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The American Journal of Managed Care June 2015
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Impact of the Patient-Centered Medical Home on Veterans' Experience of Care
Ashok Reddy, MD; Anne Canamucio, MS; and Rachel M. Werner, MD, PhD
Moving From Healthcare to Health
Bernard J. Tyson
Improving Diabetic Patient Transition to Home Healthcare: Leading Risk Factors for 30-Day Readmission
Hsueh-Fen Chen, PhD; Taiye Popoola, MBBS, MPH; Kavita Radhakrishnan, PhD, RN; Sumihiro Suzuki, PhD; and Sharon Homan, PhD
Quality of Care and Relative Resource Use for Patients With Diabetes
Troy Quast, PhD
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David Auerbach, PhD, MS; Ateev Mehrotra, MD, MPH; Peter Hussey, PhD; Peter J. Huckfeldt, PhD; Abby Alpert, PhD; Christopher Lau, PhD; and Victoria Shier, MA
Medication Adherence and Measures of Health Plan Quality
Seth A. Seabury, PhD; Darius N. Lakdawalla, PhD; J. Samantha Dougherty, PhD; Jeff Sullivan, MS; and Dana P. Goldman, PhD
Cost-Effectiveness of Combinatorial Pharmacogenomic Testing for Treatment-Resistant Major Depressive Disorder Patients
John Hornberger, MD, MS, FACP; Qianyi Li, MS; and Bruce Quinn, MD, PhD
Stimulating Comprehensive Medication Reviews Among Medicare Part D Beneficiaries
William R. Doucette, PhD; Jane F. Pendergast, PhD; Yiran Zhang, MS, BS Pharm; Grant Brown, PhD; Elizabeth A. Chrischilles, PhD; Karen B. Farris, PhD; and Jessica Frank, PharmD
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John Kralewski, PhD, MHA; Bryan Dowd, PhD, MS; Ann Curoe, MD, MPH; Megan Savage, BS; and Junliang Tong, MS
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Helaine E. Resnick, PhD, MPH; and Michael E. Chernew, PhD

Impact of the Patient-Centered Medical Home on Veterans' Experience of Care

Ashok Reddy, MD; Anne Canamucio, MS; and Rachel M. Werner, MD, PhD
The patient-centered medical home is being adopted to improve patient experiences of care. However, the authors observed no impact of medical home implementation on veterans' care experiences.

Objectives: A core tenet of the patient-centered medical home is improving patient experiences of care, but evidence is limited on the impact of medical home adoption on patient experiences of care.

Study Design: We conducted a repeated cross-sectional, patient-level analysis in 1 region of the Veterans Health Administration (VHA), which includes 56 primary care sites.

Methods: Our primary outcomes include 5 domains of patient care experience from the Survey of Healthcare Experiences of Patients (SHEP). We used a linear probability model to test whether
changes in medical home implementation are associated with changes in patient experience of care.

Results: During the study period, 30,849 SHEP respondents received care. We observed significant increase in medical home implementation: a 10-fold increase in percentage of primary care
providers who were part of a medical home, a 7-fold increase in 8 out of 9 structural measures of the medical home, and an increase in overall quality of medical home implementation. Yet, we found no association between medical home adoption and 5 domains of patient experience of care. For example, patients assigned to a medical home provider had a 0.51 percentage point (95% CI, –1.8 to 2.8) higher response in how well they communicate with their provider compared with patients not assigned to a medical provider and with patients in the pre–medical home

Conclusions: Despite wide implementation of the medical home, we did not see an improvement in patient experiences of care in the VHA. As we focus on primary care transformation, we need to find ways to incorporate the patient’s voice and input into these transitions.
Am J Manag Care. 2015;21(6):413-421
Take-Away Points
Large-scale adoption of the patient-centered medical home in the Veterans Health Administration is, in part, being implemented to improve patient experiences of care, but evidence is limited on the impact of medical home adoption on veterans’ experiences of care.
  • We found medical home providers increased from 8.2% in 2010 to 81.1% in 2012.
  • We observed a 7-fold increase in adoption of 8 out of 9 structural measures of the medical home.
  • However, we observed no association of medical home implementation on 5 domains of patient care experiences.
  • We need to understand which medical home elements impact patient-centered care.
Strong primary care systems with services dedicated to providing patient-centered, continuous, comprehensive, and coordinated healthcare may improve patient health outcomes and lower costs.1-4 The patient-centered medical home (PCMH) is a widely adopted healthcare delivery model that seeks to strengthen primary care. As of 2013, the National Committee for Quality Assurance (NCQA) has recognized over 5000 practices as medical home practice sites.5 In addition, practice transformation to the medical home model is being tied to payment by several insurers, including CMS, which is investing millions of dollars into medical home practices to achieve the triple aim—improved patient experiences of care, improved quality of care, and reduced costs.6

