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Early Changes in VA Medical Home Components and Utilization
Jean Yoon, PhD, MHS; Chuan-Fen Liu, PhD, MPH; Jeanie Lo, MPH; Gordon Schectman, MD; Richard Stark, MD; Lisa V. Rubenstein, MD, MSPH; and Elizabeth M. Yano, PhD, MSPH
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Early Changes in VA Medical Home Components and Utilization

Jean Yoon, PhD, MHS; Chuan-Fen Liu, PhD, MPH; Jeanie Lo, MPH; Gordon Schectman, MD; Richard Stark, MD; Lisa V. Rubenstein, MD, MSPH; and Elizabeth M. Yano, PhD, MSPH
Significant changes were found in patients' utilization of healthcare related to early implementation of medical home components in VA primary care clinics.
ABSTRACT
Objectives:
In 2010, the Veterans Health Administration (VA) began national implementation of its patient-centered medical home (PCMH) model, called Patient Aligned Care Teams (PACTs), to improve access, coordination, and patient-centered care. We evaluated changes in reported implementation of PCMH components in all VA primary care clinics, and patients’ utilization of acute and non-acute care and total costs after 2 years.

Study Design: Longitudinal study of 2,607,902 patients from 796 VA primary care clinics.

Methods: Clinics were surveyed for their implementation of PCMH components. Patient outcomes were measured by outpatient visits for primary care, specialty care, telephone care, and emergency department (ED) care; hospitalizations for an ambulatory care–sensitive condition (ACSC); and costs of VA care in fiscal years (FYs) 2009 and 2011. Multi-level, multivariable models predicted changes in utilization and costs, adjusting for patients’ health status, clinic PCMH component scores, and a patient fixed effect.

Results: Clinics reported large improvements in adoption of all PCMH components from FY 2009 to FY 2011. Higher organization of practice scores was associated with fewer primary care visits (P = .012). Greater care coordination/transitions was modestly associated with more specialty care visits (P = .010) and fewer ED visits (P = .018), but quality/performance improvement was associated with more ED visits (P = .032). None of the PCMH components were significantly related to telephone visits, ACSC hospitalizations, or total healthcare costs.

Conclusions: Improvements under organization of practice and care coordination/transitions appear to have impacted outpatient care, but reductions in acute care were largely absent.

Am J Manag Care. 2015;21(3):197-204
Take-up of medical home components appeared to be high after Patient Aligned Care Teams (PACTs) began in the Veterans Health Administration. However, while improvements under organization of practice and care coordination/transitions seemed to have impacted outpatient care, reductions in acute care were largely absent.
  • Identifying areas that would benefit from ongoing improvement is critical for the long-term success of PACTs.
  • Better measures of PACTs implementation are also needed to evaluate the longer-term consequences of PACTs.
The spread of the patient-centered medical home (PCMH) model to numerous healthcare systems can be attributed to the promise of revitalized primary care delivery through team-based care aiming to improve continuity, coordination, quality, and access, as well as to reduce costs. Based on these principles, the Veterans Health Administration (VA) began a vast initiative in 2010 to implement the PCMH model, called Patient Aligned Care Teams (PACTs), across the entire VA healthcare system, which included more than 800 primary care clinics caring for more than 5 million patients.

Several healthcare systems have implemented pilot programs of the PCMH and found significant improvements in quality of care, reductions in acute care, and cost savings.1,2 The PACT model is expected to experience similar results since it focuses on improving access to primary care, increasing telephone and other non–face-to-face care, as well as reducing unnecessary emergency department (ED) visits and hospitalizations. Lower costs are expected, in part, from a reduction in (often very costly) acute care episodes.

An early PACT evaluation found that after implementation, there were significant changes: greater use of telephone care, fewer face-to-face visits, improved appointment access and continuity, and better follow-up after hospital discharge.3 However, PACT models encompassed a multiplicity of PCMH features that ranged from routinely scheduling same-day appointments to tracking specialty consultations, and from coordinating post discharge care to the use of electronic decision support systems. Baseline adoption of these features was highly variable across clinics4—and clinics may have implemented new PACT features to varying degrees—so it is unknown how differences in early implementation influenced utilization and costs.

This study evaluated the changes in adoption of different PCMH components by VA clinics, and the relationship to patients’ utilization of acute and non-acute care and total costs after 2 years. We focused on a cohort of primary care patients who used VA care from 2009 to 2011, and were therefore most likely to experience the effects of PACT. This examination can guide ongoing PACT implementation, suggesting what PCMH components and aspects of care might be emphasized most productively in later stages.

METHODS

Data Sources and Cohort

We conducted a longitudinal study of a panel of primary care patients using data from before PACT implementation (fiscal year [FY] 2009) and shortly after PACT implementation began (FY 2011); VA FYs begin the October 1 prior and end September 30, of the indicated year. We obtained VA patient-level utilization and cost records and linked them to clinic-level data for FYs 2009 and 2011. Patient data included sociodemographic characteristics, VA inpatient and outpatient utilization, diagnoses, lab test results, and costs of all care; clinic data included clinic characteristics and reporting of PCMH features. The study cohort was limited to patients who had at least 2 primary care visits in FY 2009 and used any VA outpatient care in FY 2011; altogether, 544,984 patients were excluded. Patients were linked to the clinic where they had a plurality of visits. The study cohort included 2,607,902 patients from 796 clinics; our cohort was older, predominantly male, more likely to be service-connected or below the means test, and sicker based on risk scores and chronic conditions compared with excluded patients. This study received approval from the Stanford University Institutional Review Board (protocol #20124).

