Lena M. Chen, MD, MS

Aiming to encourage care coordination and cost efficiency, the Center for Medicare & Medicaid Innovation (CMMI) launched the Bundled Payments for Care Improvement (BPCI) initiative in 2013. To help gauge the program’s potential impact and generalizability, we describe early and current participants.

 We examined the cross-sectional association between BPCI participation and providers’ structural and cost characteristics.

 Using data from October 2013 and June 2014, we quantified changes in BPCI participation. We described structural differences between participating and nonparticipating hospitals using 

 tests, and we used the Cochrane-Armitage test to assess whether participants were more likely be in higher 90-day episode cost quintiles than their peers at baseline (2009-2010).

Overall (risk-bearing and non—risk-bearing) participation in BPCI increased from about 400 in October 2013 to more than 2000 in June 2014—attributable, in part, to Model 2, the most comprehensive of the 4 models offered by CMMI for provider participation. Model 2 hospitals increasingly resemble eligible but nonparticipating hospitals. For the most commonly chosen condition of hip replacement, Model 2 hospitals were not costlier than their peers. Hospitals used to make up 97% of Model 2 participants, but physician practices now comprise a substantial number of Model 2 participants. However, most BPCI participants have not yet begun to bear financial risk. Risk-bearing Model 2 hospitals are a smaller and less representative group, with higher baseline costs for hip replacement than their peers.

Growing participation in BPCI suggests strong interest in bundled payments. The long-term impact of BPCI will depend on CMMI’s ability to persuade interested but non—risk-bearing participants to bear risk.

	Growing and more representative enrollment in Medicare’s Bundled Payments for Care Improvement (BPCI) initiative suggests strong interest in bundled payments. The long-term impact of BPCI will depend on the results of risk-bearing participants.

Episode-based bundled payments are one of several proposed healthcare payment reforms aimed at encouraging care coordination, quality improvement, and cost efficiency.

 Historically, hospitals, physicians, and post acute care providers have been paid separately for services occurring during and after hospital admissions. With bundled payments, a fixed lump sum payment is shared among all caregivers, who also share savings when actual expenditures fall below the bundled payment amount. Based on data highlighting wide variation in episode payments across hospitals with many common conditions,

 some estimate that implementation of bundled payments around hospitalizations could save more than $30 billion annually in the Medicare program alone.

A new program by the Center for Medicare & Medicaid Innovation (CMMI) provides early insights about the future prospects of bundled payments. The Bundled Payments for Care Improvement initiative (BPCI) was launched in 2013, with CMMI offering providers 4 models for participation.

 Model 1 includes Part A services for the index hospitalization alone, and thus most closely resembles current fee-for-service payment. Model 2 is the most comprehensive of the 4, encompassing Part A and Part B services for the index hospitalization, readmissions, and all other post acute care. Model 3 includes only post acute care, and Model 4 includes both the index hospitalization and any readmissions.

Although the end results of the BPCI program will not be available for some time, we provide an overview of the program’s general parameters and describe the characteristics of its participants in order to help gauge the program’s potential impact and generalizability. More specifically, we describe national patterns of participation in the BPCI program, as well as the association between participation and providers’ structural and cost characteristics. 

CMMI began accepting applications from hospitals and other providers for the BPCI initiative in 2011. The first participants began bearing financial risk in April 2013 (Model 1) and October 2013 (Models 2-4). CMMI briefly accepted new applications in late 2013 (Model 1) and early 2014 (Models 2-4), with many of these participants not yet having begun risk-bearing (ie, subject to financial gains and losses in the program). Program participants first enter a non—risk-bearing period preparing for implementation (Phase 1), followed by the start of a 3-year risk-bearing period (Phase 2), which is staggered among participants. Even for individual participants, risk-bearing may be rolled out over time; a BPCI participant may begin bearing risk for some of their selected conditions (ie, partial risk-bearing) before doing so for all of their selected conditions (ie, complete risk-bearing).

To describe program participation, we used BPCI participant lists from October 2013 (defined as early participants) and June 2014 (defined as current participants). Data on BPCI participants were first made publicly available in October of 2013.

