro

s par

tner

ed w

ith a

cute

car

e ho

spita

ls w

ho h

ad im

plem

ente

d C

POE.

Hos

pita

ls tr

ansm

itted

med

icat

ion

data

to Q

uant

ros,

whi

ch w

as th

en, p

arse

d th

roug

h qu

ality

mea

sure

s al

gorit

hms;

met

rics w

ere

calc

ulat

ed a

bout

app

ropr

iate

med

icat

ion

use

and

disc

ontin

uatio

n. In

add

ition

to th

e ho

pes o

f im

prov

ing

qual

ity, t

he p

roje

ct

hope

d to

lead

hos

pita

ls in

mak

ing

use

of th

eir h

ealth

IT to

trac

k qu

ality

pe

rfor

man

ce in

a m

uch

mor

e effi

cien

t man

ner.

Gra

nt

Prog

ram

; R

esea

rch

Med

icat

ion

Man

agem

ent;

Patie

nt S

afet

y

Acu

te-C

are

Hos

pita

l

Mas

sach

uset

ts Q

ualit

y E-

Mea

sure

Val

idat

ion

Stud

y (A

HRQ

EQ

M

Gra

nt)15

1

RA

ND

C

orpo

ratio

nEv

alua

tes t

he re

adin

ess o

f str

uctu

red

EHR

dat

a to

supp

ort a

mbu

lato

ry

clin

ical

qua

lity

mea

sure

men

t. U

sing

the

Am

bula

tory

Car

e Q

ualit

y A

llian

ce

(AQ

A) a

mbu

lato

ry c

are

mea

sure

men

t set

, the

stud

y te

am is

com

parin

g qu

ality

mea

sure

s by

appl

ying

two

stan

dard

mea

sure

men

t met

hods

: (1)

a

“hyb

rid m

etho

d,”

com

bini

ng c

laim

s dat

a w

ith m

edic

al re

cord

revi

ew a

nd

(2) a

“cla

ims-

only

met

hod,

” ba

sed

upon

cla

ims d

ata

aggr

egat

ed a

cros

s co

mm

erci

al h

ealth

pla

ns a

nd th

e M

edic

are

prog

ram

.

Res

earc

h;

Qua

lity

Impr

ovem

ent

EHR

; Usin

g C

laim

s D

ata;

Dat

a A

ggre

gatio

n/

Inte

grat

ion

Am

bula

tory

C

are;

M

edic

are

Indi

ana

PRO

SPEC

T

(AH

RQ E

DM

Gra

nt)10

2R

egen

strie

f In

stitu

teA

reso

urce

use

d fo

r a v

arie

ty o

f clin

ical

out

com

es a

nd c

linic

al e

ffect

iven

ess

rese

arch

. It b

uild

s on

the

Indi

ana

Net

wor

k fo

r Pat

ient

Car

e (I

NPC

) and

will

en

hanc

e th

e in

fras

truc

ture

to c

aptu

re ri

cher

clin

ical

dat

a an

d en

hanc

e th

e ab

ility

to in

corp

orat

e pa

tient

out

com

es, i

mpr

ove

capt

ure

of d

evic

e ut

iliza

tion

data

, inc

orpo

rate

gen

omic

and

oth

er h

igh

thro

ughp

ut re

sults

, and

iden

tify

patie

nt fo

r rec

ruitm

ent a

nd e

nrol

lmen

t tec

hnol

ogie

s.

Dat

a R

epos

itory

; R

esea

rch

Patie

nts/

Con

sum

ers

Con

sum

er-P

urch

aser

D

isclo

sure

Pro

ject

(C

PDP)

152

Rob

ert W

ood

John

son

Foun

datio

n

A co

aliti

on o

f lea

ding

nat

iona

l and

loca

l con

sum

er o

rgan

izat

ions

, em

ploy

ers,

and

labo

r org

aniz

atio

ns d

edic

ated

to im

prov

ing

the

qual

ity a

nd a

fford

abili

ty

of h

ealth

car

e in

Am

eric

a fo

r con

sum

ers a

nd h

ealth

car

e pu

rcha

sers

. The

proj

ect’s

miss

ion

is to

shar

e us

eful

info

rmat

ion

abou

t pro

vide

r per

form

ance

to

pat

ient

s so

they

can

mak

e in

form

ed c

hoic

es, t

o pu

rcha

sers

so th

ey c

an

bett

er re

war

d th

e be

st p

erfo

rmin

g pr

ovid

ers,

and

to p

rovi

ders

so th

ey c

an

impr

ove

the

qual

ity o

f the

car

e th

ey p

rovi

de.

Gra

nt

Prog

ram

Patie

nt

Safe

ty; C

are

Tran

sform

atio

n;

Dec

ision

mak

ing

Inte

ract

ive

Prev

entiv

e H

ealth

Rec

ord

(IPH

R)

inte

grat

ed w

ith

Elec

tron

ic H

ealth

R

ecor

d153

RTI I

nter

natio

nal;

Virg

inia

A

mbu

lato

ry

Car

e Out

com

es

Res

earc

h N

etw

ork

(AC

OR

N);

Virg

inia

C

omm

onw

ealth

U

nive

rsity

D

epar

tmen

t of

Fam

ily M

edic

ine

An

inte

ract

ive

prev

entiv

e he

alth

reco

rd u

sed

by p

atie

nts f

rom

14

med

ical

pr

actic

es in

Virg

inia

to le

arn

abou

t the

ir pr

even

tive

heal

th n

eeds

and

acc

ess

rela

ted

educ

atio

nal i

nfor

mat

ion

and

tool

s. Pa

tient

s sig

n on

to th

e sy

stem

be

fore

eac

h vi

sit to

vie

w h

ealth

info

rmat

ion

draw

n fr

om th

e pr

actic

e’s E

HR

an

d co

mpl

ete

a he

alth

risk

ass

essm

ent.

