The Medical Home: What Do We Know, What Do We Need to Know? A Review of the Earliest Evidence on the Effectiveness of the Patient-Centered Medical Home Model
Prepared For: Agency for Healthcare Research and Quality, U.S. Department of Health and Human Services, 540 Gaither Road, Rockville, MD 20850, www.ahrq.gov
Contract Numbers: HHSA290200900019I/HHSA29032002T, HHSA290200900019I/HHSA29032005T
Prepared By: Mathematica Policy Research, Princeton, NJ; Aparajita Zutshi, Ph.D., Deborah Peikes, Ph.D., M.P.A., Kimberly Smith, Ph.D., M.P.A., Melissa Azur, Ph.D. (Mathematica Policy Research), Janice Genevro, Ph.D., Michael Parchman, M.D., David Meyers, M.D. (Agency for Healthcare Research and Quality).
We would like to thank a number of people for their assistance with this paper. The authors of many of the evaluations patiently answered questions about the interventions and their evaluation methods. Kristin Geonnotti at Mathematica Policy Research provided in-depth analyses of some of the papers included in this review. Michael Barr at the American College of Physicians; Robert Reid at Group Health Research Institute; and Randall Brown, Christopher Trenholm, Silvie Colman, Brian Goesling, and Tim Novak at Mathematica provided helpful comments and guidance.back to top
The patient-centered medical home (PCMH, or medical home) aims to reinvigorate primary care and achieve the triple aim of better quality, improved experience, and lower costs. This study systematically reviews the early evidence on the effectiveness of the PCMH.
Of 498 articles on U.S.-based interventions, published or disseminated from January 2000 to September 2010, 14 evaluations of 12 interventions met our inclusion criteria: (1) tested a primary-care, practice-based intervention with three or more of five key PCMH components; and (2) conducted a quantitative evaluation of either (a) a triple aim outcome (quality of care, costs [or hospital use or emergency department use, two major cost drivers], and patient or caregiver experience), or (b) health care professional experience. We describe the interventions, their target populations, and implementation settings, and provide a broad overview of the research approaches used to evaluate these interventions. We developed and applied a formal rating system to identify interventions that have been evaluated using rigorous methods, and synthesized the evidence of effectiveness on each outcome generated by rigorous evaluations. Using these findings, we provide guidance to inform current efforts and structure future evaluations to maximize learning.
The joint principles that first defined the PCMH were released in 2007, and we reviewed evidence through September 2010. Reflecting the time required to evaluate and publish findings on the model, the 12 interventions reviewed here—many of which are often cited in support of the medical home—are best viewed as precursors to the medical home. While these early interventions varied, most essentially tested the addition of a care manager operating from within the primary care practice, rather than a fundamentally transformed practice. Most interventions were evaluated in practices that were part of larger delivery systems and targeted patients who were older and sicker than average. Six of the 12 interventions evaluated at least one outcome using rigorous methods. This rigorous evidence indicates mostly inconclusive results (because of insufficient sample sizes to detect effects that might exist or uncertain statistical significance of results because analyses did not account for clustering of patients within practices); however, we found some favorable effects on quality of care, hospital and emergency department use, and patient or caregiver experience, and a few unfavorable effects on costs. Our review of these early interventions indicates that we need more evaluations of the medical home to assess its effectiveness.
Improving primary care is the key to achieving the triple aim outcomes. Although the PCMH is a promising innovation, rigorous quantitative evaluations and comprehensive implementation analyses are needed to assess effectiveness and refine the model to meet stakeholders’ needs. Findings from future evaluations will help guide the substantial efforts practices and payers need to adopt the PCMH with the goal of achieving the triple aim outcomes.back to top
Chapter 1. Why Evidence on the Effectiveness of the Medical Home Is Important
- The patient-centered medical home (PCMH) is a promising approach to improving primary care delivery.
- The PCMH aims to improve quality, reduce cost, and improve the experience of patients, caregivers, and health care professionals.
- We systematically review the quantitative evidence generated by early evaluations of the PCMH.
- We also distill lessons for future evaluations, to build a better evidence base.
Reinventing primary care is a task that is “far too important to fail” (Meyers and Clancy, 2009) and central to reforming health care delivery. While patient-centered primary care was once the backbone of our health care system, over time the system has become more specialized and technologically sophisticated (Bodenheimer and Pham, 2010), and fewer medical residents are choosing to become primary care physicians (Bodenheimer, 2006). The current health care system, with its incentives for volume over value, produces fragmented care that lacks coordination, patient-centeredness, and proactive population health management (Berenson and Rich, 2010b; Bodenheimer and Pham, 2010; Dentzer, 2010; Rittenhouse, Shortell, and Fisher, 2009; Howell, 2010). Although 93 percent of Americans want one place or doctor that provides primary care and coordinates care with specialists, only half report having such an experience (Schoen, Osborn, Doty, et al., 2007; Stremikis, Schoen, and Fryer, 2011). The PCMH is a promising model that aims to reinvent primary care so that it is “accessible, continuous, comprehensive, and coordinated and delivered in the context of family and community” (American Academy of Family Physicians, American Academy of Pediatrics, American College of Physicians, et al., 2007), and, in so doing, improve the triple aim outcomes of quality, affordability, and patient and caregiver experience.
The medical home concept arose in the 1960s as a way of improving care for children with special needs, and policy interest outside pediatrics grew over time (Kilo and Wasson, 2010). In 2007, primary care physician societies endorsed the “Joint Principles of the Patient-Centered Medical Home” (AAFP, AAP, ACP, et al., 2007). Intrigued by the potential of the PCMH model, major employers, private insurers and State Medicaid agencies across the Nation are currently rolling out pilots and demonstrations of the concept. The Centers for Medicare & Medicaid Services, the Department of Veterans Affairs, and other Federal agencies are also testing the model (http://pcmh.ahrq.gov/page/federal-pcmh-activities). It will likely be many years before results of current evaluations become available. Transforming care will require recognizing and addressing many barriers to change using lessons from these evaluations (Landon, Gill, Antonelli, et al., 2010).
Against this backdrop, decisionmakers must consider whether the current evidence on the model is already strong enough to proceed with widespread adoption, or whether gathering additional evidence is warranted. To contribute to this discussion, researchers at the Agency for Healthcare Research and Quality (AHRQ) and Mathematica Policy Research undertook a systematic review of quantitative evaluations of the medical home model to summarize the evidence on medical home effectiveness as well as identify lessons for future evaluations to generate a solid evidence base to guide health system reform. In addition to the triple aim outcomes, we review the model’s effectiveness on the experience of health care professionals (hereafter referred to as professional experience), since the success of primary care transformation and improvements in care are contingent on the satisfaction and ongoing engagement of health care personnel. This paper provides a more detailed version of papers published earlier by AHRQ and the American Journal of Managed Care (Peikes, Zutshi, Genevro, et al.; 2012a; Peikes, Zutshi, Genevro, et al., 2012b).
Given the relative newness of the PCMH model, and the time it takes to design, implement, and evaluate an intervention and disseminate findings, we were not surprised to find that many of the interventions evaluated to date, and currently cited as evidence in favor of the model, are precursors to the model. Many of these pioneering efforts were undertaken before the recent interest in the medical home and contained many, but not all, of its components.1 To emphasize the difference between the interventions reviewed here and those being tested now, we refer to the ones we review as “PCMH precursors.”
The review limits the synthesis of findings to interventions evaluated using rigorous methods. While much can be learned from rapid-cycle evaluations of small pilots and from evaluations of narrowly targeted interventions, this review intends to fulfill stakeholders’ need for rigorous quantitative evidence on broad medical home-like interventions that test multiple components and examine effects on key outcomes.2 Qualitative evaluations of PCMH implementation can also offer valuable insights into the implementation of these interventions and provide context for generalizing findings; they were excluded from this review, however, because our focus is on outcomes and because existing evaluations rarely documented their implementation experiences in published reports.
Some readers may not consider an evidence review of the PCMH to be necessary because they believe that the evaluations conducted to date, combined with the vast cross-sectional literature on the positive relationship between more primary care and better outcomes, provide sufficient evidence to proceed with widespread adoption of the model. Others may feel that the model is being held to a higher standard than many clinical interventions currently being used without strong evidentiary support. However, we believe that, given the significant investments required to transform practices and revitalize our primary health care system, many decisionmakers are, appropriately, going to demand rigorous evidence of effectiveness.
Historically, rigorous evaluations of a number of promising health care interventions have shown the interventions to be ineffective in achieving their goals. For example, telephonic disease management seemed to address obvious problems in coordination and patient self-management, but a number of randomized trials showed that many programs were ineffective and pointed the way to refining the model to offer better integration with providers, more in-person contact, and careful focusing of efforts to those most likely to benefit (Peikes, Peterson, Brown, et al., 2012c; Brown, Peikes, Peterson, et al., 2012; McCall and Cromwell, 2011; Peikes, Chen, Schore, et al., 2009; Peikes, Peterson, Brown, et al., 2010). Similarly, rigorous evidence regarding the effectiveness of the PCMH model and how best to refine it is critical if it can be used for transforming primary care, especially given the substantial investments the model requires.
This review makes two important methodological contributions. First, we limited it to multi-component interventions with at least three of the five components of the PCMH model. Earlier reviews typically included results from interventions with as few as one feature, largely as a result of the infancy of the model. Homer, Klatka, Romm, et al. (2008) found that only 1 of the 33 studies they reviewed was of an intervention modeled after the medical home, while the others tested selected components. Rosenthal (2008), the Robert Graham Center (2007), and DePalma (2007) each reviewed the literature on individual components, such as team-based care, rather than multi-component interventions that more closely resemble the PCMH model.
Second, we limit the synthesis of the evidence to that generated by rigorous evaluations, which we assess using a systematic review process. Three previous reviews did not consider the rigor of the evidence (Grumbach and Grundy, 2010; Fields, Leshen, and Patel, 2010; DePalma, 2007). Two conducted a limited assessment by restricting the summary of the evidence to comparison group (Homer, Klatka, Romm, et al., 2008) or peer-reviewed (Friedberg, Lai, Hussey, et al., 2009) studies, with neither assessing the strength of the analytical methods used by the studies or excluding studies that did not use rigorous methods from their summary of the evidence.
What Is the PCMH, and How Might It Improve Outcomes?
