Senior Patients With Multiple Chronic Diseases Organizational Factors & Patients Outcomes M Ryan...

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CARE COORDINATION FOR SENIOR PATIENTS WITH MULTIPLE

CHRONIC DISEASES: EXAMINING THE ASSOCIATION BETWEEN

ORGANIZATIONAL FACTORS AND PATIENT OUTCOMES

A DISSERTATION

PRESENTED TO

THE FACULTY OF THE HELLER SCHOOL FORSOCIAL POLICY AND MANAGEMENT

BRANDEIS UNIVERSITY

In Partial Fulfillmentof the Requirement of the Degree

Doctor of Philosophy

By

Marian Ryan, M.P.H., C.H.E.S.

February 2010

Stanley Wallack, Ph.D., ChairpersonProfessor and Director, Schneider Institutes for Health Policy, Heller School


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UMI Number: 3391164

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The signed copy of the signature page is on file at the Heller School for Social Policy andManagement

This dissertation of Marian Ryan entitled Care Coordination for Senior Patients withMultiple Chronic Diseases: Examining the Association between Organizational Factorsand Patient Outcomes, directed and approved by the candidate's Committee, has beenaccepted by the Faculty of The Heller School for Social Policy and Management and theGraduate Faculty of Brandeis University in partial fulfillment of the requirements for theDegree of

DOCTOR OF PHILOSOPHY

Lisa M. Lynch, Ph.D.Dean, The Heller School forSocial Policy and Management

February, 2010

Dissertation Committee:

Stanley Wallack, Ph.D. (Chair) Professor and Director, Schneider Institutes for HealthPolicy, Heller School

Jody Hoffer Gittell, Ph.D., Associate Professor and Director, MBA Program, HellerSchool

Grant Ritter, Ph.D., Senior Scientist, Heller School

Stuart Levine, M.D., MHA, Corporate Medical Director, Healthcare Partners, Inc.,Assistant Clinical Professor of Internal Medicine at University ofCalifornia, LA, David Geffen School of Medicine

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Copyright by

Marian Ryan

2010

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ACKNOWLEDGMENTS

First and foremost, I wish to acknowledge and thank the Agency for Healthcare Researchand Quality and the Jewish Healthcare Foundation for their respective fellowships thatenabled me to complete my doctoral coursework and dissertation research in residence atBrandeis University. Without such financial support this dream could not have beenrealized.

I also express gratitude to the Brandeis Alumni and the Brandeis University Dean fortheir respective dissertation grants that partially funded the printing of my patient surveyand my travel to conduct the on-site clinic interviews for my research.

I thank my dissertation committee, Drs. Stanley Wallack, Jody Hoffer Gittell, Grant

Ritter, and Stuart Levine for their guidance in the development of this research study. Ithank the gifted professors at The Heller School who afforded me the opportunity toexpand my quantitative skills and apply theory to health services research questions. Iappreciate my site organization for allowing this study to be conducted, providing thesecondary data, and allowing access to their clinics, physicians, and patients for myprimary data collection.

This study could not have been completed without the additional mentoring I received inmultilevel modeling. I wish to thank Professor Xiaodong Liu, Assistant Professor,Psychology Department, Brandeis University for his time and mentoring in thespecification and estimation of multilevel models. I must acknowledge and express mygratitude to the wonderful staff at the University of Michigans Center for StatisticalConsultation and Research, especially Brady West and Zingling Zhang for their expertknowledge and guidance. Richard Congdon, senior programmer at Harvard School ofPublic Health, must be recognized for his assistance in helping me resolve my challengeswith the HLM software. Lastly, I must acknowledge the multilevel modeling discussionlist-serve group that has facilitated sharing between experts in the field of multilevelmodeling and student neophytes eager to learn. I thank Drs. J. Hox and T. Snijders fortaking the time to reply to my e-mails and sending me reference materials.

I wish to thank my dissertation support group Dr. Andrew Ryan, Christina Marsh, andKaren Tyo for their support, encouragement, and constructive criticism throughout thisprocess. Words cannot adequately express my gratitude to Karen Tyo for her expertSAS mentoring that enabled me to complete all of the required programming for thisstudy.

I wish to express my gratitude to the Sacred Heart Parish community in Newton for theirspiritual nourishment and friendship throughout this arduous journey. I especially thankSister Patricia Gallagher for her untiring love, encouragement, prayers, and support as myGod-given, personal angel for this walk. I thank the breakfast group who provided me

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my daily dose of laughter, hot tea, and shared memories over these past years. I amgrateful for the access to a senior population willing to pilot my patient survey and offerme feedback. I thank retired professor and statistician, Dr. Edgar Canty for his help withthe manual input of my completed patient surveys.

Finally, I must acknowledge and express my deepest gratitude to my family and friendsfor their love, encouragement, and inspiration. All the unexpected gifts and e-mails liftedmy spirits especially during the times of self-doubt and discouragement. In choosing torelinquish a wonderful position in California and move 3,000 miles east to accept the pre-doctoral fellowship, my family especially my mother made tremendous sacrifices. I loveand appreciate her more than words can adequately convey and must acknowledge herexamples of faith, courage, and commitment that fed the initiation and completion of thisjourney.

I thank God for a life-time of blessings and divine support that have brought me to thisplace. I ask God to continue to open doors of opportunity that allow me to use His gifts

to make this world a better place for everyone!

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ABSTRACT

CARE COORDINATION FOR SENIOR PATIENTS WITH MULTIPLE

CHRONIC DISEASES: EXAMINING THE ASSOCIATION BETWEENORGANIZATIONAL FACTORS AND PATIENT OUTCOMES

A Dissertation Presented to the Faculty of The Heller School for Social Policyand Management and the Graduate Faculty of Brandeis University,

Waltham, Massachusetts

By

Marian Ryan, M.P.H., C.H.E.S.

The Institute of Medicine has identified care coordination as a national priority to

improve the quality of care. Care coordination is critical for senior patients who are

challenged by our fragmented healthcare delivery system. Many senior patients have

multiple chronic conditions, receive care from numerous providers across different care

settings, and take multiple prescriptions. The primary care physician (PCP) is in a unique

position to coordinate care and the Chronic Care Model (CCM) purports to optimally

support the PCP.

The CCM posits that the redesign of physician practice organizations will result in

effective physician-patient interactions and subsequently improved patient outcomes.

Physician-patient relational coordination and trust, which are not included in the CCM,

may play a significant role in facilitating these productive interactions between

physicians and patients envisioned by the CCM framework. Therefore, the theories of

RC and trust within the CCM framework guide this research.

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This study evaluated quantitatively the association between the Chronic Care Model

components and PCP relational coordination and trust, and nationally recognized quality

measures using patient and organization data from a single, multispecialty medical group

with an Independent Practice Association division. The main research questions

examined in this study were the following: 1) do the CCM components predict quality

outcomes, 2) do PCP relational coordination and trust predict quality outcomes and 3) do

RC and trust moderate patient risk covariates such as low levels of education, etc.?

The patient population was composed of managed care Medicare beneficiaries with

diabetes and at least one additional chronic condition receiving care from this

organization between 2004 and 2007. Longitudinal analyses were conducted using four

years of medical claims and physician satisfaction data from the study organization,

incorporating proxy variables (PCP communication and coordination scores) for

relational coordination (RC) and trust. Cross-sectional analyses utilized primary data

assessing CCM, RC and trust that were linked with respondents 2007 claims data. The

cross-sectional analyses also examined two additional outcome variables end of life

discussions with PCP and overall PCP satisfaction derived from the patient survey.

