Behavioral Health Integration in Community Health Centers and Emergency Department Use.

Medicaid enrollees with behavioral health disorders often experience fragmented care, leading to high rates of preventable use of emergency departments (EDs). As part of its Medicaid Transformation Program, the Washington Health Care Authority partnered with regional accountable communities of health to collect data on behavioral health integration in community health centers. Clinics who participated in the integrated care demonstration received technical and financial support to increase capacity for integration. This column describes results from an analysis that linked clinic surveys to Medicaid claims to explore characteristics of highly integrated clinics and assess whether clinic capacity for behavioral health integration is associated with ED visit frequency.

Components of the Patient-Centered Medical Home Associated with Perceived Access to Primary Care.

BACKGROUND: Following implementation of the patient-centered medical home (PCMH) within the Department of Veterans Affairs (VA), access to primary care improved. However, understanding of how this occurred is lacking. OBJECTIVE: To examine the association between organizational aspects of the PCMH model and access-related initiatives with patient perception of access to urgent, same-day, and routine care within the VA. DESIGN: Cross-sectional PARTICIPANTS: Veterans who responded to the annual Survey of Healthcare Experiences of Patients in 2016 (N = 241,122 patients) and primary staff who responded to VA National Primary Care Provider and Staff Survey (N = 4815 staff). MAIN MEASURES: Three outcomes of perception of access: percentage of patients responding in the highest category for same-day care (waiting ≤ 1 day), urgent care (always receiving care when needed), and routine care (always receiving checkups when desired). Predictors were staff-level report of access-related initiatives and organizational factors in the clinic. We used generalized estimating equations to model associations, adjusting for characteristics of patients and their respective clinics. KEY RESULTS: Access was significantly better in clinics where staff reviewed performance reports (+ 0.9% in the highest perception of access for urgent care, P < 0.01; + 1.2% for routine care, P < 0.001), leadership was supportive of the PCMH (+ 1.6% for urgent care, P < 0.01), and initiatives to improve access included open access (+ 0.8% to + 1.7% across all outcomes, P < 0.01) and telehealth visits (+ 1.2% to + 1.4%, P < 0.001). Perceived access was worse in clinics with moderate staff burnout (- 1.1% to - 1.4%, P < 0.001), primary care provider turnover during the past year (- 1.0% to - 1.6%, P < 0.001), or medical support assistant turnover in the past year (- 0.9% to - 1.4%, P < 0.001). CONCLUSIONS: Perception of access was strongly associated with identifiable organizational factors and access-related initiatives within VA primary care clinics that could be adopted by other health systems.

Developing an Algorithm for Combining Race and Ethnicity Data Sources in the Veterans Health Administration.

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.

The Association Between Neighborhood Socioeconomic and Housing Characteristics with Hospitalization: Results of a National Study of Veterans.

BACKGROUND: Social determinants of health (SDOH) have an inextricable impact on health. If remained unaddressed, poor SDOH can contribute to increased health care utilization and costs. We aimed to determine if geographically derived neighborhood level SDOH had an impact on hospitalization rates of patients receiving care at the Veterans Health Administration's (VHA) primary care clinics. METHODS: In a 1-year observational cohort of veterans enrolled in VHA's primary care medical home program during 2015, we abstracted data on individual veterans (age, sex, race, Gagne comorbidity score) from the VHA Corporate Data Warehouse and linked those data to data on neighborhood socioeconomic status (NSES) and housing characteristics from the US Census Bureau on census tract level. We used generalized estimating equation modeling and spatial-based analysis to assess the potential impact of patient-level demographic and clinical factors, NSES, and local housing stock (ie, housing instability, home vacancy rate, percentage of houses with no plumbing, and percentage of houses with no heating) on hospitalization. We defined hospitalization as an overnight stay in a VHA hospital only and reported the risk of hospitalization for veterans enrolled in the VHA's primary care medical home clinics, both across the nation and within 1 specific case study region of the country: King County, WA. RESULTS: Nationally, 6.63% of our veteran population was hospitalized within the VHA system. After accounting for patient-level characteristics, veterans residing in census tracts with a higher NSES index had decreased odds of hospitalization. After controlling all other factors, veterans residing in census tracts with higher percentage of houses without heating had 9% (Odds Ratio, 1.09%; 95% CI, 1.04 to 1.14) increase in the likelihood of hospitalization in our regional Washington State analysis, though not our national level analyses. CONCLUSIONS: Our results present the impact of neighborhood characteristics such as NSES and lack of proper heating system on the likelihood of hospitalization. The application of placed-based data at the geographic level is a powerful tool for identification of patients at high risk of health care utilization.

The Impact of Social Determinants of Health on Hospitalization in the Veterans Health Administration.

INTRODUCTION: This study aims to assess the effect of individual and geographic-level social determinants of health on risk of hospitalization in the Veterans Health Administration primary care clinics known as the Patient Aligned Care Team. METHODS: For a population of Veterans enrolled in the primary care clinics, the study team extracted patient-level characteristics and healthcare utilization records from 2015 Veterans Health Administration electronic health record data. They also collected census data on social determinants of health factors for all U.S. census tracts. They used generalized estimating equation modeling and a spatial-based GIS analysis to assess the role of key social determinants of health on hospitalization. Data analysis was completed in 2018. RESULTS: A total of 6.63% of the Veterans Health Administration population was hospitalized during 2015. Most of the hospitalized patients were male (93.40%) and white (68.80%); the mean age was 64.5 years. In the generalized estimating equation model, white Veterans had a 15% decreased odds of hospitalization compared with non-white Veterans. After controlling for patient-level characteristics, Veterans residing in census tracts with the higher neighborhood SES index experienced decreased odds of hospitalization. A spatial-based analysis presented variations in the hospitalization rate across the Veterans Health Administration primary care clinics and identified the clinic sites with an elevated risk of hospitalization (hotspots) compared with other clinics across the country. CONCLUSIONS: By linking patient and population-level data at a geographic level, social determinants of health assessments can help with designing population health interventions and identifying features leading to potentially unnecessary hospitalization in selected geographic areas that appear to be outliers.