There are several definitions of the medical home model, and most contain similar elements. For this review, we use the AHRQ definition, which like many others views the medical home as a way to organize primary care to deliver patient-centered, continuous, comprehensive, and coordinated care with a system-based focus on safety and quality.3 The definition focuses on five principles of primary care delivery4:
- A patient-centered orientation toward each patient’s unique needs, culture, values, and preferences; support of the patient’s self-care efforts; and involvement of the patient in care plans.
- Comprehensive care that meets the large majority of each patient’s physical and mental health care needs, including prevention and wellness, acute care, and chronic care, and is provided by a cohesive team.
- Care that is coordinated across all elements of the complex health care system and connects patients to both medical and social resources in the community.
- Superb access to care that meets patients’ needs and preferences, including care provided after hours and by email and telephone
- A systems-based approach to quality and safety that includes gathering and responding to patient experience data, having a commitment to ongoing quality improvement, and practicing population health management.
In the AHRQ definition, health IT, workforce development, and enhanced payment are considered to be important facilitators of change that support the medical home.5
A conceptual framework guides this review (Figure 1). The framework illustrates the medical home and the outcomes it is hypothesized to improve. We categorize the outcomes using a triple aim framework inspired by the frameworks of Don Berwick and the National Quality Strategy (Berwick et al., 2008; Department of Health and Human Services, March 2011). Specifically, implementing the medical home model is expected to improve quality of care (including processes of care and health outcomes), reduce costs (including use of hospital and emergency department [ED] services—two key drivers of cost), and enhance the experience of care (for patients and caregivers, who are the users of the health care system). We also expect the model to improve the experience of health care professionals.
Figure 1. Conceptual Framework for the Effectiveness of the Medical Home
As the figure shows, factors external to the medical home will also influence the outcomes of interest. These include patient factors (such as health risk, motivation, behaviors, and socioeconomic status), as well as system-wide factors (such as adoption of health IT, payment policies affecting primary care and other providers, workforce development, community resources, the organizational context of the primary care setting, and the nature and cooperativeness of other providers in the medical neighborhood). These factors, in addition to the medical home components, will collectively determine whether an intervention improves key outcomes, the magnitude of these improvements, and the applicability of the findings to other implementation settings and populations. It is important to interpret the findings of evaluations in the context of these multiple external factors to distill lessons to guide the transformation of primary care.
This review is organized as follows. In Chapter 2, we present five key questions and the review methods. Chapter 3 describes the interventions included in this review along with their target populations and implementation settings. Chapter 4 provides a broad overview of the research designs used to evaluate these interventions. In Chapter 5, we assess the rigor of the evaluations of these interventions. Chapter 6 synthesizes the evidence on each outcome using only findings from rigorous evaluations. Chapter 7 summarizes the evidence, discusses how to apply the evidence to current medical home initiatives, and describes limitations of this review. Finally, we share lessons on how to improve the rigor, comprehensiveness, and generalizability of the evidence in the future.back to top
Chapter 2. Key Questions and Approach
- This review of the evidence on the effectiveness of the medical home addresses five key questions:
- Which interventions have been evaluated using quantitative methods, for which patients, and in which implementation settings?
- Which evaluation designs and outcome measures were used to evaluate these interventions?
- Which interventions were evaluated using rigorous methods?
- What are the effects of the rigorously evaluated interventions on key outcomes?
- What lessons does the current evidence teach us?
- Of 498 studies released between January 2000 and September 2010 on U.S.-based interventions, 14 evaluations of 12 interventions tested a multi-component, practice-based intervention and quantitatively examined effects on quality, cost, patient or caregiver experience of care, or professional experience.
- We draw on the U.S. Preventive Services Task Force methods and well-regarded systematic reviews from other fields to rate the internal validity of each analysis undertaken by an evaluation; we do not factor generalizability into the rating.
- We select only rigorous analyses when we summarize the evidence of effectiveness on each outcome.
- We view results that are not statistically significant as inconclusive rather than as evidence of no effects, because we suspect that many of the evaluations lacked sufficient statistical power to detect effects if they existed.
- For findings reported as statistically significant from practice-level evaluations that did not account for clustering of patients within practices, we used published estimates of clustering to adjust their statistical significance; when such estimates were not available, we view the findings as having uncertain statistical significance and classified them as inconclusive, because it is possible that a clustering adjustment could render the findings not statistically significant.
This review examines five key questions. Our goals are to understand what evidence supports the medical home and what lessons can be drawn at this time to ensure that current pilots and demonstrations can generate a solid evidence base in the future. Below we describe the key questions and the sample of evaluations and methods used to answer each question.
- Which interventions have been evaluated using quantitative methods, for which patients, and in which implementation settings? Specifically, we ask how close to the PCMH, as defined by AHRQ, these early interventions are, and whether they have been tested among the intended users of the PCMH model: typical patients and practices. Our goal is to establish how relevant the current evidence base is for understanding the effectiveness of the PCMH.
- Which research designs and outcome measures were used to evaluate these interventions?Ideally, the evidence base would consist of evaluations with strong research designs and analyses of the full range of outcomes that stakeholders need to guide their decisions. We assess whether the existing body of evidence meets this goal.
- Which interventions were evaluated using rigorous methods?We develop and apply a systematic assessment approach to identify interventions that have been rigorously evaluated (and thus can provide rigorous evidence) as well as those that require further evaluation.
- What is the evidence from rigorous evaluations regarding the effects of early PCMH interventions on key outcomes?We use findings from rigorously evaluated interventions to summarize the evidence on effectiveness of the medical home. We exclude findings from weak evaluations because we have less confidence in their findings about the intervention’s effectiveness.
- What lessons can be learned from the early evaluations?We provide insights on how to strengthen the evidence base to meet the high demand for reliable information on the effectiveness of the medical home. These insights emerged from reviewing the interventions that have been tested so far, and identifying factors that have limited the rigor and scope of the current evidence. This information is intended for model developers, funders, and evaluators of medical homes, and has relevance to evaluations of other practice-level interventions.
Selection of Evaluations for Key Questions
We used two samples of evaluations to answer the key questions. From a literature search, we identified 14 evaluations of 12 interventions that met our inclusion criteria (described below). We used this sample to describe the interventions and their evaluations, and to assess the rigor of the evaluations (key questions 1-3). We found that 6 of the 12 interventions were evaluated using rigorous methods for at least one outcome, and we used this sample of evaluations to synthesize the evidence on each outcome (key question 4). We show this process in a flowchart in Figure 2 and describe it in detail below. Finally, we draw on the sample of 14 evaluations and our analyses of key questions 1-4 to draw lessons on how to improve the evidence base on the medical home (key question 5).
Sample Selection for Key Questions 1-3: Description of the Interventions, Their Evaluations, and Assessment of the Rigor of the Evaluations
The review team conducted a broad search to identify English-language studies in the published and grey literature on the PCMH in the United States. To capture published studies, we used Ovid and EBSCO search engines to search within multiple social science, health services, and medical databases for articles from January 2000 to September 2010 containing the words “medical home” or “primary care transformation.” We also conducted targeted searches to identify studies of initiatives for which no start dates were specified, but that are widely cited as being part of the evidence base on the medical home. We identified additional studies by reviewing content on 100 relevant Web sites, examining bibliographies in existing review articles, and gathering input from experts in the field. This search process yielded 498 potentially relevant citations. As with all evidence reviews, because of publication bias, the evaluations summarized here may be more likely to include favorable effects and less likely to include no effects or unfavorable effects.
Of the 498 citations, we found 14 evaluations of 12 interventions6 that met our two criteria for inclusion in the review:
- The evaluation tested a primary-care, practice-based intervention with three or more of the five medical home components defined by AHRQ (delivering care that is patient-centered, comprehensive and team-based, coordinated, accessible, and systems-based in its approach to quality and safety). We excluded evaluations of care coordination and disease management interventions that met these criteria but were not provided from within, or in close partnership with, the practice (for example, interventions delivered by off-site care managers via telephone).
- The evaluation used quantitative methods to examine effects on either (a) a triple aim outcome: quality of care, costs7 (or hospital use or emergency department use, two major cost drivers), and patient or caregiver experience; or (b) professional experience.
Because most of the interventions target different subgroups of the U.S. primary care population, our inclusion criteria did not consider the population served. We also did not require that the intervention include health IT or provide enhanced payment. For most evaluations, findings were published in multiple articles.
Two evaluations of medical home interventions—the American Academy of Family Practice’s National Demonstration Project (NDP), which is often cited in the medical home literature, and the Illinois Medical Home Project (IMHP)—are not included in the sample. Because these evaluations tested the effect of facilitation as an intervention for practice redesign efforts, they did not meet the first criterion of testing the effects of a medical home intervention. In other words, they tested the effect of helping practices redesign themselves to become medical homes relative to the effect of practices becoming medical homes on their own. We also did not include studies of the Physician Practice Connections-Primary Care Medical Home Assessment (PPC-PC) (Holmboe, Arnold, Weng, et al., 2010) and the Physician Practice Connections Readiness Survey Assessment (PPC-RS) (Solberg, Asche, Pawlson, et al., 2008). These studies did not test a specific intervention; instead, they surveyed physicians about the presence of medical home capabilities to examine whether physicians who reported such capabilities delivered better quality of care.
Sample Selection for Key Question 4: Synthesis of Evidence from Rigorous Evaluations
We included only findings from rigorous evaluations in the synthesis of the evidence. Overall, 6 of the 14 evaluations were rated as providing rigorous evidence on at least one outcome: 4 evaluations on different aspects of quality of care (3 on processes of care, 3 on health outcomes, and 2 on mortality); 6 on cost or utilization (4 on total costs, 5 on hospital use, and 3 on ED use); 3 on experience of care (3 on patient experience, 2 on caregiver experience); and 1 on professional experience.
Methods for Key Questions 1-4
We turn now to the methods we used to answer key questions 1-4.
Key Question 1: Description of Interventions
We describe each of the 12 interventions in terms of AHRQ’s five core principles, as well as two of the facilitators of the model: health IT and payment (none of the interventions undertook workforce development). We also describe the patient populations each intervention targeted (for example, Medicare patients, patients with chronic physical or mental illnesses), as well as the type of insurance coverage patients had (both fee-for-service and managed care). Finally, we describe the implementation setting in which the intervention was implemented (in larger care delivery settings, such as integrated delivery systems, versus in independent practices) and whether the practice had access to electronic health records (EHRs). We draw particular attention to the number of practices in which the intervention was implemented, as this has important implications for the ability of the evaluation to identify effects of the intervention when they occur. Chapter 3 presents these findings.