In all fitted Hierarchical Generalized Linear Models (HGLM) using longitudinal data and

examining the log odds of the diabetes quality measures, PCP communication and

coordination (combined as the proxy variable for RC) was a significant predictor. In the

fitted HGLM using the cross-sectional survey-linked data, PCP RC moderated the

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negative impact on the diabetes quality composite measure from low education of the

patient (p=0.04). Both RC and trust were significantly associated with the probability of

patients having end of life discussions with their PCP (p=0.03). Lastly, the logisitic

model fit with the CCM component scores from 24 clinics, 81 PCPs and 408 patients

found the overall chronic care model score and the score for self-management support

significant (p = 0.07 and 0.03 respectively). In this fitted model the combined variable

for high RC and trust did not reach statistical significance although the coefficient was

positive. Additionally, statistically significant correlations were found between the proxy

variable of PCP coordination/ communication examined as a key predictor in the

longitudinal analyses, and RC and trust examined as key predictors in the cross-sectional

analyses.

In summary, this study found a strong association between high levels of PCP

communication/coordination and diabetes quality composite measures in a senior

population with significant disease burden. Moreover, the study found that PCP

relational coordination and trust play an important role in end of life discussions with

patients. Finally, the study supports previous research which highlights the importance of

the self-management component within the CCM.

Given the growing prevalence of multiple chronic conditions among the elderly, this

study provides evidence to support reimbursement for care coordination within primary

care. The study also supports the current emphasis on the expansion of patient-centered

medical homes within an infrastructure of the Chronic Care Model. Finally, the role of

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PCP partnership including RC and trust is critical to meaningful discussions with patients

in primary care settings when patient preferences and options can be fully explored and

prior to an emergent medical crisis.

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TABLE OF CONTENTS

INTRODUCTION 1

CHAPTER 1: BACKGROUND 4

Study Rationale 4

Theoretical Framework 9The Chronic Care Model (CCM) 9Relational Coordination 11Trust 15

CHAPTER 2: LITERATURE REVIEW 17

Gap in the Literature 23

CHAPTER 3: RESEARCH METHODS 26

Introduction 26Research site and population 28Research Questions: 31

Four-Year Longitudinal Analyses 32

Analytical Plan 32Longitudinal Analysis - Key Predictor Variables 38Dependent Variables Selected Patient Quality and Adherence Measures 44Dependent Variable Construction 46Patient Sample Selection and Variable Construction 52

Longitudinal Analyses Covariates 53

Cross-sectional Analyses (2007) 56Analysis Plan 56Key Variables of Interest 57Patient Survey 58Relational Coordination and Trust 63Patient Survey - Dependent Variables and Variable Construction 67

Physician Survey Development 68PCP Survey - Key Domains of Interest 70Clinic Survey 73Variable Construction from Clinic Survey Assessments 75

CHAPTER 4: DESCRIPTIVE STATISTICS 76

Longitudinal Analyses: 76Final Patient Sample 76

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Dependent Variables 77PCP Descriptive statistics 79Patient Descriptive statistics 81Descriptive Results of Hierarchical Clustering 84

Cross Sectional Analyses 2007 Survey Linked Data 85Descriptive Statistics Patient Survey Responses 85

Dependent Variable Descriptive Statistics 87Patient Descriptive Statistics 88PCP Descriptive Statistics Patient Survey Level-Two 91Clinic Descriptive Statistics Patient Survey - Level three 93PCP Survey Descriptive Statistics 94

CHAPTER 5: BIVARIATE RELATIONSHIPS LONGITUDINAL AND CROSS-SECTIONAL SAMPLES 97

Results of Bivariate Associations Longitudinal Sample 97

Results of Bivariate Associations Cross-sectional Sample 110

Other Bivariate Relationships Explored 117

CHAPTER 6: HIERARCHICAL GENERALIZED LINEAR MODELREGRESSION RESULTS 119

Tested Research Questions and Hypotheses 119

Longitudinal Analyses 120HGLM Diabetes Screening Measure Outcome 121HGLM Diabetes Screening with Control Measure 137HGLM Diabetes Screening Composite Measure 142

HGLM Results 30-day versus 90-day readmission findings 148HGLM Results Group practice Sample 149

HGLM Results - Diabetes Screening Measure Group Practice Model 150HGLM Results - DM Measure with Control Group practice Model 156

Cross-Sectional Analyses 157Patient Survey - HGLM Results Diabetes Screen Composite Measure 157Logistic Regression Results Patient Survey Discussing End of Life and Total PCP Satisfaction 160Logistic Regression Results Linked to CCM Components 161

CHAPTER 7. CONCLUSION AND IMPLICATIONS 164

Assessment of Study Hypotheses 164Benefits of HGLM Analyses in this Study 171Study Limitations 173Recommendations for Future Study 175Health Policy Implications 176

REFERENCES 179

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APPENDICES 190Appendix A. The Chronic Care Model 191Appendix B. Model I Distributions for PCP Key Domains 192Appendix C. Correlation between different constructions of PCP Domains 194Appendix D. Correlation Matrix PCP Domain Scores 197Appendix E. Patient Survey Packet 198

Appendix F. Correlation Matrix MD RC, trust, PCC, and PACIC 206Appendix G. PCP Survey Packet 207Appendix H. ACIC with introduction 214Appendix I. Correlation Matrix PredCMCD and RC and Trust 222

List of Tables

Table 1. Outcome Variables ............................................................................................. 27

Table 2. Organization's Physician Satisfaction Surve ...................................................... 40

Table 3. PCP Domains ...................................................................................................... 42

Table 4. Dependent Variables ........................................................................................... 51

Table 5. Relational Coordination Measure ....................................................................... 58

Table 6. Trust Measure ..................................................................................................... 59

Table 7. PCC and PACIC ................................................................................................ 60

Table 8. Key PCP Domains .............................................................................................. 65

Table 9. Additional Patient Survey Variables ................................................................... 67

Table 10. PCP Survey ....................................................................................................... 68

Table 11. PCP Survey Covariates ..................................................................................... 72

Table 12. Assessment of Chronic Illness Care (ACIC) .................................................... 74

Table 13. Patient Sample Descriptive Statistics .............................................................. 76

Table 14. Model I Dependent Variables .......................................................................... 78

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Table 15. PCP Univariate Statistics ................................................................................. 81

Table 16. Patient Descriptive Statistics ........................................................................... 83

Table 17. Level 3 Model I PCP Sample with Linked Patients ..................................... 85

Table 18. Level 2 Model I Patient Sample Size ............................................................ 85

Table 19. Patient Survey Responders as Compared with Non-responders ...................... 87

Table 20. Patient Survey Linked Dependent Variables (Dichotomous Variables 0/1) 88

Table 21. Patient Survey - Patient Descriptive Statistics ................................................. 90

Table 22. PCP Descriptive Statistics - Patient Survey - Level Two ................................ 92

Table 23 Clinic Descriptive Statistics linked to Patient Survey Respondents ............ 94

Table 24. PCP Descriptive Statistics PCP Survey ........................................................ 95

Table 25. Bivariate Statistics: Diabetes Screens, and Diabetes Screens with A1c andLDL control and No Acute Utilization 2004 through 2007 (significant findings) ........... 98

Table 26. Bivariate Statistics: Diabetes Composite (A1c, LDL, CR screens) and No

Acute Utilization 2004 through 2007 (significant findings) ........................................... 101

Table 27. Bivariate Statistics: Medication Adherence to Oral Diabetes and No AcuteUtilization 2004 through 2007 and Adherence only (significant findings) .................... 104

