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The Association Among Medical Home Readiness, Quality, and Care of Vulnerable Patients
Lena M. Chen, MD, MS; Joseph W. Sakshaug, PhD; David C. Miller, MD, MPH; Ann-Marie Rosland, MD, MS; and John Hollingsworth, MD, MS
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The Association Among Medical Home Readiness, Quality, and Care of Vulnerable Patients

Lena M. Chen, MD, MS; Joseph W. Sakshaug, PhD; David C. Miller, MD, MPH; Ann-Marie Rosland, MD, MS; and John Hollingsworth, MD, MS
The characteristics of patients who visit practices that are ready versus unready for the patient-centered medical home differ in important ways.

We examined 17,358 visits, representing 341 million adult outpatient visits to general practitioners and internists during the study interval. Nearly three-fourths occurred at practices ready for the PCMH (Table). Compared with patients seen at visits to unready practices, patients seen at visits to ready practices were more likely to have 3 or more comorbid conditions (25% of visits to PCMH-ready practices vs 17% of visits to other practices; P = .001). However, visits to both types of practices were similar with respect to race/ethnicity and socioeconomic status. Practice characteristics of both types of visits were also similar except for physician ownership and rural location, although this latter finding was not statistically significant (P = .058). In sensitivity analyses with broader categories, 2 findings became statistically significant. When we compared visits in the South with all other practices, visits in the South were less likely to occur at PCMH-ready compared with unready practices (36% vs 52%; P = .049). Similarly, when we compared privately insured visits with all other visits, privately insured visits were more likely to occur at PCMH-ready compared with unready practices (57% vs 51%; P = .047).

Visits to PCMH-ready practices were generally more likely to meet the medication and counseling/screening quality indicators, although this difference reached statistical significance for only 3 indicators (Figures 1 and 2). For example, 18% of visits to ready practices included diet counseling, compared with 12% of visits to unready practices (P = .018). Similarly, exercise counseling occurred at 14% of visits to ready practices, but at only 5% of visits to unready practices (P <.001). For medication quality indicators, at visits to ready practices (vs unready ones), 41% (vs 32%) of patients were prescribed a diuretic or beta-blocker for hypertension (P values for difference = .031).

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

Our study must be considered in the context of prior work on the value of health information technology (IT) tools, which the NCQA PCMH standards emphasize heavily. Many of the NAMCS items that we evaluated mapped to the NCQA elements that capture health IT tools. To the extent that the use of health IT improves chronic disease management,20 our finding that visits by patients with multiple chronic conditions are more likely to occur at ready practices is reassuring. Second, evidence is still being gathered to assess whether medical homes deliver better-quality care across multiple domains (eg, patient satisfaction, clinical processes of care, patient outcomes).1,3,21 While a fully developed medical home is clearly more than the sum of its parts, our observation of modestly higher quality among visits to ready practices provides 1 piece of evidence to suggest that adoption of PCMH components may be beneficial to care quality.

Our study has several important limitations. First, we ascertained PCMH readiness by matching survey responses about a practice’s infrastructure and processes of care to 1 standard of medical home readiness: the NCQA PCMH standard. However, prior studies have utilized this method,13,22 and the NCQA standard has been widely adopted and was developed with input from organizations such as the American College of Physicians.23 Second, because the NAMCS questions are not based on the NCQA PCMH standard, we could not perfectly match NCQA elements to NAMCS questions, nor could we match all NCQA elements to an NAMCS question. This may have resulted in some misclassification of ready practices as unready or vice versa, but the directionality of this bias is difficult to ascertain. Prior work has found the NAMCS-NCQA matching approach robust to missing data (see eAppendix 2). Third, our measures of quality were limited by the cross-sectional nature of the NAMCS survey. However, the quality measures have been validated,16-19 and one of the criteria for constructing these measures with NAMCS data was that the measures have a high likelihood of correlating with improved patient outcomes.17 Nevertheless, we cannot make conclusions about causality between medical home readiness and quality of care. Fourth, it is possible that better scores on exercise and diet counseling measures for PCMH-ready versus unready practices reflect better documentation at PCMH-ready practices, since many of the NCQA measures that we mapped to NAMCS rely on health IT tools.
In spite of these limitations, our findings on care quality have implications for PCMH implementation strategy. To date, policy makers have often tested these models in practices most ready for transformation into a fully recognized PCMH.12,24 For example, 2 large CMS programs provide incentives to practices that already have some medical home capabilities. Criteria for inclusion in CMS’ Comprehensive Primary Care Initiative include having some level of medical home recognition. CMS’ Multi-payer Advanced Primary Care Practice Demonstration provides a monthly care management fee for beneficiaries receiving primary care from a medical home. Moreover, programs often require that participating practices attain a certain level of medical home readiness as early as 1 year after enrollment. This too would seem to favor the participation of practices that are PCMH-ready (or nearly ready). Our data support these efforts, as they suggest that such a strategy will not leave out a disproportionate number of visits by poor or minority patients, or by patients with multiple comorbidities.1,4-7

While it is reasonable to design PCMH programs that may attract the most PCMH-ready practices first, policy makers should also take steps to ensure that the least ready practices are not left behind. This would also shed light on the broader generalizability of results from early implementation efforts at PCMH-ready practices. Future research should examine whether or not PCMH incentives that account for baseline levels of medical home readiness are effective in bringing along the least ready practices. For example, in the Medicare Shared Savings Program, tailored incentives exist for provider organizations at different stages of becoming an accountable care organization25; a similar approach might be utilized to support PCMH adoption by a wide range of practices. Implementation of PCMHs will require as much care as ongoing, related work that assesses the net benefits of PCMH adoption.

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

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

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

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

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

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


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