However, early evidence on adoption of PCMH has demonstrated limited success in achieving these goals.7,8 Furthermore, there is limited evidence on whether patient experiences of care have improved in this “patient-centered” intervention. Patient experience of care is increasingly being recognized as an important measure of healthcare quality, as patient-centered care is associated with improved patient satisfaction, adherence to physician recommendations, self-management, and health status among individuals with chronic diseases,9-14 as well as rehospitalization rates and mortality among hospitalized patients.15,16 Despite the evidence in favor of providing patient-centered care, few studies have investigated the effect of the medical home model on patient-centered care.

To our knowledge, only 3 studies of adult populations have shown a small but positive effect of medical home adoption on elements of patient care experience17-19 and 2 other studies did not show any effect.20,21 However, these studies have been limited by small patient samples, limited measures of medical home intervention, and lack of evidence on how well the medical home principles are being implemented across practice sites or on the “dose” of the medical home intervention.

To address several of these challenges, our study uses data from one of the largest national experiments with medical home adoption to date—medical home adoption by the Veterans Health Administration (VHA). The VHA began implementation of the medical home model in April 2010, which it called the Patient Aligned Care Team, or PACT, initiative. The VHA dedicated over $1 billion nationally to PACT implementation.

The PACT initiative’s main goals for primary care are for it to become more comprehensive, coordinated, and patient centered.22 While similar in focus to NCQA medical home recognition, that tool, in several areas, may not be appropriate for the VHA setting. In fact, the VHA has been a leader in several NCQA medical home domains, such as health information technology infrastructure, electronic prescribing, patient registries, and quality performance measurement.23,24 Thus, a major focus for evaluating the PACT initiative has been on how effectively these resources are being implemented.25

To measure the effect of this implementation on patient experience of care, we used a mixed-methods approach, linking data from a series of structured interviews with a staff of more than 50 primary care sites on the extent and success of PACT implementation with data on patient experience of care for more than 30,000 veterans.



To examine the effect of PACT implementation on patient experience of care, we used 2 sources of variation: the timing and the effectiveness of PACT implementation across study sites. In doing so, we measured the impact of having a PACT primary care provider (PCP) on a patient’s experience of care, and the impact of how effectively a clinic has implemented the PACT model on that same experience. Using a repeated cross-sectional design, we conducted patient-level analyses, with patients clustered within PCPs and sites of care, to test whether changes in healthcare delivery in the VHA under the PACT transformation led to changes in patient experience of care.

Study Population

Our study was based in a large mid-Atlantic region of the VHA (Veterans Integrated Service Networks [VISN] 4), which includes 56 primary care sites providing care for more than 300,000 veterans. Our study cohort included patients who responded to the Survey of Healthcare Experiences of Patients (SHEP) between July 2010 and October 2012 within VISN 4. SHEP is mailed monthly to a random sample of veterans with an outpatient visit in the previous 30 days, stratified by clinic site and physician type (primary care vs specialist).26 The national response rate for the outpatient SHEP in the 2010 survey was 53.2%.27


Main independent variables. Our independent variables are derived from detailed interview-based qualitative data conducted in VISN 4 on PACT implementation. Below, we have included a detailed overview of the 3 methods we used to measure PACT implementation. A full description of the mixed-methods methodology and interview guides used to derive each independent variable has been published previously.28

Measure of timing of PACT implementation. The first measure of PACT implementation was based on the dates that each PCP in the VISN became a PACT provider. We created a binary variable that equaled 1 when a provider became a PACT provider, and 0 before. Providers were considered to be PACT providers once they had started the PACT training process.