Table 28. Bivariate Statistics: Medication Adherence to Ace Inhibitors and ArbMedications and No Acute Utilization 2004 through 2007 (significant findings) ......... 106

Table 29. Bivariate Statistics: Diabetes Screens and DM Screens with A1c control andNo Acute Utilization 2004 through 2007 Group Model Only (significant findings) ... 108

Table 30. Bivariate Statistics: Diabetes Screens and DM Screens and No AcuteUtilization Patient Survey Respondents (significant findings) ................................... 111

Table 31. Bivariate Statistics: Diabetes Screening Composite and Composite with NoAcute Utilization Patient Survey Respondents (significant findings) ........................ 112

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Table 32. Bivariate Statistics: Diabetes Screens with A1c control and No AcuteUtilization - Patient Survey Respondents (significant findings)..................................... 113

Table 33. Bivariate Statistics: End of Life and Total PCP Satisfaction Patient SurveyRespondents (significant findings) ................................................................................. 115

Table 34. PCP Sample with Linked Patients Diabetes A1c and LDL Screens HGLMModel .............................................................................................................................. 122

Table 35. Patient Sample Size Repeated Measure Diabetes A1c and LDL ScreensHGLM Model ................................................................................................................. 123

Table 36. Diabetes Screen Multilevel Descriptive Statistics ......................................... 123

Table 37. Diabetes Screen HGLM Unconditional Model ............................................. 125

Table 38. Diabetes Screen HGLM Level-One ............................................................... 127

Table 39. Diabetes Screen HGLM Level-One and Level-Two ..................................... 130

Table 40. Diabetes Screen HGLM Final Model ............................................................ 135

Table 41. PCP Sample with Linked Patients Diabetes A1c and LDL Control & NoAcute Utilization HGLM Model ..................................................................................... 138

Table 42. Patient Sample Size Repeated Measure Diabetes A1c and LDL Control & NoAcute Utilization HGLM Model ..................................................................................... 138

Table 43. Diabetes Screen & Control Multilevel Descriptive Statistics ........................ 139

Table 44. HGLM: DM Screen & Control ...................................................................... 141

Table 45. PCP Sample with Linked Patients Diabetes Composite HGLM Model ....... 143

Table 46. Patient Sample Size Repeated Measure Diabetes Composite HGLM Model 143

Table 47. Diabetes Screening Composite Multilevel Descriptive Statistics.................. 144

Table 48. HGLM: Diabetes Screening Composite ........................................................ 146

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Table 49. Level 3 Group Practice Diabetes Screen HGLM PCP Sample with LinkedPatients ............................................................................................................................ 151

Table 50. Level 2 Group Practice Diabetes Screen HGLM - Patient Sample Size .... 151

Table 51. Group Practice Diabetes Screen Multilevel Descriptive Statistics ................ 152

Table 52. HGLM: Group Practice Diabetes Screen....................................................... 155

Table 53. Patient Survey Final Sample Nested within Primary Care Physicians (PCPs)......................................................................................................................................... 158

Table 54. Patient Survey Final Sample PCPs Nested within Sites/Clinics .................. 158

Table of Figures

Figure 1. Conceptual Model for Longitudinal Analyses .................................................. 33

Figure 2. Average Access, Communication, and Coordination Scores 2004 to 2007 ..... 42

Figure 3. Conceptual Model for Cross-sectional Analyses .............................................. 57

Figure 4. Overall Mean Results for Dependent Variables Over Time ............................ 79

Figure 5. Predicted DMScreens as function of time duration ......................................... 122


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Introduction

Diffusion of the Chronic Care Model (CCM) as a prominent strategy for redesigning the

U.S. health care system promoted by professional associations, conference

presentations on pilot programs and the large national pilot program funded by the Robert

Wood Johnson foundation and the Center For Health Systems Change has been rapid

over the past two decades. The ultimate goal of CCM diffusion was the reorganization of

provider organizations that would function as bridges linking the various fragmented

parts of our system, multiple care settings and providers, to improve clinical patient

outcomes. The key mechanism for these desired results was productive interactions

between informed, activated patients and prepared, proactive practice teams led by

primary care physicians. This key operational mechanism lacked explication within the

model simply described as an expectant result of the CCM components.

A great deal of health services research has found continuity of care with a primary care

physician plays an important role. Medical sociologists have studied the physician-

patient relationship for decades through the evolution of the health care system from one

largely treating acute care illness to one consumed greatly today by treatment for chronic

conditions. This shift in emphasis necessitates ongoing, negotiated, needs-based, goal-

oriented treatment over time that minimizes acute exacerbations rather than producing a

cure. Long-term relationships with primary physicians increase the opportunities for the

exchange of critical information between physicians and patients. Given the complexity

of illness found among the elderly such exchanges assist physicians with assessing

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patients medical conditions, needs and treatment preferences and nurture mutual trust.

Trust is essential to the development of physician-patient partnerships needed to optimize

outcomes in chronic illness care.

This dissertation research was conducted in an integrated multi-specialty group practice

organization which embraced the CCM philosophy of practice redesign and committed

the financial and personnel resources to establish CCM components. The organization

has implemented an Electronic Medical Record system with e-prescribing and registry

list capacity, health education classes, dietician support, care management services, and

team meetings to coordinate care for high-risk patients. While extensive senior

leadership exists for the support of CCM components, variation in quality measures

exists throughout their organization affiliates. This may be the result of physician

adoption and consistent application of supportive CCM tools or it may be the result of

differences in patient-centered care.

One objective of this study was to determine if clinic/site-specific implementation of

CCM components would explain any of the variation observed in quality outcomes

among its Medicare patients with diabetes. Would any specific CCM component or the

CCM composite predict any of the clinical outcomes being examined?

Moreover, at the physician level of the organization would relational coordination and

trust, explain any of the variation observed? Every patient in the organization is assigned

a primary care physician (PCP) accountable for the assessment of health care needs and

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the subsequent provision and coordination of needed health care services. Additionally,

the organization has provided Spanish language classes for the physicians to facilitate

communication among the many Spanish-speaking patients served.

The second objective of this study was to determine if relational coordination and trust in

a PCP predicts any of the clinical outcomes being examined? Would relational

coordination and trust provide measurable constructs for the productive interactions

envisioned in the CCM model as a result of implementing the recommended health care

system redesigned components?

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Chapter 1: Background

Study Rationale

A key recommendation in the IOM report Crossing the Quality Chasm was an appeal to

redesign the health care delivery system with an emphasis on improving the coordination

of care in our fragmented system [1]. Care coordination is often discussed by

policymakers as the next generation or extension of care management strategies aimed at

reducing the ever escalating cost of health care services, reducing system fragmentation,

and improving quality of care and improving consumer quality of life [2]. Nowhere are

these goals as critical as they are in the delivery of care to the growing number of

Medicare beneficiaries; more than half of this group have two or more chronic

conditions, one-fourth have problems with mental function or cognitive impairments and

more than one quarter report their health status as fair or poor [3]. The U.S. healthcare

system design-fragmented, highly specialized, acute care and short-term treatment

focused, impersonal, without financial reimbursement for care coordination-is a poor

match for the beneficiaries just described [4].