The

syst

em th

en g

ener

ates

a su

mm

ary

page

that

list

s the

dat

es o

f pas

t pre

vent

ive

serv

ices

and

ass

ocia

ted

test

resu

lts,

high

light

s miss

ing

info

rmat

ion,

and

mak

es e

vide

nce-

base

d re

com

men

datio

ns

on n

eede

d pr

even

tive

serv

ices

, thu

s allo

win

g th

e pa

tient

to d

iscus

s the

se

need

s with

the

doct

or a

t the

upc

omin

g vi

sit. Th

e pa

tient

’s in

form

atio

n is

auto

mat

ical

ly tr

ansm

itted

bac

k to

the

EHR

, mak

ing

it ea

sy fo

r clin

icia

ns to

id

entif

y an

d di

scus

s the

pat

ient

's pr

even

tive

care

nee

ds d

urin

g th

e vi

sit.

Tool

s/Pr

oduc

ts;

Con

sum

er

Enga

gem

ent

Patie

nts/

Con

sum

ers;

EHR

; Dat

a A

ggre

gatio

n/

Inte

grat

ion

Prim

ary

Car

e;

Am

bula

tory

C

are

http://quantros.com/resources/news/national-patient-safety-foundation-awards-grant-to-research-safety-of-computerized-ordering-of-medications





http://healthit.ahrq.gov/ahrq-funded-projects/massachusetts-quality-e-measure-validation-study




http://www.edm-forum.org/publicgrant/About/projectprofiles/indiana/

http://www.edm-forum.org/publicgrant/About/projectprofiles/indiana/

http://www.rwjf.org/en/grants/grantees/the-consumer-purchaser-disclosure-project.html



http://www.innovations.ahrq.gov/content.aspx%3Fid%3D3743%26utm_source%3Dissueanc%26utm_medium%3Demail%26utm_campaign%3D20121205





C-2

6

Hea

lth IT

-Ena

bled

Qua

lity

Mea

sure

men

t:

Pers

pect

ives,

Pa

thw

ays

,

an

d P

ract

ica

l G

uid

an

ce

Nam

eO

rgan

izat

ion

Des

crip

tion

Func

tion

al T

agTo

pica

l Tag

Spec

ial T

ag

Cen

ter f

or T

echn

olog

y an

d A

ging

(CTA

)154

The

Scan

Fo

unda

tion

A na

tiona

l lea

der i

n th

e us

e of

pat

ient

-cen

tere

d te

chno

logi

es fo

r old

er a

dults

th

at w

orks

with

hea

lth c

are

prov

ider

s, ag

ing

serv

ice

orga

niza

tions

, pay

ers,

foun

datio

ns a

nd te

chno

logy

com

pani

es to

ena

ble

them

to a

ccel

erat

e th

e de

ploy

men

t of p

rove

n te

chno

logi

es.

Gra

nt

Prog

ram

; Q

ualit

y Im

prov

emen

t

Patie

nts/

Con

sum

ers;

EHR

Agi

ng S

ervi

ces

Shar

ed C

are

Plan

155

Shar

ed C

are

Plan

A fr

ee, e

asy-

to-u

se re

cord

that

lets

pat

ient

s org

aniz

e an

d st

ore

vita

l hea

lth

info

rmat

ion.

It is

also

a se

lf-m

anag

emen

t car

e pl

an th

at h

elps

impr

ove

patie

nts’

unde

rsta

ndin

g of

thei

r hea

lth a

nd h

elps

them

man

age

chro

nic

cond

ition

s, co

ordi

nate

the

care

of o

ther

s, an

d im

prov

e he

alth

.

Tool

/Pro

duct

Patie

nts/

Con

sum

ers

Com

para

tive

Out

com

es

Man

agem

ent w

ith

Elec

tron

ic D

ata

Tech

nolo

gies

(CO

MET

) (A

HRQ

ED

M G

rant

)102

Stan

ford

Sle

ep

Med

icin

e C

ente

rA

proj

ect t

o de

velo

p a

new

ele

ctro

nic

data

net

wor

k in

fras

truc

ture

that

will

pr

ospe

ctiv

ely

enab

le ac

cess

to a

nd th

e sh

arin

g of

clin

ical

and

rese

arch

dat

a be

twee

n fo

ur C

linic

al C

ente

rs (C

Cs).

The

deve

lopm

ent o

f thi

s new

, bro

ad,

scal

able

, and

sust

aina

ble

elec

tron

ic d

ata

netw

ork

will

ena

ble

the

colle

ctio

n of

lo

ngitu

dina

l dat

a co

nten

t (bo

th d

igita

l and

phy

sical

) by

each

of t

he fo

ur C

Cs.

Tool

s/Pr

oduc

tsIn

form

atio

n Ex

chan

ge

Hud

son

Valle

y In

itiat

ive15

6Ta

coni

c IP

A,

Taco

nic

Hea

lth

Info

rmat

ion

Net

wor

k an

d C

omm

unity

(T

HIN

C),

Med

Alli

es

Rev

olut

ioni

zes h

ealth

car

e de

liver

y th

roug

h th

e w

ork

of th

ree

orga

niza

tions

th

at sh

are

a co

mm

on v

ision

whi

ch is

to le

vera

ge h

ealth

info

rmat

ion

tech

nolo

gy, p

hysic

ian

prac

tice

tran

sform

atio

n an

d va

lue-

base

d pu

rcha

sing

in

the

purs

uit o

f car

e de

liver

y th

at is

pat

ient

-cen

tere

d, c

oord

inat

ed, a

cces

sible

, hi

gh q

ualit

y, an

d effi

cien

tly d

eliv

ered

thro

ugh

sust

aina

ble

finan

cial

mod

els.

Hea

lth

Info

rmat

ion

Exch

ange

Dat

a A

ggre

gatio

n/

Inte

grat

ion;

In

form

atio

n Ex

chan

ge

Qua

lityM

etric

157

The

Uni

tedH

ealth

G

roup

A gr

oup

that

wor

ks w

ith b

oth

larg

e an

d sm

all h

ealth

car

e or

gani

zatio

ns to

m

easu

re a

nd b

ette

r und

erst

and

heal

th o

utco

mes

. Qua

lityM

etric

wor

ked

with

Spe

cial

ty G

roup

, a la

rge

spec

ialty

hea

lth c

are

man

ager

for b

ehav

iora

l he

alth

, dia

gnos

tic im

agin

g, a

nd sp

ecia

lty p

harm

aceu

tical

who

se p

rimar

y cu

stom

ers a

re U

S he

alth

insu

rers

, gov

ernm

ent a

genc

ies,

and

othe

r pay

ers,

to d

evel

op a

syst

em th

at u

ses s

tand

ardi

zed

and

scie

ntifi

cally

val

id m

easu

res.

