Lower comorbidity scores and severity levels in Veterans Health Administration hospitals: a cross-sectional study.

BACKGROUND: Previous studies found that documentation of comorbidities differed when Veterans received care within versus outside Veterans Health Administration (VHA). Changes to medical center funding, increased attention to performance reporting, and expansion of Clinical Documentation Improvement programs, however, may have caused coding in VHA to change. METHODS: Using repeated cross-sectional data, we compared Elixhauser-van Walraven scores and Medicare Severity Diagnosis Related Group (DRG) severity levels for Veterans' admissions across settings and payers over time, utilizing a linkage of VHA and all-payer discharge data for 2012-2017 in seven US states. To minimize selection bias, we analyzed records for Veterans admitted to both VHA and non-VHA hospitals in the same year. Using generalized linear models, we adjusted for patient and hospital characteristics. RESULTS: Following adjustment, VHA admissions consistently had the lowest predicted mean comorbidity scores (4.44 (95% CI 4.34-4.55)) and lowest probability of using the most severe DRG (22.1% (95% CI 21.4%-22.8%)). In contrast, Medicare-covered admissions had the highest predicted mean comorbidity score (5.71 (95% CI 5.56-5.85)) and highest probability of using the top DRG (35.3% (95% CI 34.2%-36.4%)). CONCLUSIONS: More effective strategies may be needed to improve VHA documentation, and current risk-adjusted comparisons should account for differences in coding intensity.

Do Community-level Disadvantages Account for Racial Disparities in the Safety of Spine Surgery? A Large Database Study Based on Medicare Claims.

BACKGROUND: Racial health disparities across orthopaedic surgery subspecialties, including spine surgery, are well established. However, the underlying causes of these disparities, particularly relating to social determinants of health, are not fully understood. QUESTIONS/PURPOSES: (1) Is there a racial difference in 90-day mortality, readmission, and complication rates ("safety outcomes") among Medicare beneficiaries after spine surgery? (2) To what degree does the Centers for Disease Control and Prevention Social Vulnerability Index (SVI), a community-level marker of social determinants of health, account for racial disparities in safety outcomes? METHODS: To examine racial differences in 90-day mortality, readmission, and complications after spine surgery, we retrospectively identified all 419,533 Medicare beneficiaries aged 65 or older who underwent inpatient spine surgery from 2015 to 2019; we excluded 181,588 patients with endstage renal disease or Social Security disability insurance entitlements, who were on Medicare HMO, or who had missing SVI data. Because of the nearly universal coverage of those age 65 or older, Medicare data offer a large cohort that is broadly generalizable, provides improved precision for relatively rare safety outcomes, and is free of confounding from differential insurance access across races. The Master Beneficiary Summary File includes enrollees' self-reported race based on a restrictive list of mutually exclusive options. Even though this does not fully capture the entirety of racial diversity, it is self-reported by patients. Identification of spine surgery was based on five Diagnosis Related Groups labeled "cervical fusion," "fusion, except cervical," "anterior-posterior combined fusion," "complex fusion," and "back or neck, except fusion." Although heterogeneous, these cohorts do not reflect inherently different biology that would lead us to expect differences in safety outcomes by race. We report specific types of complications that did and did not involve readmission. Although complications vary in severity, we report them as composite measures while being cognizant of the inherent limitations of making inferences based on aggregate measures. The SVI was chosen as the mediating variable because it aggregates important social determinants of health and has been shown to be a marker of high risk of poor public health response to external stressors. Patients were categorized into three groups based on a ranking of the four SVI themes: socioeconomic status, household composition, minority status and language, and housing and transportation. We report the "average race effects" among Black patients compared with White patients using nearest-neighbor Mahalanobis matching by age, gender, comorbidities, and spine surgery type. Mahalanobis matching provided the best balance among propensity-type matching methods. Before matching, Black patients in Medicare undergoing spine surgery were disproportionately younger with more comorbidities and were less likely to undergo cervical fusion. To estimate the contribution of the SVI on racial disparities in safety outcomes, we report the average race effect between models with and without the addition of the four SVI themes. RESULTS: After matching on age, gender, comorbidities, and spine surgery type, Black patients were on average more likely than White patients to be readmitted (difference of 1.5% [95% CI 0.9% to 2.1%]; p < 0.001) and have complications with (difference of 1.2% [95% CI 0.5% to 1.9%]; p = 0.002) or without readmission (difference of 3.6% [95% CI 2.9% to 4.3%]; p < 0.001). Adding the SVI to the model attenuated these differences, explaining 17% to 49% of the racial differences in safety, depending on the outcome. An observed higher rate of 90-day mortality among Black patients was explained entirely by matching using non-SVI patient demographics (difference of 0.00% [95% CI -0.3% to 0.3%]; p = 0.99). However, even after adjusting for the SVI, Black patients had more readmissions and complications. CONCLUSION: Social disadvantage explains up to nearly 50% of the disparities in safety outcomes between Black and White Medicare patients after spine surgery. This argument highlights an important contribution of socioeconomic circumstances and societal barriers to achieving equal outcomes. But even after accounting for the SVI, there remained persistently unequal safety outcomes among Black patients compared with White patients, suggesting that other unmeasured factors contribute to the disparities. This is consistent with evidence documenting Black patients' disadvantages within a system of seemingly equal access and resources. Research on racial health disparities in orthopaedics should account for the SVI to avoid suggesting that race causes any observed differences in complications among patients when other factors related to social deprivation are more likely to be determinative. Focused social policies aiming to rectify structural disadvantages faced by disadvantaged communities may lead to a meaningful reduction in racial health disparities. LEVEL OF EVIDENCE: Level III, therapeutic study.