Key Question 2: Description of Evaluations
As a precursor to the formal assessment of the rigor of the evaluations (undertaken to answer key question 3), we present a broad overview of the rigor and scope of the current research landscape on the medical home by describing the evaluation designs and outcome measures used in the 14 evaluations. Selecting a strong evaluation design is a first step that evaluators can take to ensure that the evaluation produces unbiased estimates of the causal effects of an intervention. We therefore classify the designs into those that employed a control or comparison group (randomized, controlled trials [RCTs] and nonexperimental comparison group designs)8 and those that did not (pre-post and cross-sectional designs). Designs without a control or comparison group often make it difficult to assess what the sample’s outcomes would have been absent the intervention. (The purpose of a control/comparison group is to establish that counterfactual—a necessary condition for obtaining an unbiased impact estimate.) We also describe which of the key outcomes the evaluations examined. We present these findings in Chapter 4.
Key Question 3: Assessment of the Rigor of the Evaluations
We developed a systematic approach to assess the rigor of the 14 evaluations. We drew broadly from the published methods of the U.S. Preventive Services Task Force (USPSTF) and supplemented them with specific criteria from well-regarded evidence reviews.9
Rather than give a global rating to each evaluation, we individually rated the internal validity of each analysis conducted by an evaluation as high, moderate, low, or excluded. We rated individual analyses because evaluations often used different designs, samples, and methods (and sometimes different subgroups of patients) to analyze different outcomes over varying followup periods. Therefore, to allow for the possibility that the evaluation of a single intervention could provide more rigorous evidence on some outcomes than others, we separately assessed the analysis of each outcome measure at each followup period and, if applicable, for each subgroup of patients. We include only analyses rated as high or moderate in our synthesis of the evidence.
Our rating of each analysis is based solely on an assessment of its internal validity. We do not factor generalizability (or external validity) into the rating because most interventions included in this review targeted a specific subset of primary care patients, were implemented in unique settings, and either purposely selected practices or relied on them to volunteer; therefore, nearly all of them have limited generalizability. We summarize the characteristics of patients and implementation settings used in the rigorous evaluations to alert decisionmakers to the possibility that findings may differ if interventions are implemented in other populations and settings.
We rated each analysis using a sequence of criteria, starting with the most general (evaluation design) and ending with the most specific (such as whether the analysis controlled for outcome values prior to the start of the intervention (at baseline)). We rated an analysis “excluded” if the evaluation design and methods were not described in enough detail to permit assessment of the internal validity of the results. We always rated analyses “low” if they did not employ a control or comparison group (and instead used pre-post or cross-sectional evaluations). If they did employ a control or comparison group, such as analyses from RCTs and nonexperimental comparison group evaluations, we assessed the strength of the methods to identify causal effects and produce unbiased estimates of the interventions’ effects, and accordingly rated them high, moderate, or low.
Analyses from RCTs were given a high rating if they had:
- No systematic confounders
- No endogenous subgroups10
- Low attrition
- Adjustment for any statistically significant baseline differences in the outcome between the intervention and control groups
Analyses from comparison group evaluations, and from RCTs with high attrition or with endogenous subgroups, were given a moderate rating if they had:
- No systematic confounders
- Equivalence of the outcome between the intervention and comparison group samples at baseline
- Adjustment for baseline values of the outcome
Analyses from RCTs and comparison group evaluations were given a low rating if they did not meet the criteria for a high or moderate rating. In many cases, because of the limited information that study authors can include in a journal article, we sought additional details from authors to be able to determine the rating. Appendix B describes the assessment approach in detail. Chapter 5 presents the ratings.
Key Question 4: Synthesis of Rigorous Evidence
Chapter 6 synthesizes evidence on four broad outcomes: (1) quality of care (including processes of care, health outcomes, and mortality); (2) costs, hospital use, and ED use; (3) patient and caregiver experience of care; and (4) professional experience. The synthesis of evidence on any given outcome includes only findings from analyses found to be rigorous, that is, those rated high or moderate. We did not synthesize results from analyses rated low, because we believe that if these interventions were evaluated using better methods, the results could differ substantially. For example, results could change from suggesting an intervention did not work to indicating it did, or vice versa. We also discuss the generalizability of the findings to the U.S. primary care population, using the target populations and implementation settings described in key question 1.
We categorized the rigorous findings as (1) statistically significant and favorable, (2) statistically significant and unfavorable, (3) inconclusive (that is, they fail to indicate whether or not the intervention worked) because they were not statistically significant, or (4) inconclusive because their statistical significance was uncertain due to lack of adjustment for clustering of patients within practices. We recognize that decisionmakers, from payers to providers, may be frustrated with inconclusive findings, but we believe this label accurately reflects the lack of certainty about whether or not certain interventions had an effect, as we explain below.
Statistical significance thresholds. To determine whether the effect on an outcome measure is statistically significant, we use a significance level of 5 percent for all measures except costs. If an intervention-control difference is statistically significant at the 5 percent level, we can be 95 percent confident that there is a true effect. Because of the large variation in cost outcomes, we use a more liberal significance level of 10 percent.
Results that are not statistically significant are considered inconclusive. When an intervention-control difference is not statistically significant, we treat the result as inconclusive, rather than as evidence that the intervention is not effective. Interpretation of tests of statistical significance depends on the statistical power of the test—the chance of detecting an effect that is truly there. A test is conventionally considered to be adequately powered if there is an 80 percent chance (in other words, reasonable certainty) that if a true effect of a prespecified magnitude exists, it will show up as a statistically significant intervention-control difference. When a test is not adequately powered, the effect can be difficult to detect with reasonable certainty. In other words, testing is likely to show that the difference is not statistically significant, even though it is real.
We suspect that most evaluations reviewed here had inadequately powered tests of statistical significance because of small sample sizes: none of the rigorous evaluations of practice-level interventions had more than 11 practices in the intervention group. Peikes, Dale, Lundquist, et al. (2011) estimate that, assuming a moderate amount of clustering, a model that is tested in 20 intervention practices (with 20 control practices) and targets all patients might need to reduce costs by 45 percent or more (a very large effect) to have an 80 percent chance of detecting the reduction. If cost and service use were measured among the chronically ill, as many of these evaluations do, the intervention might still need to reduce costs by 20 percent or more for the test to have an 80 percent chance of detecting it. These are large effects for an intervention to achieve, and an evaluation would need even larger sample sizes (and therefore greater power) to detect smaller, more plausible effects. In such cases, therefore, a difference that is not statistically significant does not imply that the intervention is not effective. Instead, it has not been proven to be effective; well-powered evaluations in the future would be required to assess the intervention’s effectiveness.
Statistically significant results that do not account for clustering are considered inconclusive. In some evaluations, and for some outcomes, statistical testing of practice-level interventions did not account for the clustering of patients within practices when determining statistical significance. Because clustering reduces the effective sample size, a test that ignores clustering overstates the statistical significance of a finding and might show a difference to be statistically significant when it was not (Peikes, Dale, Lundquist, et al., 2011). As a result, what at first appears to be a statistically significant difference may no longer remain so after accounting for clustering. For differences reported to be statistically significant without accounting for clustering, we adjusted the statistical significance by using published estimates of clustering.11 Differences that were no longer statistically significant post-adjustment were reclassified as not statistically significant. However, in two cases, we classified differences as “inconclusive due to uncertain statistical significance”: (1) when statistical significance was sensitive to the range of clustering estimates used; and (2) for outcomes other than cost and service use, for which we could not find published estimates of clustering.12 In both cases, because we were unable to determine the correct statistical significance, we consider significance as uncertain and the evidence inconclusive. We note that this issue is irrelevant for differences that were reported as not statistically significant because they would continue to remain so after a clustering adjustment.
Reporting the magnitude of effects. Our approach to reporting the magnitude of effects varies by outcome and category of evidence. For statistically significant effects for cost, hospital use, and ED use, we report the magnitude of effects. Ideally, we would indicate the uncertainty about the estimated effect by reporting the confidence interval to convey the possible range of the true effect. However, most evaluations did not report this information. For other outcomes, because of the diverse measures that were examined across different evaluations, we do not report the magnitude of effects. We also do not report the magnitude of results that are not statistically significant or have uncertain statistical significance. This is because the confidence interval around such results includes both favorable and unfavorable intervention-control differences.13
Figure 2. Selection of Evaluations for Key Questions 1–4back to top
Chapter 3. The Medical Home Landscape: Which Interventions Have Been Tested, for Which Patients, and in Which Implementation Settings?
- Most of the 12 interventions were developed before the recent interest in the medical home.
- Most interventions added a care manager to operate within the primary care practice. Care management is an important component of the medical home, but does not fundamentally transform a practice in the way becoming a medical home does.
- These precursor interventions differed considerably from one another.
- Few evaluations documented how well the interventions were implemented, so our descriptions may be closer to what was planned than to what was delivered.
- Half the interventions provided financial support to the practices and among those that did not, all but two provided a care manager.
- Most interventions were designed to serve patients who were older or sicker than those in the average primary care patient panel.
- Most interventions were tested in practices that were part of larger delivery systems, and are not typical of primary care practices in the United States.
In this chapter, we describe the 12 interventions that met our criteria for inclusion in the review, interventions often cited as evidence in favor of the medical home model. We describe how similar the interventions are to the PCMH model, the patient populations they targeted, and the settings in which they were implemented. To enable comparison to the PCMH, we categorize the features of each intervention into AHRQ’s five core principles and two of the facilitators (health IT and payment). We summarize the interventions as the study authors and implementers described them. Publications varied in the degree of detail they provided about how the intervention operationalized and implemented different functions and processes. We also note that few evaluations documented how well the models were implemented, so our descriptions may be closer to what was planned than what was delivered when there was a difference.
Which Interventions Have Been Tested?
Table 1. Overview of the 12 interventions reviewedView table 1 in new window
Table 1 provides an overview of the interventions, and Table 17 in Appendix C categorizes them using the framework of the AHRQ PCMH definition.
Because many of the interventions were developed before the recent interest in the medical home, most of them embedded a care manager within the primary care practice, and did not fundamentally transform the practice, as would be required to become a medical home. The care manager was typically responsible for developing an individualized care plan for high-risk patients and for coordinating their care. By providing care management from within the practice, these early interventions tested a core component of the medical home. Most of these early interventions also included each of the five AHRQ PCMH principles, but they did so in a less integrated and comprehensive manner than current demonstrations and typically for a subset of patients. A few introduced more fundamental changes to the way primary care practices operate (such as daily care team huddles, 24/7 access, and longer patient appointments) and could be considered early prototypes of the medical home model. The field is evolving rapidly, and many of these diverse interventions have been adapted to look more like the medical home model since the joint principles were released. However, at the time they were studied, they represent precursors to the model being tested today. This serves as a reminder that the evidence commonly cited in support of the PCMH is actually on precursors, and needs to be interpreted in that context.