Qua

lity

Met

ric a

lso p

artn

ered

with

Can

adia

n H

ealth

Aut

horit

y to

im

plem

ent a

pop

ulat

ion

heal

th in

itiat

ive

that

mon

itors

hea

lth o

utco

mes

in

patie

nts e

nrol

led

in c

omm

unity

bas

ed d

iabe

tes a

nd o

besit

y m

anag

emen

t and

ed

ucat

ion

prog

ram

s.

Qua

lity

Impr

ovem

ent;

Paye

r; Pu

blic

H

ealth

EHR

; Pay

men

t/

Rei

mbu

rsem

ent

Dia

bete

s; Be

havi

oral

H

ealth

; Ph

arm

acy

The

Patie

nt C

ost

Estim

ator

(myH

CE)

158

The

Uni

tedH

ealth

G

roup

An

inte

grat

ed o

nlin

e se

rvic

e th

at h

elps

con

sum

ers fi

nd q

ualit

y ca

re w

hile

be

ing

able

to e

stim

ate

the

cost

of m

ore

than

100

com

mon

trea

tmen

ts a

nd

proc

edur

es. E

stim

ates

are

also

per

sona

lized

to re

flect

an

indi

vidu

al's

own

heal

th p

lan

bene

fits,

incl

udin

g re

al ti

me

acco

unt b

alan

ces.

Tool

s/Pr

oduc

ts;

Paye

r

Patie

nts/

Con

sum

ers;

Dec

ision

mak

ing

http://www.techandaging.org/index.html

http://www.techandaging.org/index.html

https://www.sharedcareplan.org/HomePage.aspx

http://www.edm-forum.org/publicgrant/About/projectprofiles/comet/





http://www.hudsonvalleyinitiative.com/about.html

http://www.hudsonvalleyinitiative.com/about.html

http://www.qualitymetric.com/WhoWeAre/Mission/tabid/151/Default.aspx

https://www.unitedhealthcareonline.com/b2c/CmaAction.do%3FchannelId%3D81039188e449a310VgnVCM100000294ab10a

https://www.unitedhealthcareonline.com/b2c/CmaAction.do%3FchannelId%3D81039188e449a310VgnVCM100000294ab10a

C-2

7

Hea

lth IT

-Ena

bled

Qua

lity

Mea

sure

men

t:

Pers

pect

ives,

Pa

thw

ays

,

an

d P

ract

ica

l G

uid

an

ce

Nam

eO

rgan

izat

ion

Des

crip

tion

Func

tion

al T

agTo

pica

l Tag

Spec

ial T

ag

The

Prem

ium

D

esig

natio

n Pr

ogra

m15

9Th

e U

nite

dHea

lth

Gro

up

Rec

ogni

zes s

peci

alty

and

prim

ary

care

phy

sicia

ns a

nd h

ospi

tals

that

pro

vide

ca

rdia

c se

rvic

es th

at h

ave

been

ass

esse

d as

mee

ting

or e

xcee

ding

cer

tain

ev

iden

ce a

nd c

onse

nsus

bas

ed q

ualit

y an

d effi

cien

tly c

riter

ia. E

valu

ates

ho

spita

ls an

d do

ctor

s in

21 sp

ecia

lties

, inc

ludi

ng p

rimar

y ca

re, i

nter

nal

med

icin

e, c

ardi

olog

y, or

thop

edic

surg

ery,

OB/

GY

N, p

edia

tric

s, ba

sed

on

indu

stry

stan

dard

s, ev

iden

ce-b

ased

and

med

ical

soci

ety

stan

dard

s and

gu

idel

ines

from

med

ical

org

aniz

atio

ns a

nd g

over

nmen

tal a

genc

ies (

e.g.

, AC

QA

, NC

QA

, and

the

Am

eric

an C

olle

ge o

f Car

diol

ogy)

.

Qua

lity

Impr

ovem

ent;

Paye

r; In

cent

ive

Prog

ram

Paym

ent/

R

eim

burs

emen

tC

ardi

ac C

are;

Pr

imar

y C

are;

A

mbu

lato

ry

Car

e

Vie

w36

0TM

160

The

Uni

tedH

ealth

G

roup

Prov

ides

evi

denc

e-ba

sed

med

ical

info

rmat

ion

to c

ontr

acte

d ph

ysic

ians

via

an

onl

ine

port

al to

hel

p id

entif

y pa

tient

s who

may

be

cand

idat

es fo

r spe

cific

tr

eatm

ents

or s

cree

ning

s, co

nsist

ent w

ith n

atio

nal q

ualit

y gu

idel

ines

.

Qua

lity

Impr

ovem

ent;

Paye

r

Dec

ision

mak

ing

Scal

able

Arc

hite

ctur

e fo

r Fe

dera

ted

Tran

slatio

nal

Inqu

iries

Net

wor

k (S

AFT

INet

) (A

HRQ

ED

M G

rant

)102

Uni

vers

ity o

f C

olor

ado

Den

ver

A re

sear

ch p

roje

ct to

enh

ance

the

capa

city

and

cap

abili

ty o

f a sa

fety

ne

t-foc

used

dist

ribut

ed re

sear

ch n

etw

ork

to c

ondu

ct p

rosp

ectiv

e co

mpa

rativ

e eff

ectiv

enes

s res

earc

h (C

ER) v

ia a

mul

ti-se

ttin

g, m

ulti-

stat

e or

gani

zatio

n.