Association of Quality of Care With Where Veterans Choose to Get Knee Replacement Surgery.

Importance: Recent legislation expanded veterans' access to Veterans Health Administration (VA)-purchased care. Quality should be considered when choosing where to get total knee arthroplasty (TKA), but currently available quality metrics provide little guidance. Objective: To determine whether an association exists between the proportion of TKAs performed (vs purchased) at each VA facility and the quality of care provided (as measured by short-term complication rates). Design, Setting, and Participants: This 3-year cohort study used VA and community care data (fiscal year 2017 to fiscal year 2019) from the VA's Corporate Data Warehouse. Complications were defined following the Centers for Medicare and Medicaid Services' methodology. The setting included 140 VA health care facilities performing or purchasing TKAs. Participants included veterans who had 43 371 primary TKA procedures that were either VA-performed or VA-purchased during the study period. Exposures: Of the 43 371 primary TKA procedures, 18 964 (43.7%) were VA-purchased. Main Outcomes and Measures: The primary outcome was risk-standardized short-term complication rates of VA-performed or VA-purchased TKAs. The association between the proportion of TKAs performed at each VA facility and quality of VA-performed and VA-purchased care was examined using a regression model. Subgroups were also identified for facilities that had complication rates above or below the overall mean complication rate and for facilities that performed more or less than half of the facility's TKAs. Results: Among the study sample's 41 775 veterans who underwent 43 371 TKAs, 38 725 (89.3%) were male, 6406 (14.8%) were Black, 33 211 (76.6%) were White, and 1367 (3.2%) had other race or ethnicity (including American Indian or Alaska Native, Asian, and Native Hawaiian or other Pacific Islander); mean (SD) age was 66.9 (8.5) years. VA-performed and VA-purchased TKAs had a mean (SD) raw overall short-term complication rate of 2.97% (0.08%). There was no association between the proportion of TKAs performed in VA facilities and risk-standardized complication rates for VA-performed TKAs, and no association for VA-purchased TKAs. Conclusions and Relevance: In this cohort study, surgical quality did not have an association with where veterans had TKA, possibly because meaningful comparative data are lacking. Reporting local and community risk-standardized complication rates may inform veterans' decisions and improve care. Combining these data with the proportion of TKAs performed at each site could facilitate administrative decisions on where resources should be allocated to improve care.

Association Between Mental Health Conditions and Outpatient Care Fragmentation: a National Study of Older High-Risk Veterans.

