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The American Journal of Managed Care January 2014
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Patient-Centered Medical Home Transformation With Payment Reform: Patient Experience Outcomes
Leonie Heyworth, MD, MPH; Asaf Bitton, MD, MPH; Stuart R. Lipsitz, ScD; Thad Schilling, MD, MPH; Gordon D. Schiff, MD; David W. Bates, MD, MSc; and Steven R. Simon, MD, MPH
Evidence-Based Guidelines to Determine Follow-up Intervals: A Call for Action
Emilia Javorsky, MPH; Amanda Robinson, MD; and Alexa Boer Kimball, MD, MPH
Electronic Health Risk Assessment Adoption in an Integrated Healthcare System
Diana S. M. Buist, PhD, MPH; Nora Knight Ross, MA; Robert J. Reid, MD, PhD; and David C. Grossman, MD, MPH
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Reina Haque, PhD, MPH; Marianne Prout, MD; Ann M. Geiger, PhD; Aruna Kamineni, PhD; Soe Soe Thwin, PhD; Chantal Avila, MA; Rebecca A. Silliman, MD, PhD; Virginia Quinn, PhD; and Marianne Ulcickas Yood, DSc
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Susan H. Busch, PhD; Andrew J. Epstein, PhD; Michael O. Harhay, MPH; David A. Fiellin, MD; Hyong Un, MD; Deane Leader Jr, DBA, MBA; and Colleen L. Barry, PhD, MPP
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Karen Tu, MD, MSc; Tezeta F. Mitiku, BSc, MSc; Noah M. Ivers, MD; Helen Guo, BSc, MSc; Hong Lu, PhD; Liisa Jaakkimainen, MD, MSc; Doug G. Kavanagh, BSEng, MD; Douglas S. Lee, MD, PhD; and Jack V. Tu, MD, PhD
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Oluseyi Aliu, MD, MS; Gordon Sun, MD, MS; James Burke, MD, MS; Kevin C. Chung, MD, MS; and Matthew M. Davis, MD, MAPP
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Denison S. Ryan, MPH; Karen J. Coleman, PhD, MS; Jean M. Lawrence, ScD, MPH, MSSA; Teresa N. Harrison, SM; and Kristi Reynolds, PhD, MPH
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Patient-Centered Medical Home Transformation With Payment Reform: Patient Experience Outcomes

Leonie Heyworth, MD, MPH; Asaf Bitton, MD, MPH; Stuart R. Lipsitz, ScD; Thad Schilling, MD, MPH; Gordon D. Schiff, MD; David W. Bates, MD, MSc; and Steven R. Simon, MD, MPH
In a pilot patient-centered medical home transformation including Lean quality improvement methodology with payment reform, patient experience was sustained or improved across key domains.
Objective: To examine changes in patient experience across key domains of the patient-centered medical home (PCMH) following practice transformation with Lean quality improvement methodology inclusive of payment reform.

Study Design: Pre-intervention/post-intervention analysis of intervention with a comparison group, a quasi-experimental design. We surveyed patients following office visits at the intervention (n = 2502) and control (n = 1622) practices during the 15-month period before and 14-month period after PCMH Lean transformation (April-October 2009).

Methods: We measured and compared pre-intervention and post-intervention levels of patient satisfaction and other indicators of patient-centered care. Propensity weights adjusted for potential case-mix differences in intervention and control groups; propensity-adjusted proportions accounted for physician-level clustering.

Results: More intervention patients were very satisfied with their care after the PCMH Lean intervention (68%) compared with pre-intervention (62%). Among control patients, there was no corresponding increase in satisfaction (63% very satisfied pre-intervention vs 64% very satisfied post-intervention). This comparison resulted in a statistical trend (P = .10) toward greater overall satisfaction attributable to the intervention. Post-intervention, patients in the intervention practice consistently rated indicators of patient-centered care higher than patients in the control practice, particularly in the personal physician and communication domain. In this domain, intervention patients reported superior provider explanations, time spent, provider concern, and follow-up instructions compared with control participants, whereas control group ratings fell in the post-intervention period (P for difference <.05).

Conclusions: In a pilot PCMH transformation including Lean enhancement with payment reform, patient experience was sustained or improved across key PCMH domains.

Am J Manag Care. 2014;20(1):26-33
Using a quasi-experimental design, we evaluated patient experience following patient-centered medical home (PCMH) practice transformation with Lean enhancement coupled with payment reform.
  • Compared with controls, intervention participants were more likely to report faster speed of registration (P = .04) and provide higher ratings across the personal physician and communication domain (P <.05) after the pilot program.