 Because Model 3 focuses exclusively on post acute care, we examine Models 1, 2, and 4—the 3 models that included the index hospitalization and were open to acute care hospitals.

To describe the structural characteristics of BPCI hospitals versus non-BPCI hospitals, our sample included acute care hospitals eligible for program participation. We utilized the American Hospital Association Annual Survey 2012.

 We defined rural hospitals as those in areas with a population less than 10,000 and we defined teaching hospitals as major or minor (ie, members of the Council of Teaching Hospitals, hospitals with a medical school affiliation, or hospitals with residency or internship training approved by the Accreditation Council for Graduate Medical Education or the American Osteopathic Association). When examining Model 2 hospitals (BPCI participants vs nonparticipants), we defined nonparticipants as all acute care hospitals not participating in Model 2 of BPCI as of June 2014, identified in the 2010 version of the 100% Medicare Provider Analysis and Review File, excluding the Model 2 BPCI hospitals, Maryland hospitals, and federal hospitals.

Given concerns that providers might “cherry-pick” high-cost conditions for performance measurement (because it may be easier to lower costs for conditions with baseline costs above benchmark), we performed a sensitivity analysis of baseline costs of care for hip replacement—the most popular condition chosen. We describe the distribution of Model 2 BPCI hospital participants who selected hip replacement, across national cost quintiles. These cost quintiles were created by ranking all hospitals (all BPCI eligible nonfederal acute care hospitals that had at least 10 hip replacement cases per year and were not located in Maryland&mdash;which is exempt from the Inpatient Prospective Payment System and Outpatient Prospective Payment System&mdash;with payment rates set by the state) by their average 90-day risk-adjusted, price-standardized, reliability-adjusted total payments, and then dividing this into quintiles. Because almost no physician practices are risk-bearing yet, we did not conduct a similar analysis for hospitalized patients attributed to BPCI physician practices (vs non-BPCI physician practices).

For this work, we utilized Part A and B Medicare claims data from 2008 to 2011. We restricted our sample to acute care hospitals that cared for fee-for-service Medicare beneficiaries 65 years or older who underwent hip replacement (

International Classification of Diseases, 9th revision, Clinical Modification

 codes 81.51 or 81.52, and diagnosis-related group codes 469 or 470) between 2009 and 2010. We included no more than 1 index admission per year per beneficiary. We price-standardized total episode payments from the procedure date through 90 days postdischarge, using a previously described methodology.

 We risk-adjusted payments using age, sex, race, admission acuity, and the 29 Elixhauser comorbidities.

This study was declared not regulated by the University of Michigan Institutional Review Board. We conducted all analyses with SAS version 9.3 (SAS Institute, Cary, North Carolina).

The BPCI program has grown rapidly since its inception. Only 171 acute care hospitals were participating in Model 2 when the earliest participants began risk-bearing in October 2013 (

). Even fewer hospitals were participating in Models 1 and 4. Participation rose sharply after CMMI accepted new applications in early 2014. The number of Model 2 participants increased more than 3-fold. Even risk-bearing participation grew substantially during this same time period. In contrast, the number of Model 1 and 4 participants remained low—and in the case of Model 4, dropped.

Physician groups make up a growing and sizable proportion of BPCI participants (

). While physician practices comprised just 3% of all Model 2 participants in October of 2013, they now make up many of the Model 2 participants. Most practices specialize in orthopedic surgery, hospital medicine, cardiology, or emergency medicine; the growth in physician groups has outpaced that of hospitals. Nevertheless, almost no physician practices have begun risk-bearing. About 10% of all eligible acute care hospitals are currently part of Model 2.

Model 2 hospitals (risk-bearing + non—risk-bearing) differ from nonparticipating hospitals (Table). For example, these hospitals are more likely to be large (500 beds or more) and to be teaching hospitals. Hospitals in the northeast are substantially overrepresented, while those in the south are underrepresented. Participating hospitals are also more likely to be nonprofit than their eligible nonparticipating peers. Risk-bearing Model 2 hospitals are an even less representative group than non–risk-bearing hospitals.