BACKGROUND: Healthcare fragmentation may lead to adverse consequences and may be amplified among older, sicker patients with mental health (MH) conditions. OBJECTIVE: To determine whether older Veterans with MH conditions have more fragmented outpatient non-MH care, compared with older Veterans with no MH conditions. DESIGN: Retrospective cohort study using FY2014 Veterans Health Administration (VHA) administrative data linked to Medicare data. PARTICIPANTS: 125,481 VHA patients ≥ 65 years old who were continuously enrolled in Medicare Fee-for-Service Parts A and B and were at high risk for hospitalization. MAIN OUTCOME AND MEASURES: The main outcome was non-MH care fragmentation as measured by (1) non-MH provider count and (2) Usual Provider of Care (UPC), the proportion of care with the most frequently seen non-MH provider. We tested the association between no vs. any MH conditions and outcomes using Poisson regression and fractional regression with logit link, respectively. We also compared Veterans with no MH condition with each MH condition and combinations of MH conditions, adjusting for sociodemographics, comorbidities, and drive-time to VHA specialty care. KEY RESULTS: In total, 47.3% had at least one MH condition. Compared to those without MH conditions, Veterans with MH conditions had less fragmented care, with fewer non-MH providers (IRR = 0.96; 95% CI: 0.96-0.96) and more concentrated care with their usual provider (OR = 1.08 for a higher UPC; 95% CI: 1.07, 1.09) in adjusted models. Secondary analyses showed that those with individual MH conditions (e.g., depression) had fewer non-MH providers (IRR range: 0.86-0.98) and more concentrated care (OR range: 1.04-1.20). A similar pattern was observed when examining combinations of MH conditions (IRR range: 0.80-0.90; OR range: 1.16-1.30). CONCLUSIONS: Contrary to expectations, having a MH condition was associated with less fragmented non-MH care among older, high-risk Veterans. Further research will determine if this is due to different needs, underuse, or appropriate use of healthcare.

Anticipating VA/non-VA care coordination demand for Veterans at high risk for hospitalization.

ABSTRACT: U.S. Veterans Affairs (VA) patients' multi-system use can create challenges for VA clinicians who are responsible for coordinating Veterans' use of non-VA care, including VA-purchased care ("Community Care") and Medicare.To examine the relationship between drive distance and time-key eligibility criteria for Community Care-and VA reliance (proportion of care received in VA versus Medicare and Community Care) among Veterans at high risk for hospitalization. We used prepolicy data to anticipate the impact of the 2014 Choice Act and 2018 Maintaining Internal Systems and Strengthening Integrated Outside Networks Act (MISSION Act), which expanded access to Community Care.Cross-sectional analysis using fractional logistic regressions to examine the relationship between a Veteran's reliance on VA for outpatient primary, mental health, and other specialty care and their drive distance/time to a VA facility.Thirteen thousand seven hundred three Veterans over the age of 65 years enrolled in VA and fee-for-service Medicare in federal fiscal year 2014 who were in the top 10th percentile for hospitalization risk.Key explanatory variables were patients' drive distance to VA > 40 miles (Choice Act criteria) and drive time to VA ≥ 30 minutes for primary and mental health care and ≥60 minutes for specialty care (MISSION Act criteria).Veterans at high risk for hospitalization with drive distance eligibility had increased odds of an outpatient specialty care visit taking place in VA when compared to Veterans who did not meet Choice Act eligibility criteria (odds ratio = 1.10, 95% confidence interval 1.05-1.15). However, drive time eligibility (MISSION Act criteria) was associated with significantly lower odds of an outpatient specialty care visit taking place in VA (odds ratio = 0.69, 95% confidence interval 0.67, 0.71). Neither drive distance nor drive time were associated with reliance for outpatient primary care or mental health care.VA patients who are at high risk for hospitalization may continue to rely on VA for outpatient primary care and mental health care despite access to outside services, but may increase use of outpatient specialty care in the community in the MISSION era, increasing demand for multi-system care coordination.

Outpatient care fragmentation in Veterans Affairs patients at high-risk for hospitalization.