Comparing Preventable Hospitalizations Among Veterans With and Without Mental Illnesses Before and After Implementation of PACT.

OBJECTIVES: The authors examined whether the rate of preventable hospitalizations among veterans with mental illness changed after implementation of the Department of Veterans Affairs (VA) primary care medical home-Patient Aligned Care Teams (PACT). METHODS: A 12-year retrospective cohort analysis was conducted of data from 9,206,017 veterans seen in 942 VA primary care clinics between October 2003 and March 2015. Preventable hospitalizations were those related to ambulatory care-sensitive conditions (ACSCs), identified with ICD-9 codes. Changes in rates of ACSC-related hospitalizations were compared between patients with and without mental illness in two age groups (<65, ≥65). Patients with mental illness diagnoses were grouped as follows: depression, posttraumatic stress disorder, anxiety, substance use disorder, and serious mental illness. Interrupted time-series analysis was used to model long-term trends and detect deviations after PACT implementation. RESULTS: There was an overall increase in mental illness diagnoses across both age groups. Among older veterans (≥65) with any mental illness, the rate (per 1,000 patients) of ACSC-related hospitalizations was five fewer in the post-PACT period, compared with the pre-PACT period. Among younger veterans (<65), there was a slight increase in ACSC-related hospitalizations in years 3-5 post-PACT if they had any mental illness (.6 per 1,000 patients), depression (.3), anxiety (1.4), or a substance use disorder (.6). CONCLUSIONS: In this retrospective, observational study examining large systemwide changes in clinical practice, mental illness was more likely to be diagnosed after PACT implementation, compared with before, and results suggested a benefit of PACT implementation among older veterans in terms of a reduction in ACSC-related hospitalizations.

Patient-Centered Medical Home Implementation and Burnout Among VA Primary Care Employees.

Burnout is widespread throughout primary care and is associated with negative consequences for providers and patients. The relationship between the patient-centered medical home model and burnout remains unclear. Using survey data from 8135 and 7510 VA primary care employees in 2012 and 2013, respectively, we assessed whether clinic-level medical home implementation was independently associated with burnout prevalence and estimated whether burnout changed among this workforce from 2012 to 2013. Adjusting for differences in respondent and clinic characteristics, we found that burnout was common among primary care employees, increased by 3.9% from 2012 to 2013, and was not associated with the extent of medical home implementation.

The facilitators and barriers associated with implementation of a patient-centered medical home in VHA.

BACKGROUND: The 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 (Pi(2)). METHODS: We 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 Pi(2) 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 Pi(2) score decile both as a function of respondent-reported availability of facilitators and of rating of facilitator helpfulness. RESULTS: The availability of five facilitators was associated with higher odds of a respondent's clinic's Pi(2) 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 Pi(2) scores being in the highest versus lowest decile. Six barriers were associated with significantly higher odds of a respondent's clinic's Pi(2) 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 Pi(2) score clinics were similar, with fewer, smaller significant associations, all in the expected direction. CONCLUSIONS: A 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.

Local Area Unemployment and the Demand for Inpatient Care Among Veterans Affairs Enrollees.

Prior research examining the relationship between economic conditions and health service demand has focused primarily on outpatient use. This study examines whether local area unemployment, as an indicator of economic conditions, was associated with use of inpatient care, which is theoretically less subject to discretionary use. Using a random sample of 131,603 patients dually enrolled in the Veterans Affairs (VA) Health System and fee-for-service Medicare, we measured VA, Medicare, and total (VA and Medicare) hospitalizations. Overall, local unemployment was not associated with VA, Medicare, or total hospitalization probability. Among low-income veterans exempt from VA copayments, higher local unemployment was moderately associated with a lower probability of hospitalization through Medicare. For veterans subject to VA copayments, higher local unemployment was moderately associated with a higher likelihood of VA hospitalization. These results suggest inpatient use is less sensitive to the economy, although worse economic conditions slightly affected inpatient demand for select veterans.

Patient-centered medical home implementation and primary care provider turnover.

BACKGROUND: The Veterans Health Administration (VHA) began implementing a patient-centered medical home (PCMH) model of care delivery in April 2010 through its Patient Aligned Care Team (PACT) initiative. PACT represents a substantial system reengineering of VHA primary care and its potential effect on primary care provider (PCP) turnover is an important but unexplored relationship. This study examined the association between a system-wide PCMH implementation and PCP turnover. METHODS: This was a retrospective, longitudinal study of VHA-employed PCPs spanning 29 calendar quarters before PACT and eight quarters of PACT implementation. PCP employment periods were identified from administrative data and turnover was defined by an indicator on the last quarter of each uncensored period. An interrupted time series model was used to estimate the association between PACT and turnover, adjusting for secular trend and seasonality, provider and job characteristics, and local unemployment. We calculated average marginal effects (AME), which reflected the change in turnover probability associated with PACT implementation. RESULTS: The quarterly rate of PCP turnover was 3.06% before PACT and 3.38% after initiation of PACT. In adjusted analysis, PACT was associated with a modest increase in turnover (AME=4.0 additional PCPs per 1000 PCPs per quarter, P=0.004). Models with interaction terms suggested that the PACT-related change in turnover was increasing in provider age and experience. CONCLUSIONS: PACT was associated with a modest increase in PCP turnover, concentrated among older and more experienced providers, during initial implementation. Our findings suggest that policymakers should evaluate potential workforce effects when implementing PCMH.