BPCI hospitals and physician groups in Model 2 chose from a menu of 48 eligible clinical conditions for performance measurement. These 48 episodes make up approximately three-fourths of all admissions at acute care hospitals for older fee-for-service Medicare beneficiaries. However, most early entrants limited their participation to a narrow subset of these conditions (Table). For example, nearly half of early Model 2 participants selected only 1 or 2 conditions: the most popular choices were major lower extremity joint replacement (selected by 86% of Model 2 participants), congestive heart failure (60%), coronary artery bypass grafting (CABG) (44%), and chronic obstructive pulmonary disease/asthma (43%). Later entrants were more likely to choose all 48 conditions. For example, about half of the Model 2 orthopedic practices chose all of the nonorthopedic conditions offered, in addition to all of the orthopedic conditions. When participants did limit themselves to a smaller number of conditions, orthopedic and cardiac conditions were favored.

Among current risk-bearing Model 2 hospitals that selected the most commonly chosen condition of hip replacement, more than 5 times as many Model 2 hospitals were in the highest compared with the lowest cost quintile (

). In contrast, the baseline costs of all current Model 2 hospitals (risk-bearing + non—risk-bearing) resemble that of nonparticipating hospitals.

We found that provider participation in the national bundled payment initiative for Medicare beneficiaries has grown substantially in the past year, with participation tripling for the most comprehensive model (Model 2). Although this growth is comprised primarily of non—risk-bearing participants, the number of risk-bearing providers has also increased. Physician practices—especially orthopedic practices&mdash;have increasing interest in the program, as evidenced by the rising number of physician practices in the BPCI’s non–risk-bearing phase. The clinical scope has broadened as well. Whereas many early participants selected 1 or 2 conditions, many current entrants have selected all 48 conditions offered.

Bundled payments are 1 of several “alternative payment models” that were highlighted by HHS Secretary Sylvia Burwell, when she described the federal government’s efforts to move toward paying for value.

 The size of the BPCI program dwarfs prior bundled payment initiatives, and is comparable in size to other CMMI payment reforms such as the Medicare Shared Savings Program (with 343 accountable care organizations [ACOs] as of January 2014). CMMI permits providers to enroll in multiple payment reform initiatives simultaneously (eg, BPCI, Next Generation ACOs). Of note, we found that BPCI hospitals share a number of characteristics with Medicare ACOs.

BPCI builds upon earlier and ongoing efforts around bundled payments. As early as 1991, the Medicare Participating Heart Bypass Center Demonstration tested bundled payments with CABG at 7 hospitals.

 This was followed by efforts to implement bundled payments in other settings or for other populations; for example, Medicare’s Acute Care Episode Demonstration, which ran from 2009 to 2012, targeted orthopedic and cardiac conditions in 5 hospitals or health systems.

 The Geisinger ProvenCare program started with CABG in 2006 and now includes several other conditions and procedures,

 and the ongoing PROMETHEUS payment model offers many different bundles of care.

These earlier efforts have highlighted the difficulty of implementing bundled payments,

 and thus, the rapid growth of BPCI participation is somewhat surprising. Administrative and logistical challenges to bundled payments include establishing provider networks that share and distribute risk, constructing the legal and regulatory framework to support these arrangements, and modernizing information and billing systems to accommodate episodes of care. Recent data suggest that these are continuing challenges. For example, a recent effort to establish bundled payments for orthopedic procedures in California broke down during the implementation phase, with disagreements such as how a bundle should be defined.

 In contrast, CMMI delineated what services would be included in a bundle, had an established risk-adjustment methodology, and designated benchmarks for episode costs; it is too early to tell though whether or not these aspects of the program will be enough to minimize implementation challenges. Finally, it is possible that many of BPCI’s current participants will drop out of the program prior to beginning to bear risk.

The rising interest of physician groups is another notable finding from our descriptive work, and one that makes sense given the central role that physicians play in an episode of care. For example, post acute care makes up a substantial proportion of total episode costs for major joint replacement.