  • 62% of intervention participants were very satisfied pre-intervention versus 68% post-intervention (P = .004).

  • Future studies are needed to determine if physicians, over time, will thrive under an adjusted salary-based reimbursement system in the PCMH.
The patient-centered medical home (PCMH) has emerged as a patient-focused, quality-driven, cost-effective model to revive primary care. A growing body of literature supports the notion that health systems emphasizing the role of primary care can achieve superior outcomes at a lower cost.1 The PCMH joint principles were developed into a set of best practices by the National Committee for Quality Assurance and have been used as the blueprint for organizational change for many recent PCMH demonstration projects. As preliminary results of PCMH demonstration projects emerge, successes have shown that the PCMH has the potential to reduce cost and improve quality,2,3 although transformation is often challenging4 and short-term declines in satisfaction may be observed following interventions to implement the PCMH model.3 Coupling practice transformation with payment reform may encourage more rapid adoption of the PCMH model, and both private and public payers are starting to invest heavily in such transformation pilots. Lean quality improvement methodology have been incorporated with some PCMH transformations to help facilitate PCMH practice improvements, but did not integrate changes to the current resource-based relative value scale.3 Therefore, if the PCMH model is to be widely utilized, it will be vital to demonstrate that its implementation with concurrent payment reform results in favorable outcomes with respect to quality and cost, as well as patients’ perceptions of care.

Overlooked for many years, patient experience has recently gained increasing prominence. The Affordable Care Act of 2010 calls for novel approaches to increase patient access and enhance patient-centered care in order to improve patient experience and outcomes.5 In general, patient-centered care experiences and outcomes-oriented research are becoming increasingly recognized as a core element of quality healthcare delivery in the United States and other countries.6 In their pursuit of a 3-part aim to improve quality, cost, and patient experience, some health plans and health systems have begun to link measures of patient satisfaction to provider payment.7

Few data have been available regarding the effect of PCMH transformation, with or without Lean quality improvement methodology, on patient experience in the setting of payment reform. To assess the impact on patient experience of transformation of care to a medical home linked with implementation of a new physician reimbursement scheme, we undertook a 3-year quasi-experimental study. We assessed patients’ experience with office visits at a clinic site where these interventions were implemented compared with a control site, both within a high-performing, large, multispecialty group practice. We evaluated the effect of PCMH transformation, including Lean quality improvement methodology, on patient experience with a widely used survey for measuring patients’ experience with outpatient care.


Study Design and Data Collection

Following Cook and Campbell’s classification of quasi-experimental studies, we used an Untreated Control Group Design with Pretest and Posttest.8 In both the experimental and control groups, we conducted cross-sectional surveys of patients’ experiences  with care before and after the implementation of the PCMH model in the experimental site. We studied responses to a mail copy of the medical experience survey developed by Press Ganey (, a widely used, commercially available tool for measuring patients’ experience with outpatient care. All surveys were mailed 1 week after a patient’s encounter. Survey response rates across all clinic sites in the integrated system were 28.1% in 2008, 28.3% in 2009, and 36.6% in 2010. The study protocol was approved by the Partners Healthcare Institutional Review Board.

Setting and Participants

The study was conducted within Harvard Vanguard Medical Associates, a large multispecialty group of 14 practices in eastern Massachusetts, frequently recognized for achieving high scores in quality metrics.9 For this study, we included 2 of the 14 practices: 1 practice that underwent PCMH transformation with payment reform in 2009 and 1 practice,12 miles away with a similar demographic composition, that served as the control. Patients who had at least 1 internal medicine office visit from 2008 through 2010 were stratified by primary care provider. For each provider, surveys were mailed to 200 randomly selected patients, evenly distributed over the course of each of the 3 study years; each patient was surveyed only once per year. Between January 2008 and June 2009 (pre-intervention), a total of 2027 patients completed the survey: 1224 at the intervention site and 803 at the control site. Between July 2009 and December 2010 (post-intervention), we collected 2097 completed surveys: 1278 from the intervention site and 819 from the control site. Intervention and control participants were propensity weighted to adjust for possible case-mix differences between these 2 groups (see the Statistical Analysis section for more detail). 

