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BackgroundThe patient-centered medical home (PCMH) is a team-based, comprehensive model of primary care. When effectively implemented, PCMH is associated with higher patient satisfaction, lower staff burnout, and lower hospitalization for ambulatory care-sensitive conditions. However, less is known about what factors contribute to (or hinder) PCMH implementation.We explored the associations of specific facilitators and barriers reported by primary care employees with a previously validated, clinic-level measure of PCMH implementation, the Patient Aligned Care Team Implementation Progress Index (Pi2).MethodsWe used a 2012 survey of primary care employees in the Veterans Health Administration to perform cross-sectional, respondent-level multinomial regressions. The dependent variable was the Pi2 categorized as high implementation (top decile, 54 clinics, 235 respondents), medium implementation (middle eight deciles, 547 clinics, 4537 respondents), and low implementation (lowest decile, 42 clinics, 297 respondents) among primary care clinics. The independent variables were ordinal survey items rating 19 barriers to patient-centered care and 10 facilitators of PCMH implementation. For facilitators, we explored clinic Pi2 score decile both as a function of respondent-reported availability of facilitators and of rating of facilitator helpfulness.ResultsThe availability of five facilitators was associated with higher odds of a respondent’s clinic’s Pi2 scores being in the highest versus lowest decile: teamlet huddles (OR = 3.91), measurement tools (OR = 3.47), regular team meetings (OR = 2.88), information systems (OR = 2.42), and disease registries (OR = 2.01). The helpfulness of four facilitators was associated with higher odds of a respondent’s clinic’s Pi2 scores being in the highest versus lowest decile. Six barriers were associated with significantly higher odds of a respondent’s clinic’s Pi2 scores being in the lowest versus highest decile, with the strongest associations for the difficulty recruiting and retaining providers (OR = 2.37) and non-provider clinicians (OR = 2.17). Results for medium versus low Pi2 score clinics were similar, with fewer, smaller significant associations, all in the expected direction.ConclusionsA number of specific barriers and facilitators were associated with PCMH implementation, notably recruitment and retention of clinicians, team huddles, and local education. These findings can guide future research, and may help healthcare policy makers and leaders decide where to focus attention and limited resources.

Introduction Racial/ethnic disparities exist in the Veterans Health Administration (VHA), despite financial barriers to care being largely mitigated and Veterans Administration’s (VA) organizational commitment to health equity. Accurately identifying minority veterans is critical to monitoring progress toward equity as the VHA treats an increasingly racially and ethnically diverse veteran population. Although the VHA’s completeness of race and ethnicity data is generally better than its public sector and private counterparts, the accuracy of the race and ethnicity in the various databases available to VHA is variable, as is the accuracy in identifying specific minority groups. The purpose of this article was to develop an algorithm for constructing race and ethnicity variables from data sources available to VHA researchers, to present demographic differences cross the data sources, and to apply the algorithm to one study year. Materials and Methods We used existing VHA survey data from the Survey of Healthcare Experiences of Patients (SHEP) and three commonly used administrative databases from 2003 to 2015: the VA Corporate Data Warehouse (CDW), VA Defense Identity Repository (VADIR), and Medicare. Using measures of agreement such as sensitivity, specificity, positive and negative predictive values, and Cohen kappa, we compared self-reported race and ethnicity from the SHEP and each of the other data sources. Based on these results, we propose an algorithm for combining data on race and ethnicity from these datasets. We included VHA patients who completed a SHEP and had race/ethnicity recorded in CDW, VADIR, and/or Medicare. Results Agreement between SHEP and other sources was high for Whites and Blacks and substantially lower for other minority groups. The CDW demonstrated better agreement than VADIR or Medicare. Conclusions We developed an algorithm of data source precedence in the VHA that improves the accuracy of the identification of historically under-identified minorities: (1) SHEP, (2) CDW, (3) Department of Defense’s VADIR, and (4) Medicare.

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