 While orthopedic surgeons may not dictate length of stay in a skilled nursing facility (SNF), they are able to influence which patients are discharged with the less-costly option of home health (vs an SNF). Similarly, hospitalists help decide which patients require sub-acute care on discharge, and may help triage patients presenting for readmission. Furthermore, some hospitalist groups have assumed the care of patients in skilled nursing facilities in addition to acute care hospitals. Guidelines for BPCI encourage participation from physician groups, as only those inpatients cared for by a participating physician are attributed to participating physician groups. How BPCI participants will distribute risk among the various providers that share in a patient’s care (eg, attending physician, consulting physician, acute care hospital, post acute care provider) remains to be determined, however.

Although participating providers increasingly chose a wide range of conditions, the most popular conditions remain orthopedic and cardiac. This may reflect the confidence gained from prior bundled payment initiatives around these same conditions (eg, the ACE Demonstration, Geisinger’s ProvenCare). In addition, unlike many chronic medical conditions, procedures have discrete start and end points, lending themselves to episodes of care that are measured in days rather than years. Finally, surgical inpatients such as those undergoing a hip replacement, are on average more clinically homogeneous then medical inpatients. CMMI offered risk corridors to mitigate the unexpected effects of outliers, but providers may remain concerned about the inability to reliably predict the clinical course for patients with multiple comorbidities. 

Any interpretation of our results should account for the fact that BPCI is an ongoing program. For example, as of June, most Model 2 participants were still in the non—risk-bearing preparatory period (Phase 1), with the 3-year risk-bearing period (Phase 2) slated to begin by January 2015. It is possible that a substantial number of participants will drop out of the program before they begin to bear financial risk. Between October and June, one-fifth of Model 2 enrollees dropped out. It is also possible that fewer conditions will be selected for the risk-bearing phase of the program. Nevertheless, we believe that the magnitude of the trends— toward greater participation and more physician group involvement for a wider range of conditions&mdash;make these results important to note. Moreover, while we focused on those models that were open to acute care hospitals, the same general patterns were found among Model 3 (post acute care bundling) participants.

Interest in the national Medicare bundled payment program is growing, as evidenced by rising and increasingly diverse provider participation for a broad array of clinical conditions. With increased participation, the potential for generalizability of the program’s eventual outcomes has increased as well. However, the magnitude of BPCI’s long-term impact will depend on the still uncertain results of risk-bearing participants; success by a large risk-bearing group would make it easier for CMMI to convince other providers to join the program.

The authors thank Haiyin Liu, MA; Mary Oerline, MS; and Anne Cain-Nielsen, MS, for the analytic support they provided. They were compensated for their work.

 Division of General Medicine, Department of Internal Medicine (LMC) and Center for Healthcare Outcomes & Policy (LMC) and Institute for Healthcare Policy and Innovation (LMC), University of Michigan, Ann Arbor, MI; VA Ann Arbor Healthcare System (LMC), Ann Arbor, MI; Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth (EM, JDB), Lebanon, NH; National Bureau of Economic Research (EM), Cambridge, MA.

 This work was supported by funding from the National Institute on Aging (Grant No. P01AG019783). This work was also supported by a Career Development Grant Award (K08HS020671) from the Agency for Healthcare Research and Quality.

 Dr Meara received a grant for the preparation of this manuscript from the National Institute on Aging (P01AG019783). Dr Birkmeyer has equity interest in ArborMetrix, a company that profiles hospital quality and episode cost efficiency; the company played no role in the manuscript. Dr Chen reports no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

 Concept and design (LC, EM, JDB); acquisition of data (JDB); analysis and interpretation of data (LC, EM, JDB); drafting of the manuscript (LC); critical revision of the manuscript for important intellectual content (LC, EM, JDB); statistical analysis (LC); obtaining funding (EM, JDB); administrative, technical, or logistic support (JDB); and supervision (EM, JDB).

 Lena M. Chen, MD, MS, University of Michigan Division of General Medicine, North Campus Research Complex, 2800 Plymouth Rd, Bldg 16, Rm 407E, Ann Arbor, MI  48109-2800. E-mail: lenac@umich.edu.

1. Hackbarth G, Reischauer R, Mutti A. Collective accountability for medical care--toward bundled Medicare payments. 

2. Hussey PS, Sorbero ME, Mehrotra A, Liu H, Damberg CL. Episode-based performance measurement and payment: making it a reality. 