Care coordination mechanisms are needed across the various outpatient service units

where care is provided such as, primary care physician offices, specialist physician

offices, diagnostic centers and laboratories, and physical/occupational therapy, and

behavioral health provider offices. According to a survey conducted by the Robert Wood

Johnson Foundation of 6000 Americans living with at least one of six prevalent chronic

illnesses, 45 percent reported receiving no help from their doctor or their health plan in

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coordinating their medical services. Only about half reported involvement with their

doctor in making decisions about their care and were capable of internalizing the

confidence to manage their illness between visits [5]. Additional coordination is needed

during acute episodes of care involving transitions from hospitals to skilled nursing

facilities to homes [6-9]. The Institute of Medicine and the National Priorities

Partnership have identified care coordination as a national priority to improve the quality

of care. A recent study of patients with complex chronic illness in six countries found the

need for system innovations to improve outcomes for patients with complex chronic

conditions [10].

Care coordination is critical for all persons but more so for vulnerable groups such as

senior patients most likely to be challenged by our fragmented healthcare delivery

system. Almost 80 percent of seniors have at least one chronic illness, visit multiple

physicians within multiple care settings and take multiple prescriptions with little

coordination of care [11]. Inadequate care coordination for seniors living with multiple

chronic illnesses can lead to unnecessary nursing home placements, inappropriate

hospitalizations (ambulatory care sensitive conditions), redundant procedures/tests and/or

adverse drug interactions [12]. Lack of care coordination may partially explain the poor

quality care received by persons with chronic illness. According to the Crossing the

Quality Chasm approximately 50 percent of persons with chronic illness were not

Care coordination has numerous definitions. For the purpose of this study the IOM definition of carecoordination is being examined. According to the IOM, care coordination is a set of practitioner behaviorsand information systems intended to bring together access to health services, patient test results, and othervital patient information including patient and family preferences and needs to improve quality of care.

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receiving evidence-based chronic illness care and only 25 to 40 percent had their chronic

conditions under good control [1].

Ideally coordinated, patient-centered or relationship-based care would facilitate receipt of

evidence-based care for chronic illness, and incorporate appropriate geriatric assessments

[13] of long term care preferences, poly-medicine, the home environment and risk of

falls, nutrition, and depression and/or substance use. The primary care physician (PCP) is

in a unique position to coordinate the care within our fragmented delivery system of

largely specialized care. Care coordination has been identified specifically as one of

three components defining primary care [14, 15]. Furthermore, a large majority of

persons with chronic illness (including 90 percent with diabetes in the U.S.) currently

receive the bulk of their care in primary care offices [16]. Hence, strengthening the

primary care physician-patient relationship has the potential to improve coordination of

care and subsequently patient care outcomes.

The Chronic Care Model (CCM) purports to optimally support the PCP in this function

by defining six organizational elements: 1) self-management support, 2) decision-

support, 3) delivery system design, 4) clinical information systems, 5) organizational

leadership and commitment to chronic illness care, and 6) linkages to the community.

Theoretically these six key elements facilitate effective physician-patient interactions and

subsequently improved patient outcomes [17-21]. This conceptual framework developed

at the MacColl Institute for Healthcare Innovation at Group Health Cooperative is based

upon scientific evidence and a thorough examination of successful health industry

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leaders chronic illness programs. However, CCM fails to make explicit the relational

factors between primary care physicians and their patients which have the potential to

impact patient outcomes.

At the micro-level of an organization relational coordination [22-26] and trust [27-29]

may play critical roles in facilitating productive interactions between prepared physicians

and activated patients envisioned by the CCM. Szasz and Hollender, medical

sociologists defined a relationship as an abstraction embodying the activities of two

interacting [persons] [30]. The Model of Mutual Participation was described as the

importance of equality between physicians and patients as human beings. The patients

lived experiences assist the physician in determining the best treatment strategies as

treatment for chronic illness is largely carried out by the patient over a long period of

time. Failure to comply with medical recommendations and delay in seeking care may be

the result of poor experiences with the health care system in general and poor physician

communication specifically. The pathway connecting the six organizational elements of

the CCM to the improved physician-patient interactions is currently underdeveloped in

the model. If relational coordination and trust strengthen the physician-patient

relationship, then research including these dimensions may elucidate this pathway.

Moreover, the relational factors explored in this study have the potential to reduce health

care disparities as communication ineffectiveness has been demonstrated to be partially

responsible for the racial inequalities observed [31]. Social categorization of patients

could be mediated through physician communication and other interpersonal skills [32].

The IOM report described a pathway elucidating the critical link between patient and

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physician communication and health outcomes. The IOM model employed a linear

pathway whereby effective communication led to patient satisfaction and subsequently

patient adherence and improved health outcomes [33]. Improved communication and the

development of a trusting relationship between physicians and patients have the potential

to overcome inherent class, race and power differentials commonly found in the medical

encounter. Effective patient-physician relationships may be the appropriate solution to

reducing health inequalities in the U.S. Positive patient-physician encounters may reduce

racial inequalities in health outcomes by bridging knowledge and power differentials, and

eliminating the negative feedback loop produced by ineffective and/or disrespectful

medical encounters.

Relational Coordination (RC) [26, 34] consisting of timely, frequent and accurate

communication; and shared knowledge, goals, and respect are critical components to an

effective physician-patient relationship. RC and trust [35-37] are the essential ingredients

to developing physician-patient partnerships capable of achieving optimal health

outcomes for seniors living with multiple chronic diseases [38, 39]. While many models

for physician-patient communication are elucidated [40], implementation requires a

trusting, established relationship.

Effective communication is an essential component of relational coordination and trust,

as well as effective teamwork and the evolution of partnerships. Effective

communication alone however may be inadequate for producing the relationship between

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the physician and patient required for the co-production of desired patient outcomes.

Therefore, this study examines the constructs of relational coordination and trust.

Theoretical Framework

The Chronic Care Model, Relational Coordination Theory and trust in personal physician

provide the primary theoretical frameworks for this research study. My hypotheses assert

that relational coordination and trust constitute the mechanism for producing the

desirable patient care outcomes presumed by the implementation of the Chronic Care

Model (Appendix A, Theoretical framework). This study views the PCP-patient as the

critical dyadic team in chronic illness care whereby the patient carries out the primary

self-management activities in between physician office visits. Additionally, relational

coordination and trust may mediate racial inequalities in patient outcomes and adherence.

The Chronic Care Model (CCM)Dr. Ed Wagner from the MacColl Institute for Healthcare Innovation at the Group Health

Cooperative of Puget Sound, Seattle developed an analytical framework or conceptual

model, The Chronic Care Model designed to improve functional and clinical outcomes

for patients living with chronic illness [17-21, 41]. The changing U.S. demographics

intensify the need to determine the Chronic Care Model components most influential in

facilitating the proactive physician-patient relationship and care coordination.

The Chronic Care Model (CCM) provides a conceptual framework to guide organizations

in making a system change to improve chronic illness care [41]. CCM identifies six

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organizational components: 1) self-management support, 2) decision-support, 3) delivery

system design, 4) clinical information systems, 5) organization of health care, and 6) the

community. The six elements provide a framework whereby care coordination is

possible. Within this framework primary care physicians have access to evidence-based

medicine, comprehensive knowledge of patient medical history including care provided

across healthcare settings, and receive feedback reports on their patient panels as related

to their peers and organization benchmarks. Patients and families obtain consistent,

reliable information and are provided the resources needed to manage their chronic

condition(s).

Patients are at the center of The Chronic Care Model as they must live with their chronic

illnesses every day between visits to providers; patients and families are responsible for

the integration of any treatment into their daily routines. Support for self-management

is an essential element of CCM and the one most empirically tested of all the CCM

elements. Patients with chronic illness need the skills, knowledge, and resources to

optimally manage their conditions between physician visits. Patients must modify

behaviors and often medication, and take appropriate action in accordance with

recognized symptoms in order to achieve optimal outcomes.