OBJECTIVE: To examine outpatient care fragmentation and its association with future hospitalization among patients at high risk for hospitalization. DATA SOURCES: Veterans Affairs (VA) and Medicare data. STUDY DESIGN: We conducted a longitudinal study, using logistic regression to examine how outpatient care fragmentation in FY14 (as measured by number of unique providers, Breslau's Usual Provider of Care (UPC), Bice-Boxerman's Continuity of Care Index (COCI), and Modified Modified Continuity Index (MMCI)) was associated with all-cause hospitalizations and hospitalizations related to ambulatory care sensitive conditions (ACSC) in FY15. We also examined how fragmentation varied by patient's age, gender, race, ethnicity, marital status, rural status, history of homelessness, number of chronic conditions, Medicare utilization, and mental health care utilization. DATA EXTRACTION METHODS: We extracted data for 130,704 VA patients ≥65 years old with a hospitalization risk ≥90th percentile and ≥ four outpatient visits in the baseline year. PRINCIPAL FINDINGS: The mean (SD) of FY14 outpatient visits was 13.2 (8.6). Fragmented care (more providers, less care with a usual provider, more dispersed care based on COCI) was more common among patients with more chronic conditions and those receiving mental health care. In adjusted models, most fragmentation measures were not associated with all-cause hospitalization, and patients with low levels of fragmentation (more concentrated care based on UPC, COCI, and MMCI) had a higher likelihood of an ACSC-related hospitalization (AOR, 95% CI = 1.21 (1.09-1.35), 1.27 (1.14-1.42), and 1.28 (1.18-1.40), respectively). CONCLUSIONS: Contrary to expectations, outpatient care fragmentation was not associated with elevated all-cause hospitalization rates among VA patients in the top 10th percentile for risk of admission; in fact, fragmented care was linked to lower rates of hospitalization for ACSCs. In integrated settings such as the VA, multiple providers, and dispersed care might offer access to timely or specialized care that offsets risks of fragmentation, particularly for conditions that are sensitive to ambulatory care.

Is Discretionary Care Associated with Safety Among Medicare Beneficiaries Undergoing Spine Surgery?

BACKGROUND: Spine surgery and its corresponding costs have increased in recent years and are variable across geographic regions. Discretionary care is the component of spending variation that is independent of illness severity, age, and regional pricing. It is unknown whether greater discretionary care is associated with improved safety for patients undergoing spine surgery, as we would expect from value-based health care. METHODS: We conducted an analysis of 5 spine surgery cohorts based on Medicare claims from 2013 to 2017. Patients were grouped into quintiles based on the Dartmouth Atlas End-of-Life Inpatient Care Index (EOL), reflecting regional spending variation attributed to discretionary care. Multivariable regression examined the association between discretionary care and safety measures while controlling for age, sex, race, comorbidity, and hospital features. RESULTS: We observed a threefold to fourfold variation in 90-day episode-of-care cost across regions, depending on the cohort. Spine-specific spending was correlated with EOL quintile, confirming that spending variation is due more to discretionary care than it is to pricing, age, or illness severity. Greater spending across EOL quintiles was not associated with improved safety, and, in fact, was associated with poorer safety in some cohorts. For example, all-cause readmission was greater in the high-spending EOL quintile relative to the low-spending EOL quintile among the "fusion, except cervical" cohort (14.2% vs. 13.1%; OR = 1.10; 95% CI = 1.05 to 1.20), the "complex fusion" cohort (28.0% vs. 25.4%; OR = 1.15; 95% CI = 1.01 to 1.30), and the "cervical fusion" cohort (15.0% vs. 13.6%; OR = 1.12; 95% CI = 1.05 to 1.20). CONCLUSIONS: Wide variation in spending was not explained by differences in illness severity, age, or pricing, and increased discretionary care did not enhance safety. These findings point to inefficient use of health-care resources, a potential focus of reform. LEVEL OF EVIDENCE: Economic and Decision Analysis Level IV. See Instructions for Authors for a complete description of levels of evidence.

Estimating the Cost of Surgical Care Purchased in the Community by the Veterans Health Administration.