Will

fede

rate

geo

grap

hica

lly d

isper

sed

safe

ty n

et e

ntiti

es th

at c

olle

ctiv

ely

serv

e m

arke

dly

dive

rse

unde

rser

ved

popu

latio

ns a

nd w

ill b

oth

leve

rage

an

d ex

tend

the

esta

blish

ed g

over

nanc

e an

d te

chno

logi

c ca

pabi

litie

s of t

he

Dist

ribut

ed A

mbu

lato

ry R

esea

rch

in Th

erap

eutic

s Net

wor

k (D

AR

TN

et) t

o al

low

mor

e fle

xibl

e op

tions

for p

artic

ipan

ts a

nd im

prov

ed g

rid te

chno

logy

. Th

is m

ulti-

stat

e pr

ojec

t will

allo

w re

sear

cher

s, he

alth

pol

icy

expe

rts,

paye

rs,

and

clin

icia

ns to

bet

ter u

nder

stan

d th

e im

pact

of a

wid

e va

riety

of h

ealth

ca

re in

terv

entio

ns o

n he

alth

out

com

es fo

r min

ority

, und

erse

rved

and

so

cioe

cono

mic

ally

disa

dvan

tage

pop

ulat

ions

by

supp

ortin

g C

ER.

Res

earc

h;

Qua

lity

Impr

ovem

ent

Info

rmat

ion

Exch

ange

Und

erse

rved

Po

pula

tions

Cro

ssin

g th

e Q

ualit

y A

sses

smen

t Cha

sm:

Alig

ning

Mea

sure

d an

d Tr

ue Q

ualit

y of

Car

e (A

HRQ

EQ

M G

rant

)161

Uni

vers

ity o

f Pe

nnsy

lvan

iaLe

vera

ges d

etai

led

and

disc

rete

dat

a fr

om e

lect

roni

c m

edic

al re

cord

s to

dev

elop

mea

sure

s tha

t acc

ount

for h

eter

ogen

eity

am

ong

diffe

rent

di

abet

ic p

atie

nt p

anel

s, cr

edit

impr

ovem

ent i

n th

e co

ntro

l of d

iabe

tes

amon

g in

divi

dual

s in

a gi

ven

popu

latio

n ov

er ti

me,

reco

gniz

e pr

ovid

er

effor

t in

med

ical

man

agem

ent,

and

inco

rpor

ate

man

agem

ent o

f dia

bete

s co

mor

bidi

ties,

such

as h

igh

bloo

d pr

essu

re a

nd h

yper

lipid

emia

.

Qua

lity

Impr

ovem

ent;

Res

earc

h

EHR

D

iabe

tes

http://www.uhc.com/physicians/care_programs/unitedhealth_premium_designation.htm

http://www.uhc.com/physicians/care_programs/unitedhealth_premium_designation.htm

https://www.unitedhealthcareonline.com/b2c/CmaAction.do%3FchannelId%3D5bae2db818b7a210VgnVCM1000002f10b10a

http://www.edm-forum.org/publicgrant/About/projectprofiles/saftinet/





http://healthit.ahrq.gov/ahrq-funded-projects/crossing-quality-assessment-chasm-aligning-measured-and-true-quality-care





C-2

8

Hea

lth IT

-Ena

bled

Qua

lity

Mea

sure

men

t:

Pers

pect

ives,

Pa

thw

ays

,

an

d P

ract

ica

l G

uid

an

ce

Nam

eO

rgan

izat

ion

Des

crip

tion

Func

tion

al T

agTo

pica

l Tag

Spec

ial T

ag

Scal

able

Nat

iona

l N

etw

ork

for

Effec

tiven

ess R

esea

rch

(SC

AN

NER

) (A

HRQ

ED

M G

rant

)102

Uni

vers

ity

of S

outh

ern

Cal

iforn

ia

A se

cure

, sca

labl

e, d

istrib

uted

infr

astr

uctu

re th

at fa

cilit

ates

col

labo

rativ

e co

mpa

rativ

e eff

ectiv

enes

s res

earc

h (C

ER) a

mon

g w

idel

y di

sper

sed

inst

itutio

ns a

nd p

rovi

des fl

exib

ility

to p

artic

ipan

t site

s as a

mea

ns fo

r dat

a sh

arin

g. Th

is fle

xibi

lity

will

be

impl

emen

ted

by a

llow

ing

codi

ficat

ion

of d

ata

shar

ing

polic

ies—

each

inst

itutio

n w

ill sp

ecify

its o

wn

polic

ies.

SCA

NN

ER

will

con

nect

div

erse

hea

lth c

are

deliv

ery

sett

ings

with

secu

re in

fras

truc

ture

th

at u

tiliz

es d

ata

colle

cted

at t

he p

oint

of c

are.

The

netw

ork

will

hav

e a

mai

n no

de th

at m

anag

es p

olic

ies,

dist

ribut

es q

uerie

s, ag

greg

ates

resu

lts, a

nd

mai

ntai

ns tr

ust a

nd se

curit

y (a

uthe

ntic

atio

n, a

utho

rizat

ion,

aud

iting

, etc

.).

Each

site

will

mai

ntai

n a

node

that

con

tain

s dat

a fr

om th

at si

te.

Tool

s/Pr

oduc

ts;

Res

earc

h

Dat

a A

ggre

gatio

n/

Inte

grat

ion;

In

form

atio

n Ex

chan

ge

Usin

g El

ectr

onic

R

ecor

ds to

Det

ect a

nd

Lear

n Fr

om A

mbu

lato

ry

Dia

gnos

tic E

rror

s (A

HRQ

EQ

M G

rant

)162

Uni

vers

ity o

f Te

xas H

ealth

Sc

ienc

e C

ente

r H

oust

on

Use

s dat

a fr

om V

eter

ans A

ffairs

’ (VA

) EH

Rs a

nd a

non

-VA

prim

ary

care

ne

twor

k to

det

ect d

iagn

ostic

err

ors a

nd u

nder

stan

d th

eir c

ause

s. La

ys th

e gr

ound

wor

k fo

r fut

ure

prev

entio

n st

rate

gies

.