3. Bundled Payments for Care Improvement initiative fact sheet. CMS website. http://www.cms.gov/Newsroom/MediaReleaseDatabase/Fact-Sheets/2014-Fact-sheets-items/2014-01-30-2.html. Published January 30, 2014. Accessed May 26, 2014.

4. Cutler DM, Ghosh K. The potential for cost savings through bundled episode payments. 

5. Bundled payments for care improvement (BPCI) initiative: general information. CMS website. http://innovation.cms.gov/initiatives/bundled-payments/. Updated August 20, 2015. Accessed October 4, 2015.

6. Calsyn M, Emanuel EJ. Controlling costs by expanding the Medicare acute care episode demonstration. 

Articles

Expanding and more representative participation in Medicare's Bundled Payments for Care Improvement initiative suggests potential for large impact, pending the results of risk-bearing participants.

Medicare's Bundled Payments for Care Improvement Initiative: Expanding Enrollment Suggests Potential for Large Impact

Despite broad support for the patient-centered medical home (PCMH), the implications of PCMH implementation efforts that require that participants have some degree of PCMH readiness at baseline are unclear. Therefore, we sought to examine the association among PCMH readiness, quality, and the care of vulnerable patients.

 We conducted a cross-sectional study of adult visits to a nationally representative sample of US office-based primary care physicians in 2007 and 2008.

Using National Committee for Quality Assurance criteria, we determined whether or not a visit occurred at a PCMH-ready practice. We used 

 tests and multiple linear regression to measure the association between PCMH readiness and performance on 9 validated outpatient quality indicators.

Among 12,235 visits to general practitioners and 5123 visits to general internists, 73% occurred at practices that were PCMH-ready. Visits by patients with 3 or more chronic medical conditions were more likely to occur at ready practices (

= .001). Visits by patients that were poor or minority were equally likely to occur at ready and unready practices. Performance at ready practices was higher for 3 of 9 quality indicators related to chronic disease management and preventive counseling (

 = .031 [beta-blocker or diuretic prescribed for hypertension]; 

Implementation efforts that encourage the enrollment of practices most ready for the PCMH could improve the quality of care for complex patients without exacerbating socioeconomic disparities in access to care.

	Despite substantial support for the patient-centered medical home (PCMH), the implications of PCMH implementation efforts that require that participants have some degree of PCMH readiness at baseline are unclear. Our data suggest that PCMH program design that encourages the enrollment of practices most ready for the PCMH could improve the quality of care for complex patients without exacerbating socioeconomic disparities in access to care.

Empirical work provides mixed evidence that implementation of the patient-centered medical home (PCMH) will improve the quality and costs of healthcare. Some demonstration projects have shown that the PCMH is associated with increased use of preventive services,

 and reductions in emergency department visits and/or hospital admissions.

 Evidence that the PCMH positively impacts clinical processes of care and patient outcomes is weaker and more inconsistent.

 However, there is little to suggest that the PCMH will harm patients, and many policy makers hope that the PCMH will eventually improve care. In this context, efforts are under way to disseminate the PCMH,

 including a multi-state pilot initiative from CMS.

While many PCMH programs are voluntary, a substantial number require that participants have a baseline level of medical home readiness.

 Other programs require that practices attain a minimum level of medical home readiness within 12 to 18 months of program entry.

 Such choices about PCMH program design would seem to favor the enrollment of practices that are PCMH-ready (or nearly ready). If there is no differential access to care between PCMH-ready and PCMH-unready practices, then current program design makes sense, as PCMH-ready practices also tend to be multi-specialty practices with substantial visit volume.

 However, if practices that are least ready for the PCMH also disproportionately treat vulnerable populations, then overlooking such practices could initially exacerbate existing disparities. In this case, a purely voluntary approach without any prerequisites for PCMH program enrollment, combined with improvement metrics tailored to a practice’s baseline PCMH readiness, might be preferred.

Since no prior research has investigated the strengths and weaknesses of either strategy, we used data from a nationally representative sample of visits by adults to general practitioners and internists to examine the characteristics of visits to practices stratified by their medical home readiness. We addressed 2 questions: 1) What types of patients are more likely to be seen in visits to practices that are more PCMH-ready? and 2) Is there an association between PCMH readiness and quality of care delivered?