As Table 17 in Appendix C shows, nearly all 12 interventions incorporated each of the core PCMH principles, but varied in the degree to which they emphasized them.
- Patient-Centered. Most (9 of 12) of the interventions emphasized the development of an individualized care plan by a nurse care manager. Four of these interventions reported that the plan was developed in collaboration with the patient and/or caregiver, and it is difficult to determine from the available information which interventions shared the plan with patients. Several interventions also included specific protocols for developing plans and skills for patient self-management. Systematic patient outreach efforts (such as GRACE’s monthly contacts with patients and caregivers and Aetna’s option for the family to sit in on patient office visits) were less common across the interventions. One intervention (CCNC) was described as focusing on culturally sensitive and whole-person care.
- Comprehensive Care. All 12 interventions adopted a team-based approach to patient assessment and care planning. Although the size and composition of the teams varied across interventions, all teams included a PCP and a care manager. In 10 of the 12 interventions, this care manager was a nurse who may have worked with other clinical and nonclinical staff. The other two interventions used a nurse in some practices, and a psychologist, social worker, or other clinician in others. Several interventions also included team members from various disciplines, such as specialists, physical therapists, social workers, and pharmacists, depending on the needs of the patient. All 12 interventions were tested in practices led by physicians; none were tested in practices led by nurse practitioners or physician assistants.
- Coordinated Care. The mechanisms and processes for ensuring effective coordination of patient care by the care manager were rarely described. Half the interventions reported coordinating care with community-based services.
- Access to Care. Many of these interventions implemented mechanisms to enhance access to care and facilitate scheduling. These included dedicated telephone lines to a nurse care manager or care team and, among selected interventions, use of email messaging with care providers, telephone visits, and self-scheduling using electronic systems.
- Systems Approach to Quality and Safety. In 8 of the 12 interventions, there appeared to be some emphasis on systematizing the use of evidence-based care protocols. Most practices with EHRs embedded such protocols and care reminders in the EHR system. Eight interventions implemented quality assurance mechanisms, including regular training for care managers, random or periodic chart reviews, and analysis of quality and clinical outcomes (among practices with EHR capabilities).
Interventions also varied in the extent to which they emphasized payment and health IT.
- Payment: Financial and In-Kind Support. Practices and/or physicians in 6 of the 12 interventions received financial support or incentive payments to facilitate and incentivize practice changes. Of those six interventions, three provided practices with funding for startup, infrastructure (for example, health IT), and/or practice transformation costs. Three of the six provided stipends to physicians or other providers, and two provided a payment to the practice according to the number of patients served or enrolled in the program. Five interventions implemented shared savings or performance incentive programs. Five of the six interventions providing financial support to practices also provided a care manager.14 Among the six that did not provide practices with financial support, four provided a care manager.15
- Health Information Technology. Half of the 12 interventions used EHRs to help primary care teams better manage their patients and systematize the application of evidence-based care protocols. Most EHR systems were used to track patients, generate reminders, and calculate patient statistics to better target their disease management and care coordination efforts to high-risk patients. A few EHR systems were accessible to patients via a Web-based interface.
Where Were the Interventions Tested?
Table 2. Target populations and implementation settingsView table 2 in new window
Most interventions were tested in unique settings not typical of primary care practices in the United States. Table 2 summarizes the implementation settings and target populations for the 12 interventions.
Only 4 of the 12 interventions were not tested in practices in larger delivery systems such as an integrated delivery system (IDS) or comparable organizational context (Table 2). No estimates of the proportion of primary care practices that operate nationwide within larger delivery systems are available. However, an earlier study estimated that there were only about 100 IDSs in the country, most commonly in the West and Upper Midwest (Enthoven, 2009), so these are not typical of the primary care landscape. The evaluations tested interventions implemented in rural communities, in cities, across multiple counties, across entire States, and across multiple States.
Most interventions were tested in a relatively small number of intervention practices. Two interventions were tested in a single practice, four in 5 to 10 practices, three in 11 to 20, and only three were tested in more than 20 practices. As discussed in another AHRQ white paper (Peikes, Dale, Lundquist, et al., 2011), a small number of intervention practices limits the statistical power of evaluations to detect effects of interventions delivered at the practice level. From an implementation perspective, a trial with 10 or more intervention practices is large and challenging. However, from an evaluation perspective, given the need to account for clustering, a practice-level intervention with fewer than 20 intervention practices is generally too small to be able to detect effects on some outcomes, such as costs. An evaluation might require well over 500 intervention practices/sites to be likely to detect changes in costs across all patients, and at least 20 intervention practices to detect changes in costs only among high-risk patients.
Whom Did the Interventions Serve?
Most of the interventions targeted patients who were older or sicker than the average patient panel. Of the 12 interventions, the Group Health Cooperative Medical Home intervention was the only one designed to serve all patients in a practice (see Table 2). The other interventions targeted either patients with chronic illnesses or Medicare patients, who tend to be sicker than the average patient in a primary care practice. Most evaluations tested the interventions on adult patient populations; however, PACC was tested solely on pediatric patients. Five interventions served Medicare patients exclusively, and one served Medicaid patients exclusively. Nine of the interventions were tested on patients with both fee-for-service and managed care insurance coverage.
These interventions have limited generalizability to the current PCMH model being tested. They were tested largely among patients sicker and older than average and in practices that were part of larger delivery systems that are not representative of most practices in the United States. Most new PCMH demonstrations and initiatives are designed to serve all patients in the primary care practice, and many encompass diverse practice types, including small and medium-sized independent practices, as well as federally qualified health centers.16back to top
Chapter 4. How Do Studies Evaluate the Interventions?
- Among the 14 evaluations, study designs varied:
- 6 used comparison group designs.
- 4 used randomized, controlled trials.
- 2 used pre-post studies.
- 2 did not report the study design they used.
- Among the 14 evaluations, the outcomes varied:
- 5 examined all triple aim outcomes.
- 8 examined process of care or health outcome measures.
- 8 examined total costs (including intervention costs).
- 5 examined patient or caregiver experience of care.
- 5 examined health care professional experience.
To understand the scope of current research for evaluating the PCMH model, the review team collected information on evaluation designs and outcomes for the 12 interventions included in the review. Because one intervention—CCNC—was evaluated by three distinct evaluations, we examine 14 evaluations here.
The evaluations used a range of designs to assess the effects of these interventions (Table 3). These designs include patient-level RCTs, cluster RCTs (where practices, physicians, or groups of physicians, rather than patients, are randomized), comparison group designs, and pre-post designs. A comparison group design, considered to be fairly rigorous, was the most commonly used (by evaluations of CMP, GHS ProvenHealth Navigator, Group Health, and Merit Health System and Blue Cross Blue Shield (BCBS) of North Dakota Chronic Disease Management Pilot, and two evaluations of CCNC). An RCT (including cluster RCT), considered the strongest possible research design, was used by four evaluations: two RCTs (IMPACT and VA TM/HBPC), and two cluster RCTs (GRACE and Guided Care).17 Weaker designs, such as pre-post designs, were used by two evaluations (Pennsylvania Chronic Care Initiative and PACC) to examine all outcomes. The remaining two evaluations (Aetna’s Embedded Case Managers and one of the three evaluations of CCNC) did not report the design they used.
Overall, strong evaluation designs were used to evaluate many of the interventions. Because strong designs might not yield unbiased estimates of the effects of an intervention if the evaluation suffers from problems (such as high attrition of participants) or if analytic methods lack rigor, we also examined these additional features when rating the rigor of each evaluation.
Among these early evaluations, fewer than half (5 of 14) looked at all three triple aim outcomes (quality, cost, and patient or caregiver experience) (Table 3). Seven evaluations examined process-of-care measures; four investigated health outcome measures (such as functioning, self-rated health status, and quality of life); and two examined mortality. Eleven evaluations examined cost, but only eight of these examined total costs including the intervention. Without information on the costs of providing the intervention, it is difficult to assess whether the intervention increased costs, generated savings, or was cost neutral from the payer’s perspective. Twelve evaluations examined hospital use, and nine examined ED use. Although a major focus of the patient-centered medical home is on patient experience, only five of these evaluations examined measures related to patient experience, and only two examined caregiver experience. The dearth of studies examining patient and caregiver experience may reflect the relatively high cost of collecting survey data, or the fact that these models predated the current interest in the PCMH, which emphasizes patient-centeredness. Five of the 14 evaluations examined effects on professional experience.
Given the nascent stage of research on the medical home and the different goals of stakeholders, it is not surprising that evaluations varied in the measures and the length of followup periods for each outcome. This variation makes it difficult to synthesize results across evaluations. An example illustrates how measures within each outcome varied across the evaluations: process-of-care measures ranged from Prevention Quality Indicators (PQIs, used in CMP), to HEDIS measures (Group Health Cooperative Medical Home), to Assessing Care of Vulnerable Elders (ACOVE) general and geriatric care measures (GRACE). Similarly, measures of health outcomes included the SF-36, Barthel Index, quality of life, and self-rated health status. Additionally, outcomes were evaluated for different durations of followup, ranging from 8 months to 3 years (see Chapter 5 for more details). The Commonwealth Fund recognized the need to harmonize measures, data sources, and followup periods and has recently released core measures for evaluating the PCMH (Rosenthal, Abrams, Bitton, et al., 2012).18back to top
Chapter 5. Which Evaluations Provide Rigorous Evidence of Effects on Each Outcome?
- Six of the 14 evaluations received a high or moderate rating for analysis of at least one outcome.
- When the evidence on an outcome is rated low or excluded, the intervention itself could be effective (or not); however, limitations in the evaluation methods prevent us from reliably drawing such a conclusion.
Having described the 12 interventions and the research designs and outcomes used to evaluate them, we now report the findings of a formal assessment of the rigor of the evidence on effectiveness of these interventions. Our goal was to identify analyses based on strong designs and methods that generated internally valid estimates of the effects of the intervention. We rated the rigor of each analysis conducted by an evaluation as high, moderate, low, or excluded. Tables 4.1 and 4.2 show these ratings along with the primary rationale for the rating.