Res

earc

hEH

RVe

tera

ns;

Am

bula

tory

C

are;

Prim

ary

Car

e

Surg

ical

Car

e an

d O

utco

mes

Ass

essm

ent

Prog

ram

CER

Tr

ansla

tion

Net

wor

k (S

CO

AP

CER

TAIN

) (A

HRQ

ED

M G

rant

)102

Uni

vers

ity o

f W

ashi

ngto

n M

edic

ine

A le

arni

ng h

ealth

car

e sy

stem

focu

sed

on im

prov

ing

the

effec

tiven

ess,

qual

ity, a

nd sa

fety

of s

urgi

cal a

nd in

terv

entio

nal c

are.

Lev

erag

es th

e su

cces

s of t

he S

CO

AP

regi

stry

to im

prov

e ex

istin

g us

e of

dat

a fo

r qua

lity

impr

ovem

ent (

QI)

and

to e

xten

d th

e re

gist

ry in

to c

ompa

rativ

e eff

ectiv

enes

s re

sear

ch (C

ER).

Inco

rpor

ates

pat

ient

voi

ces,

stak

ehol

der p

ersp

ectiv

es, a

nd

real

wor

ld c

linic

al p

ract

ice

to a

ddre

ss q

uest

ions

of c

ompa

rativ

e be

nefit

and

co

sts f

or c

linic

ians

, pat

ient

s, pa

yers

, ind

ustr

y, an

d po

licym

aker

s. Bu

ilds

on a

n au

tom

ated

flow

of e

lect

roni

c he

alth

info

rmat

ion

usin

g cu

ttin

g ed

ge

info

rmat

ics t

ools

and

eval

uate

s the

use

and

val

ue o

f aut

omat

ed d

ata

retr

ieva

l fr

om a

cros

s div

erse

hea

lth c

are

sett

ings

incl

udin

g th

e us

e of

nov

el te

xt

min

ing

tool

s.

Qua

lity

Impr

ovem

ent;

Trai

ning

&

Tech

nica

l A

ssist

ance

; To

ols/

Prod

ucts

; R

esea

rch

Dat

a R

epos

itory

; Pa

tient

s/C

onsu

mer

s; Pa

tient

Saf

ety

Surv

eilla

nce

for A

dver

se

Dru

g Ev

ents

(AD

E) in

A

mbu

lato

ry P

edia

tric

s (A

HRQ

EQ

M G

rant

)163

Was

hing

ton

Uni

vers

ityD

evel

ops a

nd d

issem

inat

es h

ealth

IT e

vide

nce

and

evid

ence

-bas

ed to

ols

to im

prov

e he

alth

car

e de

cisio

nmak

ing

thro

ugh

the

use

of in

tegr

ated

dat

a an

d kn

owle

dge

man

agem

ent.

Use

s aut

omat

ed su

rvei

llanc

e to

mea

sure

the

inci

denc

e of

out

patie

nt A

DEs

suffe

red

by c

hild

ren

with

sick

le c

ell d

iseas

e,

cyst

ic fi

bros

is, o

r can

cer,

eith

er in

the

emer

genc

y de

part

men

t (ED

) or

durin

g th

e tr

ansit

ions

bet

wee

n ho

spita

l adm

issio

n an

d di

scha

rge.

Ana

lyze

s da

ta g

ener

ated

from

BJC

Hea

lthC

are

syst

em, w

hich

incl

udes

the

St. L

ouis

Chi

ldre

n’s H

ospi

tal.

Res

earc

h;

Publ

ic H

ealth

Dec

ision

mak

ing;

C

are

Coo

rdin

atio

n;

Dat

a A

ggre

gatio

n/

Inte

grat

ion

Am

bula

tory

C

are;

Can

cer

http://www.edm-forum.org/publicgrant/About/projectprofiles/scanner/





hhttp://healthit.ahrq.gov/ahrq-funded-projects/using-electronic-records-detect-and-learn-ambulatory-diagnostic-errors





http://www.edm-forum.org/publicgrant/About/projectprofiles/scoapcertn/






http://healthit.ahrq.gov/ahrq-funded-projects/surveillance-adverse-drug-events-ambulatory-pediatrics




C-2

9

Hea

lth IT

-Ena

bled

Qua

lity

Mea

sure

men

t:

Pers

pect

ives,

Pa

thw

ays

,

an

d P

ract

ica

l G

uid

an

ce

Tags

Use

d:

Func

tion

al

Topi

cal

Spec

ial

Advi

sory

Bod

yBe

acon

Com

mun

ities

Car

e Pr

ovid

erC

olla

bora

tive

Con

sum

er E

ngag

emen

tD

ata

Rep

osito

ryG

rant

Pro

gram

Hea

lth In

form

atio

n Ex

chan

ge

Ince

ntiv

e Pr

ogra

mM

easu

res C

lear

ingh

ouse

Paye

rPu

blic

Hea

lth (t

o in

clud

e Po

pula

tion

Hea

lth)

Publ

ic R

epor

ting

Qua

lity

Impr

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D-1



Appendix D. List of Focus Group Participants and RFI Respondents

D.1. Focus Group Participants

AHRQ would like to thank the following individuals for their valuable insights as participants in our focus groups. Their thoughtful deliberations and recommendations helped frame this report.

Please note, however, that content of the report should not be construed to reflect the opinions of any one individual or consensus among contributors; it cannot be assumed that all contributors agree with all findings illuminated in this report. Furthermore, individuals participated as independent experts, not on behalf of their organizations; organizational endorsement should not be assumed.

Taroon Amin, Ph.D.(c), M.A., M.P.H.National Quality Forum

Christopher Barnesathenahealth, Inc.