Measures

Non-acute care outcomes were measured for each patient by number of primary care visits, specialty care visits, and telephone visits. Acute care outcomes were measured by ED visits and potentially avoidable hospitalizations. We also measured total annual costs of care in each study year. Outpatient visit data were obtained from the VA Medical Statistical Analysis System utilization files and categorized based on the visit location for outpatient services. Potentially avoidable hospitalizations were obtained from inpatient records and identified by primary diagnosis for an ambulatory care–sensitive condition (ACSC).5 The total annual costs were obtained from Decision Support System records for services provided by the VA, and from Fee Basis records for services provided by non-VA providers and paid by the VA. Because of the short study period, we did not adjust costs for inflation.

Our main independent variable, reporting of PCMH features, came from the American College of Physicians Medical Home Builder (MHB) survey administered to all VA primary care clinics in 2009 and 2011. The MHB is a self-administered practice advisor used to support practices toward recognition of National Committee for Quality Assurance (NCQA) 2008 PCMH standards.6 The MHB covers 7 PCMH components: patient-centered care and communication (support for patients’ self-management and decision making and staff communication training); access and scheduling (scheduling flexibility such as same-day appointments and non–face-to-face services); care coordination and transitions in care (coordinating visits to other physicians, creating individualized treatment plans, assessing treatment barriers); organization of practice (tracking procedures, test results, medication lists, team huddles); population management (patient registries, clinical guidelines, identifying unmet needs); quality improvement and performance improvement (performance measures, satisfaction surveys); and use of technology (practice management systems, electronic health records, decision support systems). These components overlap with 6 of NCQA’s standards for receiving PCMH certification, so the MHB captures elements similar to NCQA’s measures.

The MHB was self-administered by the clinic director or other clinic leader, and the survey response rate was 100% in 2009, and 98% in 2011. The sum of PCMH features within each component was obtained for the component scores. The component scores were grouped into low and high categories based on the baseline distribution below or above the median.

In multivariable models, we included measures of time-varying patient factors in both years. We measured the Charlson Comorbidity Index (CCI) score, a measure of comorbidity burden, for each patient based on diagnoses from inpatient and outpatient records.7 We also created an indicator of risk based on available lab results reported during the study years. We chose lab tests that could, if increasing or decreasing during the study period, indicate higher risk of morbidity and mortality: cholesterol tests (>100 mg/dL), hemoglobin A1C test (>8%), anemia hemoglobin (<13 g/dL), and kidney function (eGRF <45).

For descriptive purposes, we measured several patient and clinic characteristics in the baseline year that were time invariant. Clinic measures included clinic rurality, assessed by metropolitan/nonmetropolitan codes from the Area Resource File.8 Clinic type was categorized as based at a VA medical center (VAMC); VA-owned community-based outpatient clinic (CBOC)—owned by the medical center and staffed by VA providers; leased CBOC—staffed by VA providers with VA governance; or contracted CBOC—staffed by contract providers without VA governance.9 Patient-level measures, including age, sex, race/ethnicity, marital status, service connection, means test, and insurance, were obtained from utilization records in FY 2009. Area income and education came from US Census data on income and maximum years of educational attainment linked to patients’ zip codes. We also estimated patients’ distance to their primary care clinic.

Statistical Analysis

In bivariate analyses, we compared the mean number of primary care, specialty care, telephone care, and ED visits, and ACSC hospitalizations, and total costs of care per patient in FY 2009 and FY 2011 using 1-way ANOVA with year as the independent variable to determine significant changes over time. In multivariate analyses, we estimated the longitudinal association between changes in PCMH components scores with changes in utilization and cost measures using multi-level, linear regression models for each outcome. Regression models included a fixed effect for each patient and adjusted for time-varying patient factors (risk indicator and CCI score), dummy variables for high versus low clinic PCMH component scores, and an indicator for year. The regression model for costs used the log of costs as the dependent variable since costs had a skewed distribution.

In our models, the fixed effect controls for all factors fixed over time, such as patient sociodemographic factors and clinic characteristics (eg, rurality and size). The model estimates the “within” estimator; the coefficients represent the effect of a unit change in each time-varying predictor on patient outcomes.10 The coefficient for the year indicator represented the time trend in outcomes that was not explained by changes in PCMH components or health status. Additionally, regression models adjusted standard errors to account for cluster (the intra-class correlations between patients within a primary care site).11 We also conducted regressions using Poisson models for utilization measures since they were counts, but standard errors could not be adjusted for clustering within clinics since we used a fixed effect. The effect sizes were similar with linear models, and these results are presented in the eAppendix (available at www.ajmc.com). All regressions and data analyses were conducted using Stata 11.0 (StataCorp LP, College Station, Texas).12

RESULTS

Description of Study Cohort and Clinics

At baseline, most of the primary care patients in the study cohort were middle-aged (45%) or elderly (46%) (Table 1). The study cohort was predominantly male (95%), a majority were white (67%), and a majority of patients were also married (58%). The largest proportion of veterans (42%) was eligible for VA care because of a service-connected disability, or they were within 5 years of military service. Relatively few patients (19%) had a disability rating greater than 50%, and many patients (40%) reported no other health insurance. Patients traveled a mean distance of 18 miles to their primary care clinic, the mean income of their area of residence was $40,541, and less than half of patients (46%) lived in areas where more than one-fourth of the population had a college degree.

The percent of patients with high risk (as measured by lab values) for cholesterol, diabetes, anemia, or kidney function decreased slightly from 61% of the cohort in FY 2009, to 56% in FY 2011. The CCI score, which measured total comorbidity burden, increased from 0.86 (SD = 0.84) to 0.90 (SD = 0.85) during that time.

 
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