We note here that the ratings developed in this chapter are of the evidence generated by the evaluations. When the evidence on an outcome is rated low or excluded, the intervention itself could be effective (or not), but limitations in the evaluation design or methods prevent us from reliably drawing such a conclusion. Such evaluations represent important efforts to build the evidence base and may provide important insights about how best to refine a specific intervention and guidance on how to design future evaluations; however, their usefulness in determining the quantitative effectiveness of the intervention is limited. To learn about the potential of these interventions to improve outcomes, evaluations using stronger methods are needed.
Ratings by Intervention
We began the sequential assessment of each analysis conducted as part of an evaluation by assessing the strength of the evaluation design. As noted in Chapter 4, 12 of the 14 evaluations provided information on design, while 2 (Aetna and 1 of the CCNC evaluations) did not provide sufficient information to make it possible to assess the strength of their designs. Because knowledge of the evaluation design is crucial in assessing whether the evaluation produced internally valid estimates of effects, we gave these two evaluations an “excluded” rating to indicate that we do not have sufficient information to make this assessment.
Among the 12 evaluations that provided information on design, 10 used strong designs of an RCT or a nonexperimental comparison group for most of their outcomes, and 2 (Pennsylvania Chronic Care Initiative and PACC) used pre-post designs. We gave a low rating to all analyses conducted by the two pre-post evaluations, because it is difficult to attribute changes in the outcomes to the intervention alone rather than to other factors (such as secular trends) that might have influenced the outcome—factors that could be controlled with the use of a comparison group.
The 10 evaluations that used RCTs and nonexperimental comparison group designs were next assessed for the strength of their analytic methods. Of these, 6 received a high or moderate rating for at least one outcome, while 4 received a low rating for all outcomes examined.19
Of the six evaluations that received a high or moderate rating for at least one outcome (GRACE, IMPACT, VA TM/HBPC, Guided Care, CMP, and GHS ProvenHealth Navigator), ratings varied considerably within the evaluation—an indication that the evaluation produced more rigorous evidence on some outcomes than others. Table 4.1 shows the ratings for these six evaluations. The lack of an appropriate comparison group was the most common reason for a low rating. Well-matched comparison groups (that are similar to the intervention group in terms of baseline values of key patient outcome measures, as well as practice variables such as the mix of patients, number of providers, and key infrastructure such as EHRs) are important because they help gauge the extent to which change over the course of the evaluation is due to the intervention rather than to other concurrent changes.20 In general, an evaluation that compares patients in pioneering, high-performing practices that chose to participate in an intervention, with patients in practices that had average performance before the intervention and who did not choose to change may artificially make the intervention look more effective than it truly is. Next we describe how we arrived at the ratings for the six evaluations that received a high or moderate rating for at least one outcome:
- GRACE: We gave a high rating to analyses of most outcomes examined by this cluster RCT evaluation. We rated one process-of-care measure low because it was examined for an endogenous subgroup—patients who had one or more hospital stay in the first year—and the evaluation did not report whether the intervention and control patients in this subgroup were comparable at baseline. We also rated the analysis of professional experience low, because the evaluation examined effects on this outcome without using a comparison group.
- IMPACT: We gave a high rating to analyses of most outcomes examined by this RCT evaluation. The analysis of patient experience at two of four followups received a low rating because the outcome was examined for an endogenous subgroup, and the evaluation did not report whether the intervention and control group subgroups were comparable at baseline. We also rated the analysis of professional experience low because the evaluation examined effects on this outcome without using a comparison group.
- VA TM/HBPC: The analyses of costs and service use for patients both with and without a terminal illness, and the analyses of health outcomes and experience of care for patients and caregivers among patients without a terminal illness, were rated high. However, during the course of the evaluation, there was high attrition among the subgroup of patients with a terminal illness, and the evaluation did not ascertain whether the intervention and control groups who were alive at followup had similar baseline values of the outcomes being examined. Analyses of all outcomes for this subgroup received a low rating because we do not know whether the intervention rather than pre-intervention differences between the surviving members of intervention and control groups are driving the results.
- Guided Care: Analyses of all outcomes, except professional experience, received a high rating. The sample of providers experienced high attrition during the evaluation. However, because the evaluation showed that intervention and control group providers had similar baseline values of the professional experience measures, and also controlled for these values in the analyses, the analyses of professional experience received a moderate rating.
- GHS ProvenHealth Navigator: This comparison group evaluation received a moderate rating (the highest rating a comparison group evaluation can receive) for its analysis of hospital use because the intervention and comparison groups had comparable hospital use before the intervention began, and the evaluation controlled for baseline hospital use in the analyses. Analysis of costs, however, received a low rating because the intervention and comparison groups did not have similar values of costs at baseline.
- CMP: This comparison group design evaluation received a moderate rating for all analyses it conducted.
Ratings by Outcome
Not all evaluations examined all outcomes, and among those that did, not all used rigorous methods. Table 5 shows the number of evaluations receiving a high or moderate rating for an outcome among those that examined that outcome (which is also shown in Figure 2). This ranges from one to five studies, with three studies for most outcomes.back to top
Chapter 6. Evidence Synthesis
- The rigorous evidence on PCMH effectiveness indicates some favorable effects across quality of care, hospital and ED use, and patient and caregiver experience, a few unfavorable effects on costs, and mostly inconclusive results (because of insufficient sample sizes or inadequate methods to account for clustering in the data).
- The interventions were tested in atypical practices and among older and sicker-than-average patients.
This chapter synthesizes the evidence on each key outcome from analyses rated as high or moderate.
As described in Chapter 2, we synthesize findings on four broad outcomes of interest: (1) quality of care (including processes of care, health outcomes, and mortality); (2) costs, hospital use, and ED use; (3) patient and caregiver experience; and (4) health care professional experience. Within these outcomes, we classify the findings on each measure into one of four categories: (1) statistically significant with a favorable effect, (2) statistically significant with an unfavorable effect, (3) inconclusive because of lack of statistical significance, and (4) inconclusive because of uncertain statistical significance. Inconclusive findings cannot indicate whether the intervention worked or not. As described in Chapter 2, we consider findings that are not statistically significant as inconclusive rather than as evidence that the intervention had no effects because we suspect that the evaluations lacked adequate power to detect effects that might have existed. We also view as inconclusive findings from analyses that did not correctly account for clustering of patients within practices, because their reported statistical significance is likely overstated. We report the magnitude of effects for statistically significant effects on cost and service use, but not for other outcomes, and note that the true effect on cost and service use could fall in a wide range around the reported estimate.
Findings From Rigorous Evaluations
Improving the Quality of Care: Processes of Care, Health Outcomes, and Mortality
The evidence on processes of care and health outcomes indicates some improvements with the majority of evidence being inconclusive.
Processes of Care. Of the three rigorous evaluations of processes of care (IMPACT, GRACE, and CMP), only IMPACT had favorable effects (Table 6). It increased rates of medication use throughout its 2 years and increased use of psychotherapy or specialty mental health care in the first year but not the second. However, for GRACE and CMP, findings on processes of care were inconclusive, because some results were not statistically significant and because statistical significance for other results had been determined without accounting for the clustered nature of the data.
Health Outcomes. Evaluations of GRACE, IMPACT, and VA TM/HBPC provided rigorous evidence on measures of health outcomes. IMPACT improved nearly all the health measures that were examined, GRACE improved some of the measures, and VA TM/HBPC did not improve any (Table 7). IMPACT reduced depression symptoms and improved overall quality of life and the SF-12 physical component score throughout the first and second years of the program, and reduced overall functional impairment through the first 1.5 years. General health status, which was first measured at 1 year, showed an improvement that continued through the second year. GRACE improved half the SF-36 scales; however, for the other scales, as well as for activities of daily living, instrumental activities of daily living, and days in bed, the findings were inconclusive because the results were not statistically significant. The findings from the evaluation of VA TM/HBPC were also inconclusive because the results for functional status and quality of life of patients with a nonterminal illness were not statistically significant.21
Mortality. Two evaluations (of GRACE and CMP) examined effects on mortality using rigorous methods. Although effects among all patients would not be expected in the short followup periods employed, they are possible among the high-risk Medicare patients these interventions served. The evidence was inconclusive for both interventions (Table 8). The evaluation of CMP did not adjust statistical significance for clustering, which rendered the results they reported as statistically significant inconclusive. Results from its remaining analyses, and results from GRACE, were not statistically significant. Without knowing the power, it is difficult to determine whether these indicate no effects.
Reducing Costs of Care and Service Use
The evidence on costs is mixed, with some increases, one reduction, and some inconclusive results. The evidence on hospital use and ED use, two important drivers of costs, is mostly inconclusive but shows some reductions.
Cost. Four evaluations examined the effects on total costs (including intervention costs) using rigorous methods (GRACE, IMPACT, VA TM/HBPC, and Guided Care; Table 9). Two of these evaluations reported statistically significant cost increases and the other two were inconclusive because the cost differences were not statistically significant.
The VA TM/HBPC increased costs by 12 percent over a 1-year period among its target population of chronically and terminally ill veterans, and GRACE increased costs by 28 and 14 percent among Medicare patients in the first and second years of the intervention.22 The cost increases for GRACE were driven by increases of 46 and 30 percent in each year among the low-risk subgroup of Medicare patients. Although GRACE increased costs in both years, it is the only evaluation to report any savings for some enrollees. These savings of 23 percent for high-risk Medicare patients (those with a probability of repeated admission [PRA] score of 0.4 or above, which represented one-fourth of the enrollees) emerged a year after the 2-year intervention ended (when there were no intervention costs).23 These reductions were enough to offset the 19 percent increase in costs of low-risk patients. As a result, there were no statistically significant differences between intervention and control group costs for all patients in the third year.
The evaluations of IMPACT and Guided Care reported no statistically significant differences in costs. Without sufficient information on statistical power, it is not possible to determine whether this represents lack of effects, and therefore cost neutrality, or insufficient sample size to detect effects. As a result, the evidence on costs from these evaluations is inconclusive.
Hospital Use. Evaluations of five interventions (GRACE, VA TM/HBPC, Guided Care, CMP, and GHS ProvenHealth Navigator) provide rigorous evidence on hospital use. Some evidence indicates reductions in use, but the remaining evidence is inconclusive (Table 10). GHS ProvenHealth Navigator, the only intervention that reduced hospitalizations for all patients (Medicare Advantage enrollees), decreased hospitalizations by an estimated 18 percent and readmissions by 36 percent.