Andrew Baskin, M.D.Aetna

Christine Bechtel, M.A.National Partnership for Women & Families

Miriam BeechamHealthwise

A. John Blair, III, M.D.MedAllies and Taconic IPA, Inc.

Keith BooneGE Healthcare

Maureen Boyle, Ph.D.The Substance Abuse and Mental Health Services Administration

Helen Burstin, M.D., M.P.H., FACPNational Quality Forum

Jason ColquittGreenway Medical Technologies

Patrick Conway, M.D., M.Sc.Centers for Medicare and Medicaid Services

Douglas CyranWellPoint

Joe Francis, M.D., M.P.H.Department of Veterans Affairs

Mike Furukawa, Ph.D.Office of the National Coordinator for Health IT

Charles A. Gallia, Ph.D.Oregon Medicaid Program/Oregon Health Authority

Kate Goodrich, M.D., M.H.S.Centers for Medicare and Medicaid Services

Rosemary HallSocial Security Administration

Andrew Hamilton, R.N., M.S.Alliance of Chicago Community Health Centers

Lein HanCenters for Medicare and Medicaid Services

Yael Harris, Ph.D, M.H.SFormerly of the Human Resources and Services Administration—now at the Office of the National Coordinator for Health IT

Robert HastingsSocial Security Administration

Sharon M. Hibay R.N., D.N.P.American Board of Internal Medicine

Judith Hibbard, Dr.P.H.Oregon Health, University of Oregon

Aparna HigginsAmerica’s Health Insurance Plans

Devery Howerton, Ph.D.Centers for Disease Control and Prevention

Jesse James, M.D., M.B.A.Office of the National Coordinator for Health IT

Marcia Guida James, M.S., M.B.A., C.P.C.Humana Inc.

D-2



Diane Jones, J.D.American Hospital Association

Fasiha Kanwal, M.D., MSHSBaylor College of Medicine

Lisa M. Kern, M.D., M.P.H., FACPWeill Cornell Medical Center

Karen, Kmetik, Ph.D.American Medical Association

Eugene A. Kroch, Ph.D.Premier, Inc.

Kevin Larson, M.D.Office of the National Coordinator for Health IT

Elizabeth Leibach, Ed.D.The Centers for Disease Control and Prevention

Alice Lind, R.N., M.P.H.Center for Health Care Strategies

Gerard Livaudais, M.D., M.P.H.Quantros

Denise Love, B.S.N., MBHCANational Association of Health Data Organizations

Ranyan Lu, Ph.D., M.B.A.UnitedHealth Group

Ginny Meadows, R.N.McKesson

Samantha Meklir, M.P.P.Health Resources and Services Administration

Farzad Mostashari, M.D., Sc.M. Office of the National Coordinator for Health IT

Vi NaylorGeorgia Hospital Association

Sean NolanMicrosoft Corp.

Jacob Reider, M.D. Office of the National Coordinator for Health IT

Alina Pabin, M.U.P.Blue Cross Blue Shield of Michigan

Carolyn PareBuyers Health Care Action Group

Lisa Patton, Ph.D.The Substance Abuse and Mental Health Services Administration

Eva Powell, M.S.W.National Partnership for Women & Families

Marjorie Rallins, D.P.M., M.S.AMA-convened Physician Consortium for Performance Improvement

Marta Render, M.D.Department of Veterans Affairs

William Rollow, M.D., M.P.H.IBM

Megan Sawchuk, M.T. (ASCP)Centers for Disease Control and Prevention

Eric Schneider, M.D., M.Sc., FACPHarvard School of Public Health

Sarah Scholle, M.P.H., Ph.D.National Committee for Quality Assurance

Jeffery Smith, M.P.P.College of Healthcare Information Management Executives

Shelly Spiro, R.Ph., FASCPSpiro Consulting

Sharon L. Sprenger, M.P.A., RHIA, CPHQThe Joint Commission

Kathleen R. Stevens, R.N., Ed.D., FAANUniversity of Texas Health Science Center

Michael Toedt, M.D.Indian Health Services

Cristie Upshaw Travis, M.H.A.Memphis Business Group on Health

Alexander Turchin, M.D., M.S.Division of Endocrinology, Brigham and Women’s Hospital

Steven Waldren, M.D.American Academy of Family Physicians

Dennis White, M.B.A., M.S.

Bryan Yeaman, M.D.Norman Regional Health System and Oklahoma HIE, SMRTNET

D-3



D.2. RFI Respondents

AHRQ would also like to thank the following individuals for their thoughtful RFI responses which provided a wealth of information that guided the selection of topics most critical to explore in focus groups.

Please note that although organizational affiliation is listed for reference, organizational endorsement should not be assumed as it was often unclear as to if individuals were responding independently or on behalf of their organizations. Credentials are also included where available, but may be incomplete. Concurrence with all items in this paper cannot be assumed as RFI respondents often diverged on opinions, perspectives, and recommendations.

Vinod K. Sethi, M.D. & Simi Abraham, MSIV Texas Tech Health Sciences Center SOM Amarillo

William Acevedo, M.D.MedLogiq

Heather Angier, M.P.H.OHSU Family Medicine

Sean Benson, D.D.S. Wolters Kluwer Health— Clinical Solutions

Peggy C. Binzer, J.D.Alliance for Quality Improvement and Patient Safety

Robert W. Block, M.D., FAAP American Academy of Pediatrics

Pamela J. Brewer, Michelle McGlynn, Leigh Burchell, Jason Colquitt, Lauren Fifield, Charlie Jarvis, Meg Marshall, Ginny Meadows & Mark SegalHIMSS Electronic Health Record Association

John H. Bullion, M.B.A. BoundaryMedical Inc.

Leigh Burchell Allscripts

Blair ChildsPremier, Inc.

Bill ConlanHealthQx

Richard A. Correll, M.B.A. & Drex DeFord College of Healthcare Information Management Executives

Sean M. Currigan, M.P.H. American College of Obstetricians and Gynecologists

Renee CutwayCatholic Health, Nazareth Campus

Clara Evans Dignity Health

Kevin Fickenscher, M.D. American Medical Informatics Association

Willia Fields, DNSc, R.N., FHIMSS & H. Stephen Leiber, CAEHIMSS

Linda E. Fishman, M.B.A. American Hospital Association

Jamie Ferguson & Lori Potter, J.D. Kaiser Permanente

John Grant, M.D. & Michael Winlo, M.D., M.B.A.Palantir Technologies

Dan Haley athenahealth, Inc.