For our study, we used data from the 2007 and 2008 National Ambulatory Medical Care Survey (NAMCS), which is an annual multistage probability sample of outpatient visits to randomly selected, nonfederally employed, office-based physicians in the United States.

 Practices are sampled during a 1-week time frame, so the sample is unlikely to include 2 visits by the same patient. In 2007 and 2008, data were collected from 32,778 and 28,741 office-based visits, respectively. The data files contain practice-, physician-, patient-, and visit-level characteristics. Weighted estimates from them are considered representative of all US outpatient visits. We focused on adult (ie, aged ≥18 years) ambulatory visits occurring at general and internal medicine practices.

We assessed the medical home readiness of each practice where a visit occurred using previously described methods.

 In brief, we mapped practice-level characteristics reported by physicians in the NAMCS to the 2011 National Committee for Quality Assurance (NCQA) PCMH certification standards (see 

 The NCQA assigns a point value to each of the 30 elements (including 6 “must-pass” elements) outlined in the PCMH standards. We mapped 14 elements (including 3 “must-pass” elements), representing 6 of the medical home standards, to items collected in the NAMCS. The 6 PCMH standards were: 1) enhanced access and continuity (8 points); 2) identifying and managing patient populations (16 points); 3) planning and managing care (7 points); 4) providing self-care support and community resources (6 points); 5) tracking and coordinating care (6 points); and 6) measuring and improving performance (13 points).

To calculate a practice’s PCMH readiness score, we used the NCQA scoring system

 to derive a cumulative point total for each practice by summing across all of the passed elements. Based on the 14 measured elements, the maximum point total was 56 when no missing data were present. In the case of missing data, the maximum point total was based on all non-missing data (see 

). The practice-level readiness score was then calculated by dividing the cumulative point total by the total number of available points. The resulting readiness score is expressed as a percentage and can be interpreted as the percentage of measureable NCQA elements that the practice (where the visit occurred) possesses. Based on the NCQA’s levels of medical home recognition, we categorized practices into 2 groups: unready (<35 %) and ready (≥35 %) for medical home implementation.

To measure the quality of care provided at each visit, we first determined whether or not the visit met any of 6 validated medication quality indicators.

 These indicators were: 1) angiotensin-converting-enzyme inhibitor/angiotensin receptor blocker for congestive heart failure (CHF); 2) beta-blocker for CHF; 3) diuretic or beta-blocker for hypertension; 4) beta-blocker for coronary artery disease; 5) statin for hyperlipidemia; and 6) the prescribing of no inappropriate medications during the visit. We coded these indicators using diagnosis and medication codes in NAMCS, as well as responses to questions about the presence or absence of specific chronic diseases. For each visit, the treating physician can list up to 3 diagnoses (primary, secondary, or tertiary) on the patient record form. In addition to diagnoses, the patient record form lists up to 8 original and 8 generic medications prescribed by the treating physician.

In addition to the 6 medication quality indicators, we examined 3 previously validated prevention and counseling quality indicators: diet and exercise counseling (yes/no responses in NAMCS), and blood pressure monitoring at any general medical exam visit.

 The NAMCS asks respondents to enter blood pressure readings (if blood pressure was measured at the visit). In defining a general medical exam, a visit for preventive care was defined as a general medical exam, but a follow-up visit was not considered a general medical exam.

We first described the characteristics of visits occurring at practices that were PCMH-ready versus unready. We used Student’s 

 tests to test for differences in visit characteristics. We also used 

 tests to determine whether the percentage of visits meeting each of the 9 quality-of-care indicators differed between ready and unready practices.

We applied appropriate sampling weights, clusters, and stratification to correct our standard error estimates for the complex survey design. We performed all analyses using Stata version 11.0 (StataCorp LP, College Station, Texas). The University of Michigan Health Sciences and Behavioral Sciences Institutional Review Board determined that this study was exempt from its oversight.