Physician support ensures appropriate care is provided and missed opportunities are

rare. Decision-support denotes the easy integration of explicit, evidence-based practice

guidelines into the day-to-day practice of busy primary care providers. Ideally multiple

physicians treating the same patient make treatment decisions based upon evidence-based

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medicine. Delivery system design supports care coordination through clarification of

staff roles and access to patient information in a central location accessible to all

providers interacting with the patient. The implementation of electronic medical records

in many organizations is an example of delivery system design. The creation of chronic

care teams with increased information processing capacity for the medically complex

patient is another example. Clinical information systems permit the creation of patient

registries to assist in patient monitoring and outreach. Reminder systems and physician

feedback are other benefits of clinical information systems [19].

The last two elements of The Chronic Care Model are overarching and meant to connect

the other four components discussed above. The organization of the health care refers to

an organizations commitment to managing chronic illness and can be strengthened

through physician champions, support for innovation, and the genuine desire to provide

patient-centered care. The community component acknowledges the importance of the

larger sphere in which patients live, such as local policies promoting chronic illness care.

Health care organizations can form alliances with local schools, churches, community

centers, senior centers and assist them in the promotion of health, prevention of chronic

illness and prevention of complications for those already living with chronic disease [42].

Relational Coordination

Relational Coordination Theory extends and builds upon the design theories of

coordination within organizations. Organizational structure is designed in accordance

with the task-specific information processing needs; uncertainty and interdependence

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creates a greater need for information processing or communication across units [43].

Health care organizations face great uncertainty due to the changing market demands, the

evolving complexity of medicine and the increasing longevity of greater numbers of

individuals living with multimorbidity. Moreover, the resulting increase in medical

specialization and advanced technology has further fragmented the health care system

creating higher levels of interdependencies. The Chronic Care Models elements of

delivery system design and clinical information systems would support Galbraiths

organizational information processing theory by increasing the capacity of the

organization to process information on complex patients.

Tushman and Nadlers information processing model extends Galbraiths by discussing

the additional importance of connections between sub-units, networking. They described

two different approaches for cross unit information processing mechanistic, such as

guidelines and protocols for routine tasks, and organistic, such as team meetings when

high information-processing capability with others is required to fulfill the task [44].

Design theory suggests higher levels of performance and efficiency if such coordinating

mechanisms are used in these situations. The implementation of CCM incorporating

electronic health records as part of its delivery system design would support such

information processing needs.

Argote posits the additional need for autonomy to permit mutual adjustment by agents in

accordance with general policies rather than strict rules [45]. These theories of lateral,

high bandwidth information processing and the importance of autonomy are evident in

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the CCM. Decision-support, delivery system design and clinical information systems all

contribute to efficiencies in care by facilitating the communication among providers and

ensuring access to patient information and optimal care coordination. Within the context

of the IOMs definition of care coordination, physician autonomy is essential in treating

the medically complex, non-routine patient with multi-morbidity and incorporating

patient/family needs and preferences for care.

Gittell introduced an extension to these design theories of coordination and information

processing by integrating the important people component through which other

coordinating mechanisms must work. Relational coordination is a communication and

relationship-intensive form of coordination that are expected to be particularly important

for achieving high levels of performance in settings where tasks are highly

interdependent and where levels of uncertainty and complexity are relatively high [22-

25]. Interdependence and uncertainty are high for the physician-patient dyadic team;

effective physician action depends upon the comprehensive knowledge of the patient.

The treatment plan is negotiated and tailored to meet individual patient preferences, goals

and lifestyle only if optimal information exchange occurs between the physician and

patient. Uncertainty is high given the medical complexity among the elderly, changing

life situations and the enormous time constraints placed upon outpatient medical care.

Interactive, ongoing communication in an atmosphere of shared knowledge, goals, and

respect is critical to achieving improvements in patient outcomes and adherence as

patients must make daily decisions related to their chronic conditions.

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Relational coordination theory suggests the gap in other coordinating mechanisms is met

by measuring seven, equally weighted dimensions; assess communication for 1)

frequency, 2) timeliness, 3) accuracy, and 4) problem-solving capacity, and assess

relationships by 5) shared goals, 6) shared knowledge and 7) mutual respect [22-26].

Gittell developed and validated an instrument for assessing relational coordination among

team members and posits high RC improves efficiency and performance under conditions

of high uncertainty and high task interdependencies [22-26]. Empirical evidence found

that RC mediated coordinating mechanisms such as meetings and the use of boundary

spanners such as case managers, indicating these coordinating mechanisms worked

through their impact on relational coordination not independent from it [22-26].

While several studies have supported this hypothesis in work teams, the hypothesis of RC

leading to improved patient outcomes was recently confirmed between providers and

informal caregivers within the context of hospital discharge following a surgical

procedure [46]. This study extends the hypothesis to the core critical health care team in

the ambulatory medical setting-physician/patient. While several teams operate in our

current health care system, the critical core team is the patient and primary physician.

While ancillary health providers assist busy physicians in caring for medically complex

patients, this work has been demonstrated to be most effective when it is endorsed and

actively supported by the primary care physician.

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Trust

Trust as viewed through the lens of social science may be described as a relationship of

reliance. Trust has been defined as the willingness of a party to be vulnerable often

based upon an underlying assumption of an implicit moral duty [28]. Trust has been

identified as a necessary component to the development of physician-patient relationships

[47-49] . When power and control are no longer the issue, we can form partnerships

with our patients in which we learn together about the mystery we call life [50]. Trust

leads to sharing information and reduces the traditional imbalance in power found

between the physician and patient [29]. While trust is positively related to the length of

the relationship, it is also separate from related measures such as patient satisfaction [29,

51].

Within organizational theory research two types of trust have emerged, affect-based and

cognitive-based trust that were later described as benevolent and competent trust [28].

Trusting a knowledge source (doctor in this case) to be benevolent and competent should

increase the chance that the knowledge receiver (patient) will learn from the action [28].

Organizational studies work has measured and examined the association between both

types of trust and efficiency, quality and other aspects of production. Lewin and Cross

tested a dyadic knowledge exchange model and found that useful knowledge to the

knowledge seeker was gained under conditions of competence and benevolence-based

trust [27]. Moreover, competence-based trust was especially important for the receipt of

tacit knowledge. This might be viewed in the context of the physician-patient dyad in the

following manner. If the patient judges the physician competent he or she is more likely

to provide the physician the type of information that can maximize and personalize the

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recommended treatment increasing the likelihood for treatment effectiveness and patient

adherence. Patients' trust in their physician and physicians' comprehensive knowledge of

their patients have been found to be associated with three important outcomes of care

adherence to physicians advice, patients satisfaction with their primary care physician,

and change in health status [52].

Trust in this study may be found to be subsumed within RC although not explicitly

measured within its empirically tested instrument. A recent study conducted to develop

and test an instrument to measure trust in health care providers among minority patients

with chronic illness identified six characteristics they termed collaborative trust [51].

They are the following: 1) knowledge sharing, 2) emotional connection, 3) professional

connection, 4) respect, 5) honesty, and 6) partnership. Following the development and

testing of a fifteen item survey three distinct domains were identified as, interpersonal

connection, respectful communication, and professional partnering. This work

demonstrates the multi-dimensional aspects of patient trust in providers and results in

domains similar to RC.