Rosemary Hall Social Security Administration

Justine Handelman & Joel SlackmanBlue Cross and Blue Shield Association

Devery Howerton, Ph.D.The Centers for Disease Control and Prevention

David B. Hoyt, M.D., FACSAmerican College of Surgeons

Andrzej Jablonski

Charles Jaffe, M.D., Ph.D., FACP, FACMI & Donald T. Mon, Ph.D. HL7

Gerald F. Joseph, Jr., M.D., FACOG American College of Obstetricians and Gynecologists

Jay Katzen, M.D.Elsevier Clinical Decision Support

D-4



John Kauchick, R.N., B.S.N.

Rainu Kaushal, M.D., M.P.H.; Lisa M. Kern, M.D., M.P.H.; Erika Abramsom, M.D., M.S.; Jessica Ancher, Ph.D., M.P.H. & Stephen Johnson, Ph.D. Center for Healthcare Informatics and Policy

Steven B. Kelmar Aetna

Shawn Keough-HartzProvider Resources, Inc.

Brandyn D. Lau, M.P.H., CPHJohn Hopkins School of Medicine

Teresa Lee, J.D., M.P.H. Alliance for Home Health Quality and Innovation

Tony LeeContinua Health Alliance

Gerard Livaudais, M.D., M.P.H.Quantros

Debbie O. Lucas, M.P.H. Bridging Care, LLC

Karl Matuszewski, M.S., PharmD; Joan Kapusnik-Uner & George RobinsonFirst Databank

Barbara A. McCann, M.A.Interim Healthcare Inc.

Deven McGraw, J.D., L.L.M., M.P.H. & Alice Leiter, J.D. The Center for Democracy &Technology

Judy Murphy R.N., FACMI, FHIMSS, FAAN & Bonnie Westra, Ph.D., R.N.FAAN Alliance for Nursing Informatics

Debra L. Ness, M.S. & William Kramer, M.B.A. National Partnership for Women & Families

Wendy K. Nickel, M.P.H.Society of Hospital Medicine

Margaret O’Kane, M.H.A.P. National Committee for Quality Assurance

Malinda Peeples, R.N., M.S., CDEWell Doc

Robert Plovnick, M.D., M.S. American Psychiatric Association

qMetrix

William L. Rich III, M.D., FACS American Academy of Ophthalmology

Clarke Ross, D.P.A. Consortium for Citizens with Disabilities & American Association on Health and Disability

Lewis G. Sandy, M.D.UnitedHealth Group

Sandra Seidel, M.S.N., R.N., CSVanderbilt University

Daniel M. Siegel, M.D., FAAD American Academy of Dermatology Association

Bruce Sigsbee, M.D., FAAN American Academy of Neurology

Christopher J. Smiley, D.D.S.Dental Quality Alliance, American Dental Association

Shelly Spiro, M.D.Pharmacy e-Health Information Technology Collaborative

Sharon Sprenger, M.P.A., RHIA, CPHQThe Joint Commission

Glen R. Stream, M.D., M.B.I., FAAFPAmerican Academy of Family Physicians

Zeynep Sumer, M.S.Greater New York Hospital Association

Julia Swanson, M.H.S.A.Henry Ford Health System

Lynne Thomas-Gordon, M.B.A., RHIA, FACHE, CAEAmerican Health Information Management Association

Jeanette Thornton, M.D. America’s Health Insurance Plans

John H. Wasson, M.D.Dartmouth Medical School

Dana Womack, M.S., R.N.Nurse Tech

E-1



Appendix E. Additional Resources

Over the course of research, several Web sites were identified as offering additional information, opportu-nities for collaboration, implementation tools, and various training and technical assistance. Exhibit E-1 below is not exhaustive, but is a sample of some of the many resources identified during the research process. Stakeholders are encouraged to explore and collaborate through a wide variety of available resources. Further, AHRQ does not intend to imply endorsement of all information contained within these pages since most of these Web sites are not maintained by AHRQ.

Exhibit E-1. Listing of Additional Resources for Information and Collaboration

Organization Key Reference Sites

Agency for Healthcare Research and Quality (AHRQ)

�§ Findings and Lessons Learned From the Improving Quality Through Clinical Use of Health IT Grant Initiative�§ Findings From the Transforming Healthcare Quality Through IT (THQIT) Grants�§ Health IT-Enabled Quality Measurement�§ National Resource Center for Health IT�§ National Web Conference on Translating Electronic Data Into Better Quality Care�§ Studer Group Toolkit: Patient Safety�§ Working for Quality (National Quality Strategy)�§ USHIK Meaningful Use Portal�§ USHIK Standards Portal

Centers for Medicare and Medicaid Services (CMS)

�§ CMS Quality Measures�§ EHR Incentive Program�§ Measures Management System�§ Nursing Home Quality Initiative�§ Home Health Quality Initiative

Consumer-Purchaser Disclosure Project �§ Publications Library

The eHealth Initiative (eHI) �§ eHealth Initiative Resource Center�§ HIE Toolkit

Health Resources and Services Administration (HRSA)

�§ Health Information Technology and Quality Improvement

Health Information and Management Systems (HIMSS)

�§ HIMSS Blog�§ HIMSS Events�§ The HIMSS Health IT Value Suite�§ HIMSS Resource Library

Informed Medical Decisions Foundation

�§ Center for Shared Decision Making

Institute of Medicine of the National Academies (IOM)

�§ Best Care at Lower Cost: The Path to Continuously Learning Health Care in America (2012)�§ Core Measurement Needs for Better Care, Better Health, and Lower Costs: Counting What Counts: Workshop Summary (2013)�§ Patient Engagement Meeting Video

http://www.ahrq.gov/

http://www.ahrq.gov/

http://links.govdelivery.com/track%3Ftype%3Dclick%26enid%3DZWFzPTEmbWFpbGluZ2lkPTIwMTMwODE1LjIyMDE0OTYxJm1lc3NhZ2VpZD1NREItUFJELUJVTC0yMDEzMDgxNS4yMjAxNDk2MSZkYXRhYmFzZWlkPTEwMDEmc2VyaWFsPTE3NzMxNTE5JmVtYWlsaWQ9YW5kZXJzb25fa3Jpc3RpbmVfbUBiYWguY29tJnVzZXJpZD1hbmRlcnNvbl9rcmlzdGluZV9tQGJhaC5jb20mZmw9JmV4dHJhPU11bHRpdmFyaWF0ZUlkPSYmJg%3D%3D%26%26%26100%26%26%26http://healthit.ahrq.gov/sites/default/files/docs/page/findings-and-lessons-from-the-improving-quality-through-clinician-use-of-health-it.pdf