We examined 17,358 visits, representing 341 million adult outpatient visits to general practitioners and internists during the study interval. Nearly three-fourths occurred at practices ready for the PCMH (

). Compared with patients seen at visits to unready practices, patients seen at visits to ready practices were more likely to have 3 or more comorbid conditions (25% of visits to PCMH-ready practices vs 17% of visits to other practices; 

= .001). However, visits to both types of practices were similar with respect to race/ethnicity and socioeconomic status. Practice characteristics of both types of visits were also similar except for physician ownership and rural location, although this latter finding was not statistically significant (

 = .058). In sensitivity analyses with broader categories, 2 findings became statistically significant. When we compared visits in the South with all other practices, visits in the South were less likely to occur at PCMH-ready compared with unready practices (36% vs 52%; 

 = .049). Similarly, when we compared privately insured visits with all other visits, privately insured visits were more likely to occur at PCMH-ready compared with unready practices (57% vs 51%; 

Visits to PCMH-ready practices were generally more likely to meet the medication and counseling/screening quality indicators, although this difference reached statistical significance for only 3 indicators (

). For example, 18% of visits to ready practices included diet counseling, compared with 12% of visits to unready practices (

 = .018). Similarly, exercise counseling occurred at 14% of visits to ready practices, but at only 5% of visits to unready practices (

 <.001). For medication quality indicators, at visits to ready practices (vs unready ones), 41% (vs 32%) of patients were prescribed a diuretic or beta-blocker for hypertension (

Our study had 2 principal findings. First, while visit rates among racial minorities and patients from poorer neighborhoods did not differ based on a practice’s PCMH readiness, those most ready for this new care model have a disproportionate share of patients with multiple comorbid conditions. Second, ready practices, at baseline, delivered higher or equivalent quality care in all dimensions.

Our study must be considered in the context of prior work on the value of health information technology (IT) tools, which the NCQA PCMH standards emphasize heavily. Many of the NAMCS items that we evaluated mapped to the NCQA elements that capture health IT tools. To the extent that the use of health IT improves chronic disease management,

 our finding that visits by patients with multiple chronic conditions are more likely to occur at ready practices is reassuring. Second, evidence is still being gathered to assess whether medical homes deliver better-quality care across multiple domains (eg, patient satisfaction, clinical processes of care, patient outcomes).

 While a fully developed medical home is clearly more than the sum of its parts, our observation of modestly higher quality among visits to ready practices provides 1 piece of evidence to suggest that adoption of PCMH components may be beneficial to care quality.

Our study has several important limitations. First, we ascertained PCMH readiness by matching survey responses about a practice’s infrastructure and processes of care to 1 standard of medical home readiness: the NCQA PCMH standard. However, prior studies have utilized this method,

 and the NCQA standard has been widely adopted and was developed with input from organizations such as the American College of Physicians.

 Second, because the NAMCS questions are not based on the NCQA PCMH standard, we could not perfectly match NCQA elements to NAMCS questions, nor could we match all NCQA elements to an NAMCS question. This may have resulted in some misclassification of ready practices as unready or vice versa, but the directionality of this bias is difficult to ascertain. Prior work has found the NAMCS-NCQA matching approach robust to missing data (see eAppendix 2). Third, our measures of quality were limited by the cross-sectional nature of the NAMCS survey. However, the quality measures have been validated,

 and one of the criteria for constructing these measures with NAMCS data was that the measures have a high likelihood of correlating with improved patient outcomes.

 Nevertheless, we cannot make conclusions about causality between medical home readiness and quality of care. Fourth, it is possible that better scores on exercise and diet counseling measures for PCMH-ready versus unready practices reflect better documentation at PCMH-ready practices, since many of the NCQA measures that we mapped to NAMCS rely on health IT tools.

In spite of these limitations, our findings on care quality have implications for PCMH implementation strategy. To date, policy makers have often tested these models in practices most ready for transformation into a fully recognized PCMH.

 For example, 2 large CMS programs provide incentives to practices that already have some medical home capabilities. Criteria for inclusion in CMS’ Comprehensive Primary Care Initiative include having some level of medical home recognition. CMS’ Multi-payer Advanced Primary Care Practice Demonstration provides a monthly care management fee for beneficiaries receiving primary care from a medical home. Moreover, programs often require that participating practices attain a certain level of medical home readiness as early as 1 year after enrollment. This too would seem to favor the participation of practices that are PCMH-ready (or nearly ready). Our data support these efforts, as they suggest that such a strategy will not leave out a disproportionate number of visits by poor or minority patients, or by patients with multiple comorbidities.