Two other interventions (GRACE and VA TM/HBPC) also reduced the number of hospitalizations (readmissions in the case of VA TM/HBPC), but these were limited to certain high-risk subgroups served by these interventions. GRACE did not have a statistically significant effect on hospitalizations in the first year for a high-risk subgroup of patients (defined by a high probability of repeated hospital admission), but it did reduce hospitalizations by 44 percent in the second year and 40 percent in the third (postintervention) year for this subgroup. VA TM/HBPC reduced the number of readmissions among the subgroup of severely disabled VA patients by 22 percent in the first 6 months of the yearlong intervention, but did not have a statistically significant effect over the first year when the next 6 months were included, which suggests that the estimate for the first 6 months was not sustainable. Turning to all enrollees, the evidence from GRACE and VA TM/HBPC evaluations is inconclusive.
Evidence from the remaining two interventions is also inconclusive. Guided Care’s evaluation found that intervention-control differences in the number of hospitalizations among its targeted population of high-risk Medicare patients were not statistically significant. In the case of CMP, intervention-comparison differences in the odds of hospitalization among all patients and among the subgroup without diabetes were not significant, and among the subgroup with diabetes had uncertain significance, rendering all these findings inconclusive.
Emergency Department Use. Evaluations of the GRACE, Guided Care, and CMP interventions used rigorous methods to examine effects on ED use (Table 11). Only one of these evaluations found favorable effects: intervention-control differences in the first year of the GRACE intervention were not statistically significant, but in the second year, GRACE reduced the number of ED visits by 24 percent for all patients and by 35 percent for its high-risk patients. The other two evaluations—Guided Care and CMP—provided inconclusive evidence on ED use. The evaluation of Guided Care did not find statistically significant differences in either the first 8 or 20 months of the intervention. Similarly, the evaluation of CMP did not find statistically significant differences in the odds of an ED visit in the first year among all patients, or among patients with or without diabetes. In the second year, results for all patients continued to be not significant, while among the subgroups with and without diabetes, the results had uncertain significance because the analyses did not account for clustering.
Improving the Experience of Care
Patients and Caregivers
Some evidence on patient and caregiver experience of care is favorable and the rest is inconclusive.
Patient Experience of Care. Of the three rigorous evaluations of patient experience (IMPACT, VA TM/HBPC, and Guided Care), two found evidence of some improvements, while the third was inconclusive (Table 12). IMPACT improved patients’ satisfaction with depression care at 3 months and 1 year. Patients with a nonterminal illness in the VA TM/HBPC reported improvements in all but one measure: patients in the intervention group had better access to care, interpersonal experience, technical quality, communication, and self-reported outcomes, but did not have statistically significant differences in satisfaction with care, compared to patients in the control group. The findings from Guided Care were inconclusive, in large part because the analyses did not account for clustering when calculating statistical significance and because for one measure—decision support—the intervention-control difference was not statistically significant.
Caregiver Experience of Care. Of the two rigorous evaluations of caregiver experience (VA TM/HBPC and Guided Care), the evaluation of VA TM/HBPC found mostly favorable effects, while the evaluation of Guided Care was inconclusive (Table 13). The VA TM/HBPC improved caregiver ratings of the quality of care received by patients with nonterminal illness. The intervention also improved most measures of caregiver functional status as measured by the SF-36 and reduced one of two measures of burden among caregivers of patients with nonterminal illness. For Guided Care, intervention-control differences in caregiver experience with the quality of care provided to patients, caregiver burden, and caregiver productivity at 18 months were not statistically significant; the statistical significance of differences in the remaining measures was uncertain because the analyses did not account for clustering.
Health Care Professional Experience
The evaluation of Guided Care is the only rigorous evaluation of professional experience. Its findings were inconclusive; the intervention-control differences were either not statistically significant or had uncertain statistical significance because the analyses did not account for clustering (Table 14).
Effects Over Time
We expect the effects of the medical home on key outcomes to follow different time paths. Transformation to a medical home could change outcomes in multiple, often nonlinear, ways, and improvements in outcomes could occur at different times. For example, improving access to care could lead to identification and management of ambulatory-care-sensitive conditions, which could reduce ambulatory-care-sensitive hospitalizations and ED visits. However, other changes could lead to worse outcomes in the short term. For example, more systematic assessment of patient care needs could uncover unmet needs, which could increase cost and service use in the short run but eventually reduce them through avoidance of acute exacerbations in the long run. Similarly, the introduction of health IT and new team members could create significant new organizational challenges for a practice, which could initially worsen patient and professional experience; however, these outcomes could improve over time through, for example, lower medication errors and quicker record-keeping.
In addition, for a given outcome, we expect the time path of effects to vary across different subgroups of patients. For example, costs for patients who had been receiving unnecessary care might fall, while those for patients with unmet needs might rise. For example, increased use of preventive care could increase short term costs but decrease them over longer periods. In the case of hospital use, patients who are at high risk of immediate hospitalization, such as those with severe congestive heart failure, might benefit from improved access to care and experience reductions in hospitalizations.
Unfortunately, summarizing the evidence on effects over time is currently difficult, for several reasons. First, the interventions, implementation settings, and target populations differ, which makes it impossible to ascertain whether differential effects over time reflect a true time path rather than these differences. Second, evaluations often used different followup periods, which make it difficult to compare time paths of effects on a given outcome across evaluations. Third, with the exception of costs, evaluations differed in the measures they used to examine a given outcome, which again makes it difficult to summarize effects over time across evaluations. Fourth, within a study, formal statistical testing would be needed to determine whether an effect that appears to grow over time is really statistically different across the followup periods; none of the evaluations performed such testing.
For example, in the case of costs, only one of the four rigorous evaluations (GRACE) examined effects over time. It found that it took 3 years for favorable effects on costs for high-risk patients to emerge; however, differences over time were not formally tested. In the case of hospital use, four of the five evaluations providing rigorous evidence on different measures of hospital use examined effects over time. Of these, two provided conclusive evidence but found different time paths. For GRACE, it took time for favorable effects on the number of hospital stays for high-risk patients to emerge. In contrast, VA TM/HBPC’s reduction in readmissions over the first 6 months disappeared with an additional 6 months of followup.
Current evidence on time paths is limited for the reasons cited above. Future evaluations could make a valuable contribution to the field by explicitly incorporating logic models based on theories of change and clinical experience to guide examination of key outcomes over time.
Generalizability of This Evidence Is Limited
Because the interventions were tested on particular types of patients and practices, the evidence from these six evaluations may not be applicable to the typical patient panel in the average primary care setting in the United States. All interventions targeted patients who were older and sicker (and thus at higher risk of poor health outcomes) than the general population in a practice. GRACE served low-income Medicare beneficiaries; IMPACT, patients aged 60 and over with depression; VA TM/HBPC, veterans with terminal or very complex chronic diseases; Guided Care, 25 percent of the sickest Medicare beneficiaries in a practice; CMP, chronically ill Medicare beneficiaries; and GHS ProvenHealth Navigator, Medicare Advantage patients. All were tested in practices that were part of a larger delivery system. Decisionmakers must use caution when trying to extrapolate from lessons learned from these early evaluations.back to top
Chapter 7. Conclusion and Next Steps
- The PCMH model is a promising approach to improving health care delivery, but stronger evaluations of the current model are needed to determine how it alters each of the key outcomes and how it could be refined to enhance effectiveness. Information about which patients benefit and under what circumstances will be key to interpreting this evidence.
- There are many opportunities to improve the future evidence base. Some of these are specific to evaluations of the PCMH, and others are general best practices for conducting high-quality health service evaluations:
- Use strong evaluation designs and methods.
- Conduct comprehensive implementation studies.
- Test the model in an adequate number of practices and measure different outcomes for different subgroups of patients.
- Follow outcomes for longer periods of time (despite pressure for rapid research results).
- Improve reporting and documentation of evaluation methods.
- Independently evaluate the models to ensure transparency and objectivity.
- Test the model in typical practices and among typical patients.
- Develop a core set of outcome measures and standardized measures of PCMH components.
- Measure effects on all triple aim outcomes and professional experience.
- Explore novel approaches to evaluating PCMH interventions.
Our review of the PCMH reveals that most of the current evidence on the PCMH has limited direct applicability to the PCMH model being tested today, for two reasons. First, most interventions are best viewed as precursors to the PCMH. They include some, but not all, aspects of the model. This reflects the recent rise in interest in the model: the joint principles on the PCMH were released in 2007, and we reviewed evidence through September of 2010. Second, they were tested on generally older and sicker-than-average patients, and in unique implementation settings. More research is needed to determine whether the PCMH model currently being tested will improve outcomes for all patients in typical primary care practices around the Nation.
We found that six interventions were evaluated using rigorous methods for at least one outcome. These interventions had a range of effects on key outcomes: some favorable effects on quality, and patient and caregiver experience of care; a few unfavorable effects on costs; and many inconclusive results on all outcomes. Inconclusive results are not evidence for or against the effectiveness of an intervention. Well-designed evaluations with adequate power and rigorous methods are needed to determine effectiveness.
We now summarize the findings and how they fit into the literature, cite considerations for decisionmakers applying the evidence to current medical home initiatives, discuss limitations of this review, and suggest ways to build a better evidence base.
Summary of Findings
With the exception of some favorable effects on quality of care, hospital and ED use, and patient and caregiver experience of care, and a few unfavorable effects on costs, the findings on the effectiveness of precursors to the PCMH are largely inconclusive, either because the sample size was insufficient to detect effects if they exist or because the statistical significance of the effects was potentially overstated owing to lack of adjustment for clustering of patients within practices. More evidence from ongoing evaluations of current PCMH initiatives is needed to determine whether the PCMH as it is currently being implemented improves the quality, affordability, and experience of care; under what circumstances it does so; and how refinements to the model can increase its effectiveness. Table 15 provides a high-level summary of the rigorous evidence by outcome (described in detail in Chapter 6).
- Quality of Care. There were favorable effects on quality of care among the rigorous evaluations: one of three evaluations reported improvements in processes of care, and two of three reported improvements in some measures of health outcomes. However, the remaining evaluations examining these outcomes, as well as both evaluations examining mortality, produced inconclusive evidence.
- Cost and Service Use. The rigorous evidence on cost and service use shows limited favorable effects, some unfavorable effects on cost, and many inconclusive results. The evaluation of GRACE was the only one of four evaluations to find any evidence of savings, and these were limited to the high-risk subgroup of Medicare patients in the third (or post-intervention) year. However, both GRACE and VA TM/HBPC increased total costs during the intervention, while evidence from the other two interventions—Guided Care and IMPACT—was inconclusive. Similarly, GHS ProvenHealth Navigator was the only one of five rigorous evaluations to report reductions in hospital use for its full sample of patients; two other evaluations (of GRACE and VA TM/HBPC) reported reductions only for their high-risk subgroups in some followup periods. Evidence on hospital use from the remaining evaluations (of Guided Care and CMP) was inconclusive. Similarly, one of three evaluations (of GRACE) found reductions in ED use in one of the two followup periods, but evidence from the remaining evaluations (of Guided Care and CMP) was inconclusive.