Research Questions

The main research questions examined in this study were the following: 1) Do any of the

Chronic Care Model Components predict quality outcomes and adherence among senior

patients with chronic illness: 2) Do physician relational coordination and trust predict

quality outcomes independently and/or jointly?

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Chapter 2: Literature Review

Over a decade ago a group of physicians and researchers at MacColl Institute for

Healthcare Innovation recognized that a paradigm shift was needed within the healthcare

system to address the efficient and effective treatment of chronic illness given its rapidly

increasing prevalence and its associated costs and the current healthcare system designed

for the treatment of acute illness [41]. Previous emphasis in chronic illness care had been

placed on physician behavior (adherence to evidence-based clinical practice guidelines

and medical training) and patient behavior (disease and case management programs).

What was needed was a systems approach to reorganizing care that would facilitate the

desired outcomes [41].

The resulting theoretical paradigm (the Chronic Care Model) reflected the components

found upon studying the best 72 chronic illness programs in the country. CCM was

piloted in quality improvement initiatives to determine if the organizational components

could be integrated into busy practices and if integration was possible, did it make a

difference?

A systematic review was conducted of studies of diabetes programs using the elements of

CCM [53]. Built upon a recent Cochrane review of ambulatory care diabetes

management programs, 39 studies were evaluated relative to four chronic care model

components (self-management, decision support, delivery system design and clinical

information systems). Thirty-two of the 39 studies found improvements in at least one

process or outcome measure for patients with diabetes. Because of the small number of

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studies, it could not be determined the relative impact of multiple components on

outcomes. It was noted however, that 19 of 20 interventions that included a self-

management component improved a process or outcome of care measure [53].

The Robert Wood Johnson Foundation funded quality improvement collaboratives within

72 organizations nationally to implement the Chronic Care Model. The Institute for

Healthcare Improvement using its Breakthrough Series quality improvement strategies

provided technical support and training to these organizations for the implementation.

Organizations chose to focus change efforts on patients with diabetes, heart disease,

asthma and/or depression. RAND was contracted as the evaluator.

Before results of these collaboratives were known, an explosive diffusion of the Chronic

Care Model was observed across the health care industry that was eager for a framework

to address the rising costs from the exponential growth of chronic illness. As an

example, the California Department of Health Services, Medi-Cal Managed Care

Division adopted the CCM framework for the implementation of its state-wide quality

improvement initiatives for asthma and diabetes. The diffusion is further evidenced by

the 1,345 citations identified by a recent meta-analysis of the impact of CCM on patient

outcomes [54]. The final meta-analysis contained 112 studies of CCM across four

disease states (asthma, heart failure, diabetes and depression). Beneficial effects on

clinical outcomes and process of care measures were found for all interventions that

contained at least one or more of the CCM elements [54]. Delivery system design and

self-management support were the two CCM components that contributed the greatest

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impact on outcomes. These studies did not examine CCM as a whole and its influence on

patient outcomes.

A large multispecialty medical group in the Midwest (representing 600 physicians,

300,000 patients and 17 clinics) adopted CCM in 2002 and studied the implementation of

CCM and its subsequent impact on quality for patients with diabetes, heart disease and

depression. While significant changes over two years were identified for three of the six

CCM components, only the diabetes measures were associated with two of the CCM

elements decision support and clinical information systems [20]. The same

organization reported several challenges to CCM implementation. While all the clinics

adopted the expanded, prepared practice team concept, differences across clinics were

pronounced. Finding suitable meeting times for collaboration created a universal logistic

challenge. And few clinics actively engaged the physicians [55].

The RAND evaluation on the randomized controlled Improving Chronic Illness Care

Collaboratives revealed mixed results. While sites in the treatment arm averaged 30

change efforts to implement CCM, the depth or intensity of such changes varied

tremendously across sites from ratings of 17 percent to 76 percent of the highest rating

possible [56]. Change ratings did correlate with each of the six elements of CCM. The

evaluation of the impact of these collaboratives on clinical outcomes has proved more

challenging due to the diversity of interventions employed and the conditions

emphasized. One study evaluating four collaboratives focusing on CHF found a

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statistically significant improvement in the composite clinical measure among the

collaborative organizations as compared to controls [57].

A recent meta-analysis of articles on CCM effectiveness published since 2000 concluded

that evidence supports the CCM as an integrated framework for redesigning group

practice to improve patient outcomes. This study examined only published articles that

cited at least one of the five seminal articles on CCM as a reference, Additionally, the

intervention had to operate within the ambulatory care practice, change how care is

actually delivered in some way, and incorporate at least four components of the Chronic

Care Model [58].

The Chronic Care Model was intended to implicitly function as a coordinating

mechanism by virtue of redesigning the delivery system. While the framework identifies

a systems approach to reorganizing the structural and some of the process components

required to improve outcomes, the implicit collaborative management at the heart of

CCM has not been adequately measured or been made explicit. In an earlier paper by the

founders of CCM, collaborative management occurs when patients and care providers

have shared goals, sustained working relationship, mutual understanding or roles and

responsibilities and requisite skills for carrying out their roles [59]. This implicit

coordinating mechanism has yet to be tested within the CCM framework.

With the rapid diffusion of CCM within the private sector and managed care

organizations positive experiences with disease and case management programs,

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government legislated demonstration projects aimed at improvements in chronic illness

care. While many of these demonstration projects utilized CCM implementation, disease

and case management programs, the primary care physician had been largely excluded as

an essential partner. Medicares Coordinated Care Demonstration (MCCD) and the

Medicare Health Support Program focused primarily on the patient, with encouragement

by contractors to engage the primary care physician.

The final report to Congress on the outcomes of the fifteen programs for MCCD

following the three years of implementation was not favorable [60]. Thirteen of fifteen

programs failed to reduce hospitalizations; one program actually reported an increase in

hospitalizations and one program reported fewer hospitalizations. None of the programs

generated net savings. Programs targeted a single chronic condition (CHF, CAD or

diabetes) and three used high-risk predictive algorithms for the selection of their

respective target population. Patients were the focus of most programs and while all

programs sent reports to primary care physicians, only 42 percent of physicians rated

such reports as very useful. These programs had no impact on adherence measures and

made improvements in only a few quality measures. The authors of the report concluded

that programs lacking a strong transitional component across care settings were unlikely

to yield cost-savings.

The second report to Congress on the Medicare Health Support program revealed that

none of the pilot programs at 18 months into the three-year program period met the three

requirements in the legislation improvements in clinical care quality, patient

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satisfaction, and cost neutrality to CMS [61]. The Medicare Health Support program was

the largest population-based disease management (DM) program ever legislated.

Contracts were awarded to eight experienced disease management companies or

organizations with DM infrastructure and support to manage CMS identified high-risk

beneficiaries. Among the programs original patient population only 16 out of 40

evidence-based process-of-care measures improved. None of the programs achieved

statistically significant reductions in hospitalizations, readmissions, or ED visits in their

original populations as compared with control groups. The program was suspended.

One of the largest challenges faced by these two CMS initiatives to improve chronic

illness care and reduce inappropriate expenditures was the lack of direct primary care

physician involvement. Care management or disease management programs that operate

distinct from primary care practice have the potential to further fragment medical care

[4]. A major controversy with regard to DM programs is the extent to which services

they provide are integrated with the patients other medical care [4]. Kane and others

believe that a high degree of integration between these support programs and physicians

is essential in establishing and maintaining effective physician-patient relationships. The

lack of physician involvement may have also contributed to the relatively low

participation rates for these programs as well as for the earlier CMS Case Management

demonstration programs of the mid 1990s [62, 63].

