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Objective To assess differences in risk (measured by expected costs associated with sociodemographic and clinical profiles) between Veterans receiving outpatient services through two community care (CC) programs: the Fee program (“Fee”) and the Veterans Choice Program (“Choice”). Data Sources/Study Setting Administrative data from VHA's Corporate Data Warehouse in fiscal years (FY) 2014–2015. Study Design We compared the clinical characteristics of Veterans across three groups (Fee only, Choice only, and Fee & Choice). We classified Veterans into risk groups based on Nosos risk scores and examined the relationship between type of outpatient utilization and risk within each CC group. We also examined changes in utilization of VHA and CC in FY14–FY15. We used chi‐square tests, t tests, and ANOVAs to identify significant differences between CC groups. Principal Findings Of the 1,400,977 Veterans using CC in FY15, 91.4 percent were Fee‐only users, 4.4 percent Choice‐only users, and 4.2 percent Fee & Choice users. Mean concurrent risk scores were higher for Fee only and Fee & Choice (1.9, SD = 2.7; 1.8, SD = 2.2) compared to Choice‐only users (1.0, SD = 1.2) (p < .0001). Most CC users were “dual users” of both VHA and CC in FY14–FY15. Conclusions As care transitions from VHA to CC, VHA should consider how best to coordinate care with community providers to reduce duplication of efforts, improve handoffs, and achieve the best outcomes for Veterans.

Development Service hosted a VA State of the Art conference (SOTA) focused on care coordination. The SOTA convened VA researchers, program directors, clinicians, and policy makers to identify knowledge gaps regarding care coordination within the VA and between VA and community systems of care. This article provides a summary and synthesis of relevant literature and provides recommendations generated from the SOTA about how to evaluate cross-system care coordination. Care coordination is typically evaluated using health outcomes including hospital readmissions and death; however, in cross-system evaluations of care coordination, measures such as access, cost, Veteran/patient and provider satisfaction (including with cross-system communication), comparable quality metrics, context (urban vs. rural), and patient complexity (medical and mental health conditions) need to be included to fully evaluate care coordination effectiveness. Future research should examine the role of multiple individuals coordinating VA and non-VA care, and how these coordinators work together to optimize coordination. KEY WORDS: Veterans; care coordination; health information interoperability; delivery of healthcare.

Objectives: To compare 90-day postoperative complication rates between Veterans receiving cataract surgery in VA vs Community Care (CC) during the first year of implementation of the Veterans Choice Act. Data Sources: Fiscal Year (FY) 2015 VA and CC outpatient data from VA's Corporate Data Warehouse (CDW) 10/01/14-9/30/15). FY14 data were used to obtain baseline clinical information prior to surgery. Study Design: Retrospective one-year study using secondary data to compare 90day complication rates following cataract surgery (measured using National Quality Forum (NQF) criteria) in VA vs CC. NQF defines major complications from a specified list of Current Procedural Terminology (CPT) codes. We ran a series of logistic regression models to predict 90-day complication rates, adjusting for Veterans' sociodemographic characteristics, comorbidities, preoperative ocular conditions, eye risk group, and type of cataract surgery (classified as routine vs complex). Data Collection: We linked VA and CC users through patient identifiers obtained from the CDW files. Our sample included all enrolled Veterans who received outpatient cataract surgery either in the VA or through CC during FY15. Cataract surgeries were identified through CPT codes 66 984 (routine) and 66 982 (complex).

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Veterans Affairs utilization following new Medicaid enrollment remained relatively unchanged, and the VA continued to provide the large majority of care for dually enrolled veterans. There was variation among patients as Medicaid eligibility and other program factors influenced their use of Medicaid services.

Background: Evidence-based treatment is provided infrequently and inconsistently to patients with opioid use disorder (OUD). Treatment guidelines call for high-quality, patient-centered care that meets individual preferences and needs, but it is unclear whether current quality measures address individualized aspects of care and whether measures of patient-centered OUD care are supported by evidence. Methods: We conducted an environmental scan of OUD care quality to (1) evaluate patient-centeredness in current OUD quality measures endorsed by national agencies and in national OUD treatment guidelines; and (2) review literature evidence for patient-centered care in OUD diagnosis and management, including gaps in current guidelines, performance data, and quality measures. We then synthesized these findings to develop a new quality measurement taxonomy that incorporates patient-centered aspects of care and identifies priority areas for future research and quality measure development. Results: Across 31 endorsed OUD quality measures, only two measures of patient experience incorporated patient preferences and needs, while national guidelines emphasized providing patient-centered care. Among 689 articles reviewed, evidence varied for practices of patient-centered care. Many practices were supported by guidelines and substantial evidence, while others lacked evidence despite guideline support. Our synthesis of findings resulted in EQuIITable Care, a taxonomy comprised of six classifications: (1) patient Experience and engagement, (2) Quality of life; (3) Identification of patient risks; (4) Interventions to mitigate patient risks; (5) Treatment; and (6) Care coordination and navigation. Conclusions: Current quality measurement for OUD lacks patient-centeredness. EQuIITable Care for OUD provides a roadmap to develop measures of patient-centered care for OUD.

There was low uptake of VCP services in the first year of the program. Data from additional years are needed to better understand the impact of this policy.