- Experience of Care. The rigorous evidence on patient and caregiver experience shows some favorable effects, while the rest of the evidence is inconclusive.
- Professional Experience. The lone evaluation with rigorous evidence on professional experience is inconclusive.
Placing the Findings in Context
The findings are less conclusive than those from most prior reviews. We found some promising results across quality of care, hospital and ED use, and patient and caregiver experience; however, the majority of findings were inconclusive. The conclusions we draw are consistent with those of Friedberg, Lai, Hussey, et al. (2009), who described the evidence in favor of the medical home as “scant.” Our conclusions are more tentative than those of Homer, Klatka, Romm, et al. (2008); Fields, Leshen, and Patel (2010); and Grumbach and Grundy (2010), who claimed overwhelming evidence in support of the medical home. We conclude that more work, including additional evaluations that are well-designed, implemented, and analyzed, is needed to guide decisionmakers on this young, rapidly evolving model.
Findings from the rigorous evaluations reflect unique contexts and patients. All were tested in practices that were part of larger delivery systems and targeted patients who were older and sicker than the average in U.S. primary care practices (see Table 5). As a caveat, we expect it will be harder to generate cost and service use effects of the same size among healthier patients, who do not use many services.
The improvements in cost and service use may have been concentrated among the sickest patients. Two of the six rigorous evaluations examined outcomes for different high-risk subgroups of patients among their target population of older or sicker patients.24 The evaluation of GRACE reported that even among its low-income, elderly patients, improvements were concentrated among the sickest. The evaluation of the VA TM/HBPC intervention found favorable effects among severely disabled veterans but not among other high-risk veterans; it is unclear whether this reflects lack of power to detect effects (a result of small samples), lack of followup periods long enough for effects to emerge, or a true lack of effects.
These results, while limited, raise the question of whether conducting additional analyses on sicker patients could be a useful approach for future evaluations. The highest-risk patients present providers with more opportunities to take action to reduce service use and costs in the relatively short followup periods observed because a medical home intervention is likely to reduce hospitalizations more for patients who are frequently hospitalized. In addition, there is better power to detect effects among the highest-risk patients than among all patients, reducing the likelihood of missing important beneficial effects (Peikes, Dale, Lundquist, et al., 2011). This does not imply that the PCMH should be targeted only to patients with complex medical needs. The PCMH is a whole-practice-level intervention and is expected to improve care for all. It is critical not to confuse the goal of the intervention with suggestions for refining evaluations.
Findings from multi-component medical home interventions in other settings will likely differ. Decisionmakers should consider context when interpreting findings: the findings on effectiveness will differ if the full medical home model is implemented, and with different practices, markets, and patients. For example, implementing the PCMH model in certain markets or delivery settings where there is overuse of care could produce results different from those in areas where care is underused. Similarly, modifications of the interventions might alter outcomes. For example, it is possible that adding certain components of the medical home, such as health IT and stronger financial incentives to practices, could improve outcomes. In addition, program designers may be able to identify areas in which efficiency can be increased to achieve cost neutrality or generate savings. For example, although this information was not provided in reports of these evaluations, a careful review of which team members are best suited to provide which parts of interventions could lower the costs of care.
This evidence review has several limitations, many of which stem from the fact that the PCMH, and therefore research on the PCMH, are in their nascent stages. First, the summary we present here is based on the limited number of well-evaluated interventions currently available, and on interventions that are often related, but not identical, to the PCMH model. Therefore, these findings have limited applicability to the medical home as it is currently being rolled out throughout the country. Second, we did not have resources to survey the model implementers to fully characterize the models tested and how faithful the actual implementation was to what was planned. A survey or an in-depth interview would be needed for this purpose, because most articles in the literature gave brief descriptions of the interventions, using language and categories that were not standardized, and few described the fidelity of the implementation to its original design. Third, most evaluations did not report the statistical power to detect effects. If an evaluation has an insufficient number of practices and is underpowered, it is unlikely to detect plausible effects even when they exist. Therefore, there is a chance that the number of programs that truly had effects (favorable or unfavorable) is understated in our synthesis, because we were forced to classify so many of them as inconclusive. Finally, publication bias might lead us to overstate favorable effects because journals are less likely to publish studies that find no effects or unfavorable effects.
Guidance to Improve the Future Evidence Base
This review highlights opportunities to improve the evidence base on the PCMH going forward. There is a large risk that research currently under way on PCMH interventions (not reviewed here) will fail to support decisionmakers’ information needs. A survey of 26 current medical home pilots in 18 States concluded that only 40 percent had well-developed evaluation plans. Among those with plans, only about 40 percent planned to use a comparison group design, and the others planned to use pre-post designs (Bitton, Martin, and Landon, 2010), which typically provide weak evidence.
The challenges to conducting rigorous evaluations are not unique to the PCMH. In 2011, the GAO criticized evaluations of 127 diverse health care interventions for weak evaluation designs, limited generalizability, and failure to report the outcomes of interest (in their case, quality and cost) (U.S. Government Accountability Office, 2011). Below we describe a number of steps that can be taken to improve the evidence base. Some of these are specific to evaluations of the PCMH, and others are general best practices for conducting rigorous health service evaluations:
- Use strong evaluation designs and methods. Weak designs and analytical methods severely limit the potential of a strong intervention to produce rigorous evidence for decisionmakers. Current and future evaluators of PCMH interventions have an opportunity to fill knowledge gaps and contribute to ongoing learning on PCMH effectiveness. One challenge for a good evaluation of the medical home is to make sure that practices and patients in the intervention and comparison groups are comparable at baseline. Otherwise, it is difficult to distinguish effects that are due to the intervention from preexisting differences between the intervention and comparison practices and patients. Evaluations should also use rigorous analytical methods, including adjusting analyses for clustering of patients within practices (seePeikes, Dale, Lundquist, et al., 2011).
- Conduct comprehensive implementation studies. We found that most evaluations did not report how the intervention was implemented. While undertaking an implementation study requires additional expertise and resources, it adds tremendous value in identifying barriers and facilitators to improving outcomes, how findings might generalize to other contexts, and ways to refine the model. Implementation evaluations can provide powerful insights on their own, as well as when combined with quantitative outcome studies (a mixed-methods approach).25
- Test the model in an adequate number of practices and measure different outcomes for different subgroups of patients. Because the PCMH is a practice-level intervention, it must be tested in a large number of practices, or the evaluation is likely to lack the statistical power to detect effects even when they exist. As discussed in Chapter 2, measuring costs and service use among sicker patients permits detection of smaller effects than among all patients. In contrast, measures for quality of care and patient experience typically take on a small number of values and result in less variation; therefore, effects on these outcomes can be examined and more easily detected among all patients.
- Follow outcomes for longer periods of time. Evaluations examined outcomes for 1 to 3 years, with most following patients for 2 years. While most decisionmakers are eager to obtain results, given the dramatic changes many practices must undergo to become medical homes, a short followup period might provide an overly pessimistic view of the medical home by capturing the negative effects of disruptive transformation. Consistent with this possibility, GRACE substantially increased costs by 28 percent early in the intervention, but became cost neutral a year after the intervention ended. However, the VA TM/HBPC evaluation found that short-term favorable effects on read missions dissipated over time. Evaluation designs should also explicitly consider the periods of time needed to observe the effects of PCMH interventions on health care processes and subsequently on various health outcomes; information from early evaluations might be useful in modeling time paths of effects on different outcomes.
- Improve reporting and documentation. Many evaluations were not documented well enough to assess the strength of their methods. To allow objective assessment of the evidence, evaluation results—even preliminary results or results from pilot studies—should be accompanied by a detailed description of the methods used.
- Independently evaluate the interventions to ensure objectivity. Many evaluations were conducted by intervention developers. While developers have deep knowledge of their initiatives and a commitment to learning about them, independent evaluations might provide more credible evidence. At a minimum, peer review of evaluations conducted by developers would build a better evidence base.
- Test the model in typical practices and among typical patients. All six interventions with rigorous evidence were tested exclusively in practices in larger delivery systems that had some degree of integration across providers. Therefore, these results may not apply to independent practices. Ideally, future research would test the PCMH model in practices that are representative of the primary care landscape. In terms of patients, all six interventions were tested on those that were older or sicker than average. Also, while testing effects on specific patient populations is appropriate for evaluating specific research questions, as a practice-level intervention, the PCMH must be implemented for more diverse populations as decision makers still require evidence of effectiveness for the general patient population.
- Examine a core set of outcome measures and develop standardized measures of PCMH components. Estimating effects on a core set of standardized outcome measures would enable a meta-analysis of findings across different interventions, while still enabling researchers to accommodate the unique needs of their stakeholders. A meta-analysis can dramatically improve the power to detect effects compared to individual evaluations, which are often underpowered. The body of evidence would also be improved if researchers used detailed, standardized measures of PCMH components and processes. Such measures would enable meta-analyses to discern which intervention features are most effective in which settings. The Commonwealth Fund (2011) has convened a collaborative for medical home evaluators to support the creation of a uniform research infrastructure that can guide PCMH evaluations.
- Measure effects on all triple aim outcomes and professional experience. The PCMH model grew out of the need to improve quality and experience while reducing costs. It is critical that evaluations examine all these outcomes if they are to provide comprehensive information to decisionmakers. Improving one type of outcome may not warrant model adoption if it comes at the expense of deterioration in other outcomes. Examining the full range of outcomes might require addressing a number of barriers, including payer concerns about confidentiality of cost data, limited resources to collect and analyze multiple data sources, and lack of validated tools to measure certain outcomes.26
- Explore novel approaches to evaluating PCMH interventions. A number of studies over the past decade have shown that health care interventions can be viewed as complex interventions within a complex adaptive system (CAS), similar to processes in ecology, computer science, and organizational science. Complexity science views the multiple components of complex interventions such as the PCMHas dependent on each other, as well as on the primary care practice and health care setting (Plsek and Greenhalgh, 2001). For example, quality of care delivered by a practice can be viewed as a system-level property that arises over time from the interactions among the members of the practice (Lanham, McDaniel, Crabtree, et al.,2009). As a result, in addition to individual processes or components, the relationships among practice team members become key levers for improving outcomes. Furthermore, the framework’s emphasis on the importance of the external environment underscores the influence of the medical neighborhood on key outcomes. Some evidence indicates that interventions designed and implemented using CAS principles are more effective at improving clinical outcomes (Leykum,Parchman, Pugh, et al., 2010; Leykum, Pugh, Lawrence, et al., 2007).