Evaluation of care coordination on patient outcomes has been further hampered by the

lack of a consistent definition and measurement. Researchers have developed, tested, and

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validated care coordination questions as part of overall patient satisfaction surveys of

ambulatory care [64-66]. Researchers with Improving Chronic Illness Care (ICIC)

developed and validated The Primary Assessment of Chronic Illness Care (PACIC) to

assess concordance of care with the major tenants of the Chronic Care Model that could

be accurately evaluated by the patient [67]. Too little empirical research as been

employed linking measurement of care coordination to patient outcomes [52].

Furthermore, selection bias has plagued disease management program evaluations for

decades and more recently care coordination evaluation. CMS utilized prospective

claims-based, predictive risk modeling to identify eligible patient cohorts for the

demonstration projects. Such methods have been reported to be only 14 to 20 percent

reliable in predicting high cost individuals in the coming year based upon prior claim

history [68]. The Medicare beneficiaries recruited and participating in the CMS Care

Coordination Demonstration projects were found to be healthier than the control group

beneficiaries thereby resulting in accrued benefits to those who perhaps needed them

least [63]. By contrast, identifying factors that strengthen the primary care physician-

patient team would impact all Medicare beneficiaries as even those in Fee-For-Service

Medicare acknowledge a personal or primary physician.

Gap in the Literature

A broader constellation of interdependent physician and patient behaviors may constitute

the essential yet untested factors important to improved patient outcomes for elders living

with chronic illnesses. The traditional physician-patient dyad produces dominant

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physicians and passive patients not conducive to the development of an effective team

approach to care management and the co-production of optimal patient outcomes. High

quality communication, shared knowledge and shared goals (components of relational

coordination) have the potential to create a bridge through which effective teamwork and

partnership can evolve. To achieve positive outcomes where the goal is not curative but

rather slowing disease progression, the physician must partner with the patient by

working together as an interdependent team.

Although the empirical evidence on the impact of CCM on patient outcomes has been

favorable given interventions that incorporate at least one CCM component, the

coordinating mechanism for the productive interactions between physicians and patients

has not been measured or made explicit. This research attempted to address a gap in the

literature by examining simultaneously the impact of the CCM components and the

relational factors of the physician-patient dyad within a single umbrella organization, thus

controlling for many potential confounders. Previous research has had difficulty

assessing quantitatively the relative importance of the six organizational CCM

components on patient outcomes [54]. This may be the result of the inadequate

consideration and measurement of the relational components required for delivering

effective care for chronically ill individuals may have been the reason.

The IOM report described an untested pathway elucidating the critical link between

patient and physician communication and health outcomes [1]. The mediating factor in

this linear pathway is patient satisfaction, more often a quality outcome in itself [69-71].

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The goal of this study is to incorporate CCM components, and physician relational

coordination and trust to identify the relative importance of each on patient outcomes and

adherence. This study seeks to explore the extension of the organizational theories of

relational coordination and trust as applied to the primary care physician-patient. This

dyadic team is primarily responsible for the co-production of quality outcomes for senior

patients living with chronic diseases.

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Chapter 3: Research Methods

Introduction

Longitudinal and cross-sectional analyses were conducted to evaluate quantitatively the

association between the CCM components and physician relational coordination and

trust, and patient outcomes and adherence using data from a single, multispecialty

medical group with an Independent Practice Association division as further described.

The patient population was composed of managed care Medicare beneficiaries receiving

care from this organization between 2004 and 2007. Longitudinal analyses utilized four

years of medical claims and physician satisfaction data from the study organization,

incorporating proxy variables (PCP communication and coordination) for relational

coordination (RC) and trust. Cross-sectional analyses utilized survey data assessing

CCM, RC and trust that was linked with respondents 2007 claims data. All longitudinal

and cross-sectional analyses incorporated the same dependent variables, nationally

recognized quality process measures, intermediate outcome and outcome measures, and

patient medication adherence measures (Table 1.). Additionally, the 2007 cross-sectional

analyses employed two outcome variables derived from the patient survey.

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Table 1. Outcome Variables

Outcome Quality Measure

(Type/endorsement)

Model

A1c and LDL screen Process (HEDIS) Longitudinal &

Cross-sectional

A1c and LDL control Intermediate outcome (HEDIS

) Longitudinal &Cross-sectional

Colorectal screen Process (HEDIS) Longitudinal &Cross-sectional

ACSC* hospital admission Outcome (AHRQ) Longitudinal &Cross-sectional

30-day all cause readmission Outcome (AHRQ) Longitudinal

ED visit for chronic condition Outcome (none) Longitudinal &Cross-sectional

Oral diabetic medication adherence Intermediate outcome (NQF) Longitudinal &

Cross-sectional

Ace inhibitor/ARB medication

adherence

Intermediate outcome (NQF) Longitudinal &

Cross-sectionalOverall PCP satisfaction Intermediate outcome (NCQA

) Cross-sectional

Discuss EOL preferences with PCP Process (none) Cross-sectional

* Ambulatory Care Sensitive Condition One of five chronic illnesses-DM, CHF, COPD, asthma, and HTN

Hierarchical Linear Modeling (HLM) techniques were employed to permit the

formulation of explicit structural models for processes that occur within each level of a

hierarchy (patient, PCP and clinic). A major goal of this study was to determine the

relative influence of the CCM components and physician relational factors on patient

outcomes within a natural occurring, hierarchical nested health care structure [72-79].

This statistical approach is discussed further in the analysis plan. All analytic models

represented exploratory research, which is appropriate when attempting to apply theory in

a new way [80]. Although the RC index has been empirically tested and validated it has

not been used to assess the physician-patient dyad.

This chapter is organized as follows. First, the study site, study population, and the

research questions related to the longitudinal and cross-sectional analyses are presented.

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Second, the analysis plan, conceptual model, and the variable construction for key

predictors, dependent variables and covariates, and the patient sample for the longitudinal

analyses are described. Third, the primary data collection for the cross-sectional analyses

is explained including the development and implementation of the patient survey

instrument and the subsequent variable construction. Lastly, the clinic instrument used to

assess the Chronic Care Model components and related CCM variables are presented.

Research site and population

Study Site

A large multispecialty medical group with an Independent Practice Association (IPA)

division in southern California served as the study site for this research. This

organization embraces the Chronic Care Model (CCM) in its operations and management

philosophy and possesses many of the organizational attributes desirable for achieving

optimal patient outcomes such as physician leadership and autonomy [81], and the

appropriate alignment of physician financial incentives [82, 83]. Moreover, the

organization is structurally more representative of physician practice organizations than

the frequently studied Kaiser Permanente Model. It is also highly rated by consumers

and by independent organizations such as the CA Health Foundation among IPAs in the

CA market.

This organization employs many of the CCM components including electronic medical

records with e-prescribing capacity, evidence-based clinical guidelines, physician-

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feedback reports, and integrated care management and disease management programs.

Despite these characteristics and practice improvements, variation was identified in

quality measures and utilization across physicians, medical group clinics and IPA sites.

Study Population

The study population selected were senior managed care patients (65 years of age and

older) receiving medical care by providers within the organization between 2004 and

2007 with evidence of diabetes and at least one additional chronic illness. Diabetes was

selected because of its high prevalence and cost, and the existence of standardized, well-

established quality measures for patient outcomes. Multi-morbidity was an additional

criterion because of its increased prevalence among older adults and its potential impact

on quality and adherence. A need for more research explicitly on multi-morbidity has

been identified as much of the published research to date has focused on single chronic

illnesses despite the increased prevalence of multiple chronic diseases [84, 85].