The Intervention

The intervention was a pilot that started in April 2009 in the internal medicine department of 1 clinic site. It consisted of team restructuring in addition to a series of process improvements including use of Lean reengineering,10 centered around optimizing chronic disease management and reassigning tasks traditionally performed by a physician to medical assistants or nurses. For example, nurses were trained to use medication titration protocols for virtual management of many chronic diseases, shifting simple visits off provider schedules, and medical assistants took over many administrative duties traditionally completed by physicians. Components of the intervention are summarized in Table 1 and are described in greater detail elsewhere.11

Changes in physician payment were structured around the proposal by Goroll and colleagues12 that involved replacing productivity-based (fee-for-service) compensation with a salary and bonus scheme. Each physician’s base salary was determined by their highest earnings over the prior 4 quarters. The pilot salary scheme did not have penalties for low productivity; it also incorporated quality-based bonuses for reaching benchmarks in the Healthcare Effectiveness Data and Information Set performance metrics and for responsiveness to patient communication (via telephone or secure message). Initially, 5 physicians were enrolled in the salary and bonus scheme. By the end of the pilot intervention’s implementation in October 2009, a total of 14 physicians (100% of the internal medicine department) had joined the salary scheme. All physicians at the intervention site remained salaried for the duration of the post-intervention period.

Main Outcomes

The primary outcome was overall visit satisfaction, measured on a 5-point Likert scale. Patients were asked to indicate their “overall rating of care provided” for the survey-tagged visit. Satisfaction was analyzed as a dichotomized variable “very good” versus “good,” “fair,” “poor,” or “very poor.”  Secondary outcomes were drawn from the 5 domains within the Press-Ganey survey that reflect the core principles of the PCMH: enhanced access to care (3 items), visit coordination and care (3 items), physician communication (4 items), and whole-person orientation of care (3 items).13 Responses to items within each domain were measured on the same 5-point Likert scale and dichotomized as described above. Individual survey responses were linked to each patient’s medical record, providing demographic information (age, sex, race, insurance status), healthcare utilization (the number of internal medicine and urgent care visits in the 6 months preceding the surveyed visit), certain medical conditions (hypertension, diabetes, cardiovascular disease), body mass index (dichotomized  as less than 25 kg/m2 vs 25 kg/m2 and greater), and primary care provider characteristics (sex and number of years since hire).

Statistical Analysis

All variables used in the patient experience analysis are dichotomous, so descriptive statistics of patient characteristics and experiences in the control and intervention groups in the pre-intervention and post-intervention periods were calculated using proportions. Our main analysis was to compare control and intervention groups with respect to patient experiences; however, because of possible baseline differences in patient experiences in the control and intervention groups, we compared the post-intervention minus pre-intervention experiences in the control group with the post-intervention minus pre-intervention experiences in the intervention group, commonly referred to as a difference-in-differences analysis.

Propensity score weighting was used to adjust for case-mix differences between patients in the control and intervention groups in the pre-intervention and post-intervention periods.14 In general, propensity score methods permit control for observed confounding factors that might influence both the outcomes (in our study, patient experiences) and the comparison groups (for control and intervention groups in the pre-intervention and post-intervention periods) using a single composite measure. The propensity score attempts to balance patient characteristics between the comparison groups, as would occur in a randomized experiment. The propensity of being in the 4 groups (control and intervention groups in the pre-intervention and post-intervention periods) was calculated using multinomial logistic regression models with all patient characteristics available to us as predictors: patient age, sex, race, insurance status (Medicare or Medicaid vs private insurance), the number of active medications, healthcare utilization (number of internal medicine and urgent care visits in the 6 months preceding the visit referenced in the patient experience survey), and whether the patient had any or all of the following chronic conditions: diabetes, hypertension, heart disease, or overweight.

To calculate propensity-adjusted proportions using the propensity-weighted approach, each patient was weighted by the inverse propensity score of being in their observed treatment group. P values comparing propensity-adjusted proportions across treatment groups were calculated using generalized estimating equations15 to account for physician-level clustering.

For all analyses, a P value of <.05 was considered statistically significant. All analysis was completed using SAS/STAT 9.2 (SAS Institute Inc, Cary, North Carolina).


Between January 2008 and June 2009 (pre-intervention), a total of 2027 patients completed the patient experience survey: 1224 at the intervention site and 803 at the control site. Between July 2009 and December 2010 (post-intervention), we collected 2097 completed surveys: 1278 from the intervention site and 819 from the control site. After propensity weighting on all available demographic and clinical status variables, the intervention and control sites were similar with respect to all baseline variables (Table 2).

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