While it is reasonable to design PCMH programs that may attract the most PCMH-ready practices first, policy makers should also take steps to ensure that the least ready practices are not left behind. This would also shed light on the broader generalizability of results from early implementation efforts at PCMH-ready practices. Future research should examine whether or not PCMH incentives that account for baseline levels of medical home readiness are effective in bringing along the least ready practices. For example, in the Medicare Shared Savings Program, tailored incentives exist for provider organizations at different stages of becoming an accountable care organization

; a similar approach might be utilized to support PCMH adoption by a wide range of practices. Implementation of PCMHs will require as much care as ongoing, related work that assesses the net benefits of PCMH adoption.

Michael Wu assisted with data preparation and was compensated for his work. Preliminary findings were presented as posters at the Society of General Internal Medicine Annual Meeting and the Academy Health Annual Research Meeting, both in 2012.

Division of General Medicine, Department of Internal Medicine, University of Michigan (LMC, AMR), Ann Arbor, MI; VA Ann Arbor Healthcare System (LMC), VA Center for Clinical Management Research (AMR), Ann Arbor, MI; Center for Healthcare Outcomes & Policy, University of Michigan (LMC, DCM, JH), Ann Arbor, MI; Institute for Healthcare Policy and Innovation, University of Michigan (LMC, DCM, AMR, JH), Ann Arbor, MI; Department of Statistical Methods, Institute for Employment Research (JWS), Nuremberg, Germany; Department of Sociology, University of Mannheim (JWS), Mannheim, Germany; Department of Urology, Dow Division of Health Services Research University of Michigan (DCM, JH), Ann Arbor, MI. 

 Dr Chen is supported by K08HS020671, Dr Miller is supported by K08HS018346, and Dr Hollingsworth is supported by K08HS020927, all from the Agency for Healthcare Research and Quality (AHRQ). Dr Rosland is a VA Health Services Research and Development (HSR&D) Career Development Awardee. This material is the result of work supported with resources from the VA HSR&D Center for Clinical Management Research, VA Ann Arbor Healthcare System.

Dr Hollingsworth has received funding from AHRQ. Drs Chen, Sakshaug, Miller, and Rosland report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Concept and design (LMC, JH, DCM, AMR); acquisition of data (JH, JWS); analysis and interpretation of data (LMC, JH, DCM, AMR, JWS); drafting of the manuscript (LMC, JH, DCM); critical revision of the manuscript for important intellectual content (LMC, JH, AMR, JWS); statistical analysis (LMC, JWS); obtaining funding (LMC, JH, DCM, AMR); and supervision (JH). 

 Lena M. Chen, MD, MS, University of Michigan Division of General Medicine, North Campus Research Complex, 2800 Plymouth Rd, Bldg 16, Rm 407E, Ann Arbor, MI 48109-2800. E-mail: lenac@umich.edu.

1. Hoff T, Weller W, Depuccio M. The patient-centered medical home: a review of recent research. 

2. DeVries A, Li CH, Sridhar G, Hummel JR, Breidbart S, Barron JJ. Impact of medical homes on quality, healthcare utilization, and costs. 

3. Jackson GL, Powers BJ, Chatterjee R, et al. Improving patient care. the patient-centered medical home. a systematic review. 

4. Harbrecht MG, Latts LM. Colorado’s Patient-Centered Medical Home Pilot met numerous obstacles, yet saw results such as reduced hospital admissions. 

5. Paulus RA, Davis K, Steele GD. Continuous innovation in health care: implications of the Geisinger experience. 

The characteristics of patients who visit practices that are ready versus unready for the patient-centered medical home differ in important ways.

Lena M. Chen, MD, MS

Articles

Medicare's Bundled Payments for Care Improvement Initiative: Expanding Enrollment Suggests Potential for Large Impact

The Association Among Medical Home Readiness, Quality, and Care of Vulnerable Patients