http://links.govdelivery.com/track%3Ftype%3Dclick%26enid%3DZWFzPTEmbWFpbGluZ2lkPTIwMTMwODE1LjIyMDE0OTYxJm1lc3NhZ2VpZD1NREItUFJELUJVTC0yMDEzMDgxNS4yMjAxNDk2MSZkYXRhYmFzZWlkPTEwMDEmc2VyaWFsPTE3NzMxNTE5JmVtYWlsaWQ9YW5kZXJzb25fa3Jpc3RpbmVfbUBiYWguY29tJnVzZXJpZD1hbmRlcnNvbl9rcmlzdGluZV9tQGJhaC5jb20mZmw9JmV4dHJhPU11bHRpdmFyaWF0ZUlkPSYmJg%3D%3D%26%26%26100%26%26%26http://healthit.ahrq.gov/sites/default/files/docs/page/findings-and-lessons-from-the-improving-quality-through-clinician-use-of-health-it.pdf


http://healthit.ahrq.gov/ahrq-funded-projects/health-it-enabled-quality-measurement

http://healthit.ahrq.gov/

http://healthit.ahrq.gov/events/national-web-conference-translating-electronic-data-better-quality-care

http://www.innovations.ahrq.gov/content.aspx%3Fid%3D2592

http://www.ahrq.gov/workingforquality/index.html

http://ushik.ahrq.gov/MeaningfulUseMeasures%3Fsystem%3Dmu

http://ushik.ahrq.gov/index_sdo.jsp%3Fsystem%3Dsdo

http://www.cms.gov/

http://www.cms.gov/

http://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/QualityMeasures/index.html%3Fredirect%3D/qualitymeasures/

http://www.cms.gov/Regulations-and-Guidance/Legislation/EHRIncentivePrograms/index.html%3Fredirect%3D/ehrincentiveprograms/

http://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/MMS/MeasuresManagementSystemBlueprint.html

http://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/NursingHomeQualityInits/NHQIQualityMeasures.html

http://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/HomeHealthQualityInits/HHQIQualityMeasures.html

http://www.healthcaredisclosure.org/

http://www.healthcaredisclosure.org/index.php/library-menu/by-activity/publications-menu

http://www.ehidc.org/

http://www.ehidc.org/reports.html

http://www.ehidc.org/hie-toolkit.html

http://www.hrsa.gov/index.html

http://www.hrsa.gov/index.html

http://www.hrsa.gov/healthit/index.html

http://www.himss.org/

http://www.himss.org/

http://blog.himss.org/

http://www.himss.org/Events/index.aspx%3FnavItemNumber%3D17401

http://www.himss.org/ValueSuite

http://www.himss.org/ResourceLibrary/%3FnavItemNumber%3D17397



http://patients.dartmouth-hitchcock.org/shared_decision_making.html

http://www.iom.edu/

http://www.iom.edu/

http://www.nap.edu/catalog.php%3Frecord_id%3D13444

http://www.nap.edu/catalog.php%3Frecord_id%3D13444

http://www.nap.edu/catalog.php%3Frecord_id%3D18333%26utm_medium%3Detmail%26utm_source%3DThe%2520National%2520Academies%2520Press%26utm_campaign%3DNAP%2Bmail%2Bnew%2B07.02.13%26utm_content%3D%26utm_term%3DallTopics

http://www.nap.edu/catalog.php%3Frecord_id%3D18333%26utm_medium%3Detmail%26utm_source%3DThe%2520National%2520Academies%2520Press%26utm_campaign%3DNAP%2Bmail%2Bnew%2B07.02.13%26utm_content%3D%26utm_term%3DallTopics

http://iom.edu/Activities/Quality/VSRT/2013-FEB-25/Day%25201/Patient-Clinician%2520Communication/7-Langer-Video.aspx

E-2



Organization Key Reference Sites

National eHealth Collaborative �§ The Patient Engagement Framework�§ Resource Center

National Quality Forum (NQF) �§ eMeasure Learning Collaborative�§ Measures, Reports, and Tools

Office of the National Coordinator for Health IT (ONC)

�§ Health IT Patient Safety Action and Surveillance Plan�§ Health IT Policy Committee, Quality Workgroup�§ Long Term and Post-Acute Care�§ Nationwide Health Information Exchange Resources�§ Principles and Strategy for Accelerating Health Information Exchange and Advancing Interoperability Webinar�§ Regional Extension Centers�§ Research Collaboratives�§ Research & Innovation to Enhance Health IT�§ The Standards and Interoperability (S&I) Framework

Patient-Centered Outcomes Research Institute (PCORI)

�§ Past Meetings and Events

http://www.nationalehealth.org/


http://www.nationalehealth.org/catalog

http://www.qualityforum.org/


http://www.qualityforum.org/Measures_Reports_Tools.aspx

http://www.healthit.gov/

http://www.healthit.gov/

http://www.healthit.gov/policy-researchers-implementers/health-it-and-patient-safety

http://www.healthit.gov/policy-researchers-implementers/federal-advisory-committees-facas/quality-measures

http://www.healthit.gov/policy-researchers-implementers/long-term-post-acute-care

http://www.healthit.gov/policy-researchers-implementers/nationwide-health-information-exchange-hie-resources

http://www.healthit.gov/sites/default/files/onc_cms_accelerating_hie_webinar.pdf

http://www.healthit.gov/sites/default/files/onc_cms_accelerating_hie_webinar.pdf

http://www.healthit.gov/providers-professionals/regional-extension-centers-recs

http://www.healthit.gov/policy-researchers-implementers/strategic-plan-progress-report/research-collaboratives

http://www.healthit.gov/policy-researchers-implementers/strategic-plan-progress-report/research-innovation-enhance-health-it

http://www.siframework.org/

http://www.pcori.org/

http://www.pcori.org/

http://www.pcori.org/events/%3Ftype%3Dpast

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