Principles of complexity science might be used to create better approaches to evaluate PCMH interventions, including designing more insightful implementation analyses (Litaker, Tomolo, Liberatore, et al., 2006; Campbell, Fitzpatrick, Haines, et al., 2000; Craig, Dieppe, Macintyre, et al., 2008; Stetler, Damschroder, Helfrich, et al., 2011; Damschroder, Aron, Keith, et al., 2009; Nutting, Crabtree, Stewart, et al. 2010; May, Mair, Dowrick, et al., 2007; Cohen, McDaniel, Crabtree, et al., 2004). Measures of the internal and external environment might be useful in both (1) selecting comparison practices that closely resemble the intervention practices, and (2) helping explain why an intervention was more successful in certain contexts than in others. More work is needed to develop such measures.27 In addition, from a complexity framework, attempts to isolate the relative contributions of individual components of the medical home are ill-advised and are likely to result in misleading findings, because these components are dependent on each other to achieve the desired outcomes of medical home implementation.
By moving away from a mechanistic and reductionist perspective, complexity frameworks and other new approaches may help us evaluate PCMH interventions in more insightful ways.
Although the PCMH is a promising innovation, rigorous quantitative evaluations and comprehensive implementation analyses are needed to assess effectiveness and refine the model to meet stakeholders’ needs. Findings from future evaluations can guide the substantial efforts of practices and payers to adopt the PCMH and ensure that the revitalized primary care system achieves the triple aim outcomes in a sustainable manner.back to top
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- 1 These interventions are not static; although most were implemented before the joint principles were released, many were subsequently adapted to look more like the medical home and continue to evolve today.
- 2 For example, a practice interested in decreasing the time between the receipt of laboratory results and patient notification need not wait for the results of a rigorous, controlled evaluation. It could convene the practice team members to redesign their workflow and measure changes in outcomes of interest (such as percentage of results delivered within two days) before and after implementation of the redesigned process. This approach provides quick answers to a low-cost initiative. While decisionmakers may require solid evidence on outcomes to justify large, transformative investments in primary care, for smaller initiatives, overreliance on rigorous evaluations carries the risk of delaying beneficial changes (Gold, Helms, and Guterman, 2011).
- 3 The AHRQ definition builds on the traditional definition of primary care established by the Institute of Medicine and Barbara Starfield (Donaldson, Yordy, Lohr, et al., 1996; Starfield, 1992, 2008) and incorporates aspects of the expanded care model (Barr, Robinson, and Marin-Link, 2003; Glasgow, Orleans, Wagner, et al., 2001). It is similar to the definition of the medical home provided in the joint principles but places a greater emphasis on team-based care.
- 4 See Appendix A for a detailed version of the AHRQ PCMH definition. Also available at: http://pcmh.ahrq.gov/page/defining-pcmh. Accessed August 23, 2012.
- 5 We note that pilots and demonstrations are testing different variants of the model. The variants reflect different ways of operationalizing the principles that we refer to collectively as the PCMH model.
- 6 One intervention, Community Care of North Carolina [CCNC], was evaluated by three distinct studies.
- 7 None of the studies reported effects on out-of-pocket patient costs or practice revenues.
- 8 The term control group is used exclusively when the group was assigned using an RCT. The term comparison group indicates that the group was selected using nonexperimental comparison group methods.
- 9 In addition to the USPSTF review methods (see Harris, Helfand, Woolf, et al., 2001), we drew specific operational criteria from the What Works Clearinghouse (WWC) review of educational interventions (which also typically employ clustered designs, like the many practice-level interventions reviewed here (see http://ies.ed.gov/ncee/wwc/pdf/reference_resources/wwc_procedures_v2_1_standards_handbook.pdf) and from an evidence review of home visiting programs for families with pregnant women and children (see http://www.mathematica-mpr.com/EarlyChildhood/homvee.asp).
- 10 A subgroup is considered endogenously formed, and estimates of effects for this subgroup considered biased, if the subgroup is defined on the basis of a followup (or post-randomization) value of an outcome that the intervention can potentially affect. The extent of this bias may be small if the intervention and control arms of the subgroup are comparable at baseline, or if the intervention had no effect on the outcome that defines the subgroup.
- 11 To make the correction for clustering, we used the methodology of the What Works Clearinghouse (WWC), which can be found in Appendix C of the WWC Procedures and Standards Handbook (http://ies.ed.gov/ncee/wwc/references/idocviewer/Doc.aspx?docId=19&tocId=9).
- 12 See Appendix B for details on this approach. Peikes, Dale, Lundquist, et al. (2011) discuss the importance of adjusting for clustering and how to do so in more detail.
- 13 For example, Guided Care found that the costs for the intervention group were $75,000 lower than those for the control group, but this difference was not statistically different from zero. In this case, the researchers reported the 95 percent confidence interval, which conveys that, if the intervention were tested 100 times, 95 times out of 100, the difference could randomly fluctuate anywhere from a savings of $244,000 to a cost increase of $151,000.
- 14 Aetna’s Embedded Case Managers, CCNC, Group Health Cooperative Medical Home, GHS ProvenHealth Navigator, Merit Health System and BCBS of North Dakota Chronic Disease Management Pilot, and Pennsylvania Chronic Care Initiative provided financial support or incentive payments. Of these, GHS ProvenHealth Navigator, Merit Health System and BCBS of North Dakota Chronic Disease Management Pilot, and Pennsylvania Chronic Care Initiative provided funding for startup, infrastructure, and/or practice transformation costs. GHS ProvenHealth Navigator, Group Health Cooperative Medical Home, and Pennsylvania Chronic Care Initiative provided stipends to providers while Aetna’s Embedded Case Managers and CCNC provided payments for patients served or enrolled in the program. Finally, all but CCNC implemented shared savings or performance incentive programs and all but Pennsylvania Chronic Care Initiative provided care managers.
- 15 The four included IMPACT, Guided Care, GRACE, and Care Management Plus.
- 16 As described in Chapter II, NDP and IMHP are excluded because they tested the effect of facilitated versus non-facilitated implementation of the medical home model, rather than the effect of the model itself. These interventions made an important contribution to the field, so we briefly describe them along with their implementation settings and target populations: The NDP was comprehensive. It included elements of each of the five principles in the medical home definition and emphasized the use of health IT. Practices did not receive any funding or additional practice staff. Eighteen practices received practice facilitation to become a medical home; the other 18 sought to become medical homes on their own. The 36 practices are diverse, and include small, independent practices in 25 States. The practices served all patients (Jaén, Ferrer, Miller, et al., 2010; Nutting, Crabtree, Stewart, et al., 2010). In contrast to most studies, the NDP provided rich insights about the implementation experience. The IMHP model was tested in pediatric primary care practices and included elements of four of the five core principles; the model included no changes in access to care. Five practices received practice facilitation to become a medical home; the other five received technical assistance, but no facilitation, to become a medical home. These 10 practices served children and youth with special health care needs in urban, suburban, and rural regions of Illinois. All practices received small mini-grant stipends (up to $5,000 per year for 2 years); training for quality improvement teams, which included family members; and medical home resources, including the Center for Medical Home Improvement tool kit, training materials, and other resources (American Academy of Pediatrics Illinois Chapter, 2009).
- 17 Two of these studies used cross-sectional or pre-post designs to examine some outcomes, as noted in Table 3.
- 18 See http://www.commonwealthfund.org/Publications/Data-Briefs/2012/May/Measures-Medical-Home.aspx.
- 19 The primary rationale for a low rating varied across the four evaluations as follows:
- Community Care of North Carolina. Two evaluations of CCNC used comparison group designs, but their analyses did not establish whether the intervention and comparison groups had comparable values of the outcome at baseline.
- Group Health Cooperative Medical Home and Merit Health System and Blue Cross Blue Shield (BCBS) of North Dakota Chronic Disease Management Pilot. Analyses from both evaluations received a low rating because these interventions were implemented in a single intervention practice. While such a design can represent an important opportunity to pilot a new intervention and break ground toward a larger evaluation, it cannot distinguish the effects of the intervention from other characteristics of the particular practice that implemented it, thereby undermining the ability of attributing an observed effect to the intervention.
- 20 Because most studies do not report all this information, our formal rating criterion was more liberal: we assessed only whether the intervention and comparison groups had comparable baseline values of the outcome being examined.
- 21 As the table indicates, the intervention group had a worse score than the control group for one of the eight SF-36 scales; however, it is unlikely that the intervention could have reduced functioning. Because we expect 1 of every 20 intervention-control differences to be statistically significant by chance, this statistically significant finding may have arisen simply from testing multiple differences rather than as an effect of the intervention.
- 22 While we present point estimates of the statistically significant effects, we note that the true effect could lie anywhere in a confidence interval around the point estimate.
- 23 The evaluation of GRACE was the only one to measure outcomes after the intervention had ended.
- 24 CMP did so, too, but results are inconclusive owing to the lack of adjustment for clustering.
- 25 Creswell, Klassen, Clark, et al., (2011) provide useful guidance on mixed methods. Crabtree, Chase, Wise, et al., (2011) emphasize the necessity of a mixed-methods approach when evaluating the PCMH model.
- 26 The 2011 release from AHRQ of PCMH-Consumer Assessment of Healthcare Providers and Systems (PCMH-CAHPS) may address one such barrier and enable future evaluators to measure patient experience more easily. Built on the existing, well-validated Clinician and Group survey, the PCMH-CAHPS covers topics such as provider-patient communication, coordination of care, and shared decisionmaking, and is available in adult and child versions, and in English and Spanish (https://www.cahps.ahrq.gov/surveys-guidance/item-sets/PCMH/index.html).
- 27 For example, measures of the external environment within which a PCMH operates could build on Parchman, Scoglio, and Schumm’s (2011) modeling of health care delivery across a network of providers.
Appendix A: AHRQ's Definition of the Patient-Centered Medical Home
Appendix B: Methods for Reviewing the Evidence on the Patient-Centered Medical Home
Appendix C: Supplemental Table on Descriptions of the Interventions, by AHRQ PCMH Principles and FacilitatorsView appendices in new window