The site organization identified all patients identified with diabetes and a second chronic

condition. The composite list of co-morbid chronic diseases included the following:

chronic hepatitis, alcohol cirrhosis, chronic renal failure/ESRD, chronic pancreatitis,

Arthritis (Rheumatoid and Osteoarthritis), hypertension, atrial fibrillation,

CVD/stroke/TIA, CHF, Multiple Sclerosis, Parkinsons disease, COPD/emphysema,

Asthma, Ischemic Heart Disease/CIHD/CAD/PVD, stable angina, back/lumbar pain,

Alzheimers disease, schizophrenia, anxiety disorder and major depressive/bipolar

disorder. These chronic conditions have been previously used to quantify the burden of

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illness from claim data [86-88]. Identified patients with a diagnosis of active cancer

were excluded as attainment of selected quality and adherence measures may not be

appropriate goals for a patient in treatment for active cancer.

The identified patient population for the study was also further divided into those patients

receiving their care or the majority of their care within the group model clinics. This sub-

population is referred to subsequently as the group practice population. The Chronic

Care Model components are known only for this sub-population.

Data provided for analyses on all identified patients included the following: 1) month-

month enrollment data with demographic information and PCP and clinic/site

assignments; 2) outpatient encounter data; 3) HEDIS-eligibility flags for diabetes

measures and the colorectal screening measure; 4) Hospital and Emergency Department

claims; and 5) pharmacy claims. As the organization assumes full risk contracts

(financially responsible for all medical services provided) for its managed Medicare

patients the claims extracts done for this study provided comprehensive information on

all identified patients. The notable exception was the non-reporting of pharmacy claims

by four small health plans with fewer than 10 percent of the organizations managed care

patients.

This study was approved by the Brandeis University IRB and conducted under an

executed Limited Data Use Agreement.

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Research Questions:

1) Do self-management resources and electronic medical records (two components of

the Chronic Care Model) predict quality outcomes and patient adherence for seniors

with multiple chronic diseases? (Four-Year Longitudinal Analyses; Group practice

population)

2) Do physician communication and care coordination behavior (imperfect proxy

variables for the variables of interest, RC and trust) predict patient outcomes and

adherence? (Four-Year Longitudinal Analyses)

3) Does the CCM predict quality outcomes and patient adherence for seniors with

multiple chronic diseases? (2007 Cross-sectional Analyses)

4) Does relational coordination and trust within the physician-patient dyadic team

predict patient outcomes and adherence? (2007 Cross-sectional Analyses)

Secondary Research Questions: 2007 Cross-sectional Analyses

1) Is there a correlation between the proxy variables used in the longitudinal analyses

and the specifically measured RC and trust in the cross-sectional analyses and

physician?

2) Is trust subsumed within the relational coordination construct or does it measure a

distinct characteristic of the physician-patient relationship, distinct from RC?

3) Are differences observed in RC and trust among patients of different ethnicity/race

and for those who report non-English as their preferred language?

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Four-Year Longitudinal Analyses

Analytical Plan

Dependent variables (measured annually at the patient level) were regressed on time-

varying patient covariates at level-one, time-invariant patient covariates at level-two, and

physician covariates and key physician predictors at level-three. Dependent variables

were constructed from claims data, years 2004 through 2007. Key predictors in the

longitudinal analyses included patient continuity with the PCP at level-two; and the

relational domains of physician communication, coordination and access at level-three.

The subset of longitudinal models based solely upon the group assigned patients (group

practice population) included two key CCM components (presence of health education

resources and electronic medical records) as fixed effects at level-three. The longitudinal

analyses empirically examined the IOM purported pathway to improved patient outcomes

via effective physician communication. Additionally the longitudinal analyses employed

physician coordination, communication and access as proxy variables for relational

coordination assessed subsequently in the cross-sectional analyses. The conceptual

model is depicted in Figure 1.

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Figure 1. Conceptual Model for Longitudinal AnalysesPhysician

CoordinationCommunication

& Access(+ covariates)

Patient

continuity with

PCP(+ covariates)

Physician-Patient

Dyadic Team

Patient Quality

Outcomes and

Adherence

CCM-

Group-only:

HE & EMR

PCP visits/Yr

SP Visits/Yr

Same PCP/Yr

The choice for analytic methods for clustered data can have major implications for

medical practice-based, health services research [72-74]. In health care patients treated

by a particular physician receive care in a common treatment setting that is influenced by

physician characteristics and philosophy, and that may differ from once physician to

another [72, 79]. Likewise these physicians will be influenced by the practice setting in

which they work and this setting is likely to differ across settings. Ignoring group

membership can lead to erroneous conclusions (inflated parameter estimates) regarding

the impacts of influence on outcomes [72, 79]. As a major goal of this study was to

determine the relative influence of the CCM components and PCP relational factors on

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patient outcomes as related to these likely correlated groups (patients, physicians and

clinics), HLM was the appropriate analysis method.

Wong and Mason (1985) and Longford (1993), Goldstein (1991, 2003), Snijders and

Bosker (1999) and Raudenbush and Byrk (2002), have described the multilevel extension

of generalized linear models [89-92]. The transformed multiple regression equation of

the linear predictor defines a two-level multilevel structure as follows: nij = Y00 +

Y10Xij + Y01Zi + Y11ZjXij + ujXij + u0j. For this model, nij represents the log-odds of

the conditional probability of the outcome being measured for individual i nested within

organization j. In a repeated measure model i would denote time period i within

individual j. Y00 represents the overall intercept value or the average log odds

conditional on all predictors and the level-two random error term. Y10Xij denotes the

slope of X for unit j. Y01Zi is a level-two predictor variable and Y11ZjXij is a cross-

level interaction term. UjXij is the random effect of X for unit j. In a generalized linear

model the variance of the level-one error term is a function of the population proportion

and is not estimated separately. The value of approximately 2/3 or 3.29 specifies the

variance in a logistic distribution [91]. The logit link function transforms n into the

logit(n).

Using Hierarchical Generalized Linear Modeling all longitudinal analyses predict the

probably of an event across years as a function of time, covariates and key predictors, and

random effects. The logit link transforms each linear structural model into the log of the

odds of success (represented by nij) or a positive event [90, 91, 93]. Predicted log-odds

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may be converted into odds by taking the exp(nij) or into a predicted probability by

estimating ij = 1/1 + exp(-nij).

HGLM analysis allows the inclusion of all the patients meeting study criteria regardless

of the number of measurement periods as unbalanced panel data can be modeled

successfully. HGLM estimation methods enable unbiased estimates of the Beta

coefficients in each unit as it borrows strength from the fact that the estimation of Beta is

being repeated across a number of units [93]. HGLM allows the cluster variance to be

modeled in addition to correcting the standard errors for fixed effects parameters as

compared with other approaches such as Generalized Estimating Equations (GEE). GEE

analysis corrects the standard errors of the fixed effects parameter estimates ensuring

proper interpretation of parameter significance however the cluster variation is treated as

a nuisance [91].

Additionally, as previously discussed HGLM permits predictors at each level of the

hierarchical data structure (e.g. time-varying predictors across years, individual patient

predictors and physician predictors). HGLM allows for estimation of covariance

structures between levels and estimates predictors from one level that potentially explain

variance at another lower level. Most importantly, HGLM estimates the reduction in

variation at the highest level of the data structure that is explained by the key predictors.

HGLM assumptions include the following. The transformed function forms are linear at

each level. Residuals at level two and three are assumed to be normally distributed and

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