Measure of structural change to support PACT implementation. The second measure of PACT implementation measured whether and when specific structural changes in primary care delivery were made. We conducted site visits and structured interviews with key informants at each site with the goal of identifying key structural elements of PACT implementation. Key informants were the persons at each site charged with day-to-day responsibilities related to PACT implementation. In cases where the initial contact was unable to answer all of the questions, we identified a second contact.

Structured interviews were based on an interview guide asking about structural changes to support PACT implementation in the following 10 areas: 1) accessing and using data for quality improvement; 2) care management of high-risk patients; 3) nurse medication protocols; 4) transitions from the emergency department; 5) transitions from the hospital; 6) alternatives to single-provider face-to-face visits; 7) changes to enhance access; 8) multidisciplinary teams; 9) team communication and functioning; and 10) using patient-centered methods (see eAppendix 1, available at, for interview guide). Five sets of interviews were conducted at 6-month intervals over the 2.5-year period of this study (July 2010 to December 2012 [the end month for the data analyzed]).

We summarized the interview data by creating binary variables for 9 of the 10 structural changes, indicating whether the site used any of the specific structural changes in each 6-month period—we did not include responses to queries about accessing and using data for quality improvement as respondents were often confused by this question. For example, in asking about changes to support enhanced access, we created a variable equal to 1 if a clinical site answered “yes” to any of the following questions in each time period: Are any strategies in place for enhanced access? Are scheduling scrubbing methods in place? Are you extending visit intervals when appropriate? Are you using any other methods to enhance access?

Measure of the overall quality of PACT implementation. Finally, we created one scale variable measuring the overall quality or effectiveness of PACT implementation at each site. Based on the responses to the questions on implementation of the structural measures, the interviewer was asked to rate the effectiveness of the implementation on a 5-point Likert scale ranging from 0 (if a particular structural change had not been made) to 4 (if fully implemented) for each of the 10 measuresconsisting of the 9 structural measures (after dropping the question on data access) and a measure of support from leadership. We then summed these scale ratings across the 10 questions, resulting in a summary score with a range of 0 to 40. Previous factor analysis demonstrated that the 10 items function as a summative scale with Cronbach’s alpha for the 10 items being greater than 0.75 in 4 time periods.

Dependent Variables

Our primary outcome variables include 5 measures of patient care experience: how well doctors/nurses communicate, rating of personal doctor/nurse, getting needed care, overall rating of Veterans Affairs (VA) healthcare, and getting care quickly. We used a standardized method to aggregate and dichotomize SHEP responses (eAppendix 2). For example, a survey respondent was asked the following questions: “A personal doctor or nurse is the one you would see if you need a checkup, want advice about health problem or get sick or hurt. Do you have a personal VA doctor or nurse?” (Response options: yes, no); and “Using any number from 0 to 10, where 0 is the worst personal doctor/nurse possible and 10 is the best personal doctor/nurse, what number would you use to rate your personal VA doctor/nurse?” The respondent was counted only if they had a personal VA doctor or nurse. Next, we created a variable equal to 1 if the respondent gave a score of 9 or 10. 

We analyzed SHEP survey responses from July 2010 through fiscal year 2012 in the VHA, which ended in September 2012. In the VHA, patients are assigned a primary care provider at the time of enrollment. This data was linked to PACT implementation data by linking the SHEP survey to corresponding PACT data based on the provider and clinic site and the date of the encounter.


For each SHEP survey respondent we obtained age, sex, ethnicity, and race from the self-reported survey data. We linked the respondent’s zip code with 2012 Census American Community survey data to obtain the median household income. In addition, we used the RiskSmart Diagnostic Cost Group (DCG) files at the VA Austin Information Technology Center from the same fiscal year as the SHEP survey date to account for illness severity. All patient cohort data and covariates are listed in Table 1.

Statistical Analysis

We conducted patient-level analyses, with patients clustered within PCPs, using linear probability models to test whether changes under PACT transformation were associated with changes in patient experience of care. We used the following general form to test our hypotheses:

Outcomei,j,t = αPACTj,t +Xi + βPCP + εi,j,t

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