4 Data Management

Section 4.7 Implement

Section 4 ImplementData Management - 1

Data Management

Ensuring high-quality data in an electronic health record (EHR) and being a good steward of data in a health information exchange (HIE) environment are critical to optimal use of these and other forms of health information technology (HIT).

Time needed: 4 6 hours

Suggested other tools: NA

How to Use

Review the information in this tool to develop an understanding of how to manage and maintain the best quality data for both an EHR and HIE.

Nature of Data in Information Technology

In information systems, there are two forms of data: structured and unstructured.

Structured data are processable by a computer. They are captured as the values of variables that describe person characteristics. For example, every client has a name. The name variable may have myriad values based on different combinations of the letters of the alphabet. Likewise, every client has a birthdate, from which age may be calculated. The value of automation lies in the fact that structured data can be alphabetized, arranged in a list, compared with other data to generate new data (e.g., age) and alerts (e.g., this value is not within normal limits), graphed, and trended.

Unstructured, or narrative, data are not easily processable. Examples include: a handwritten or typed sentence; information dictated through traditional dictation/transcription, digital dictation, or speech recognition; or data captured as any other form of image.

Both types of data are very important. Structured data support clinical decision making and analytical processes, but unstructured data lends context and richness to information, often referred to as the story.

New technologies are addressing the ability to structure data in narrative descriptions. These technologies are summarized below. As you evaluate EHR products and ways to conduct HIE, keep these new approaches in mind. Obtain information from your vendors about their future plans in this area. The future may occur sooner than you think! Emerging approaches to structuring unstructured data are as follows:

Content management refers to indexing narrative data so it is more readily retrievable. For example, if a therapist needs to be reminded of certain data captured during the assessment, that data can be copied to other locations, such as the notes screen for viewing. However, this data is only viewable, not codified in a manner able to be processed further by the computer.

HL7 Clinical Document Architecture (CDA) can also be used to create processable data. In this case, an indexing process in templates is used. While the Continuity of Care Document (CCD) from HL7 is starting to be used, other templates available in the Consolidated Clinical Document Architecture (C-CDA) are not yet widely used. (See Section 2 Exchange of Clinical Summaries and Other Health Information: CCR, CCD, C-CDA).

XML is used to retrieve data that has been entered into a computer. The Continuity of Care Record (CCR) can be rendered in XML, but receiving systems must have a Web-Services Architecture in order to browse the data, and most EHRs today do not have this.

Natural language processing (NLP) is a technology that parses narrative information in electronic form (e.g., word processed document) to produce structured data. This technology is improving, but is not yet widely deployed in EHR systems. One exception is the use of NLP in discrete reportable transcription (DRT) that provides the ability for voice dictation that follows a specific template in the EHR to capture spoken data points as structured data. Again, not many EHRs support this technology as yet.

Data Management

Data management refers to the process of ensuring that the structured data collected yields complete and accurate information that users can trustThe data quality must support the EHR five rights of (1) having the right clinical data, achieved through the (2) right presentation of templates to capture data and displays that provide information, in support of (3) right decision making, provided within the context of the (4) right work processes, in order to produce (5) right results.

Data quality principles have been introduced to health care by the American Health Information Management Association in its Data Quality Management Model, depicted below. For a comprehensive discussion of how to ensure the quality of the data you are collecting, see: http://library.ahima.org/xpedio/groups/public/documents/ahima/bok1_049664.hcsp?dDocName=bok1_049664

Two key ways to manage data quality are:

Ensure that there is a well-maintained data dictionary in the EHR. A data dictionary is a descriptive list of the names, definitions, and attributes of all data elements to be collected in a database. Once a data dictionary is established, one should be very careful about making changes. While there will always be new terms to add because of new disease states, drugs, etc., simply adding new terms because of personal preference works against data quality.

___________________________________________________________________________

AHIMA. Pocket Glossary of Health Information Management and Technology, Third Edition. Chicago: AHIMA Press, 2012.

Ensure that both the vendor utilizes standard definitions and terminologies and that users adhere to these standards. Every vendor will maintain a data dictionary for its EHR, but not every vendor will use standard names, definitions, and attributes. Also, the vendor may only apply standards to some of the data collected via the EHR. Applying standard data definitions and terminologies not only helps users ensure data consistency but supports the ability to share data with other organizations in HIE (see Section 1.3 Interoperability for EHR and HIE.)

Data Stewardship

Many corporations have applied principles of data management to improve the quality of data used to make business decisions. Often, this is called data stewardship. (See Data Quality Management: The Most Critical Initiative You Can Implement, available at: http://www2.sas.com/proceedings/sugi29/098-29.pdf)

Health care has lagged other industries, both in effectively using the massive amount of data the industry is accumulating and in adopting data stewardship principles. There are many issues associated with the use and potential misuse of health data. To make the best of such data in the most appropriate way, many are calling for the health care industry to adopt principles of data stewardship. (As an example, see A Stewardship Framework for the Use of Community Health Data, National Committee on Vital and Health Statistics (NCVHS): http://www.ncvhs.hhs.gov/121205lt.pdf).

Data stewardship is particularly important as a behavioral health facility is approaching use of HIE. Even if the HIE is not accumulating a data warehouse, the importance of ensuring that the data exchanged are high quality and secure is vital. The following are definitions of data stewardship, data steward, and data governance:

Data stewardship, per NCVHS (see above), is the responsibility of ensuring the appropriate collection, management, use, disclosure, or safeguarding of information.

Data steward is a person (or entity) that has a trust relationship with the data.

Data governance is the process by which responsibilities of stewardship are conceptualized and carried out.

As information is exchanged among participants in a health information exchange organization (HIO), each participant is, in essence, a data steward. The HIO may provide governance, establishing the policies for data structure, standardization of data sets, access privileges, and permissible use of data. However, every user must have a trust relationship with the data or it will not be useful. Inappropriately acquired or misused data can potentially harm the patient or client, as well as the organization.

Recommendation: Instill in every EHR and HIO user a sense of data stewardship. Use the data stewardship framework from NCVHS (summarized and adapted for behavioral health facilities below) to discuss what data stewardship means to your facility:

1.Openness, transparency, and choice. Make sure your clients know what information is being collected within your EHR and what information is being shared with others via an HIO.

2.Purpose specification. Describe for you clients why you are collecting and sharing the data about them.

3.Community engagement and participation. As an HIO participant, you are a member of a community. Engage with this community to ensure everyone upholds the principles of data stewardship.

4.Data integrity and security. Data integrity refers to the reliability of the datathat it meets specified standards of fidelity and has not been altered. Security has a threefold purpose: to ensure confidentiality; to protect the data from alteration or destruction, and to make sure data is accessible when needed. This implies that systems must have sufficient redundancy so that you do not experience unusual downtime, and that there are contingency plans in the event of uncontrollable downtime.

5.Accountability requires identification of the person or entity responsible for stewardship at each point in the flow of data, and its storage and ultimate destruction. From a practical perspective, accountability resides with everyone charged with adhering to data quality standards and security measures, such as protecting passwords and encrypting data in devices and media.

6.Protecting de-identified data. HIPAA permits protected health information (PHI) to be de-identified, rendering it no longer pro