Background. Veterans' access to Veterans Affairs (VA)-purchased community care expanded due to large increases in funding provided in the 2014 Veterans Choice Act. Objectives. To compare costs between VA-delivered care and VA payments for purchased care for two commonly performed surgeries: total knee arthroplasties (TKAs) and cataract surgeries. Research Design. Descriptive statistics and regressions examining costs in VA-delivered and VA-purchased care (fiscal year [FY] 2018 [October 2017 to September 2018]). Subjects. A total of 13,718 TKAs, of which 6,293 (46%) were performed in VA. A total of 91,659 cataract surgeries, of which 65,799 (72%) were performed in VA. Measures. Costs of VA-delivered care based on activity-based cost estimates; costs of VA-purchased care based on approved and paid claims. Results. Ninety-eight percent of VA-delivered TKAs occurred in inpatient hospitals, with an average cost of $28,969 (SD $10,778). The majority (86%) of VA-purchased TKAs were also performed at inpatient hospitals, with an average payment of $13,339 (SD $23,698). VA-delivered cataract surgeries were performed at hospitals as outpatient procedures, with an average cost of $4,301 (SD $2,835). VA-purchased cataract surgeries performed at hospitals averaged $1,585 (SD $629); those performed at ambulatory surgical centers cost an average of $1,346 (SD $463). We also found significantly higher Nosos risk scores for patients who used VA-delivered versus VA-purchased care. Conclusions. Costs of VA-delivered care were higher than payments for VA-purchased care, but this partly reflects legislative caps limiting VA payments to community providers to Medicare amounts. Higher patient risk scores in the VA could indicate that community providers are reluctant to accept high-risk patients because of Medicare reimbursements, or that VA providers prefer to keep the more complex patients in VA.

County-level Predictors of Growth in Community-based Primary Care Use Among Veterans.

BACKGROUND: The 2014 Choice Act expanded the Veterans Health Administration's (VA) capacity to purchase services for VA enrollees from community providers, yet little is known regarding the growth of Veterans' primary care use in community settings. OBJECTIVES: The aim was to measure county-level growth in VA community-based primary care (CBPC) penetration following the Choice Act and to assess whether CBPC penetration increased in rural counties with limited access to VA facilities. DATA AND SAMPLE: A total of 3132 counties from VA administrative data from 2015 to 2018, Area Health Resources Files, and County Health Rankings. ANALYSIS: We defined the county-level CBPC penetration rate as the proportion of VA-purchased primary care out of all VA-purchased primary care (ie, within and outside VA). We estimated county-level multivariate linear regression models to assess whether rurality and supply of primary care providers and health care facilities were significantly associated with CBPC growth. RESULTS: Nationally, CBPC penetration rates increased from 2.7% in 2015 to 7.3% in 2018. The rurality of the county was associated with a 2-3 percentage point (pp) increase in CBPC penetration growth (P<0.001). The presence of a VA facility was associated with a 1.7 pp decrease in CBPC penetration growth (P<0.001), while lower primary care provider supply was associated with a 0.6 pp increase in CBPC growth (P<0.001). CONCLUSION: CBPC as a proportion of all VA-purchased primary care was small but increased nearly 3-fold between 2015 and 2018. Greater increases in CBPC penetration were concentrated in rural counties and counties without a VA facility, suggesting that community care may enhance primary care access in rural areas with less VA presence.

Comparing Driving Miles for Department of Veterans Affairs-delivered Versus Department of Veterans Affairs-purchased Cataract Surgery.

BACKGROUND: The Veterans Choice Act of 2014 increased the number of Veterans eligible for Department of Veterans Affairs (VA)-purchased care delivered in non-VA community care (CC) facilities. Driving >40 miles from home to a VA facility is a key eligibility criterion for CC. It remains unclear whether this policy change improved geographical access by reducing drive distance for Veterans. OBJECTIVES: Describe the driving distance for Veterans receiving cataract surgery in VA and CC facilities, and if they visited the closest-to-home facility or if they drove to farther facilities. SUBJECTS: Veterans who had cataract surgery in federal fiscal year 2015. MEASURES: We calculated driving miles to the Closest VA and CC facilities that performed cataract surgeries, and to the location where Veterans received care. RESULTS: A total of 61,746 Veterans received 83,875 cataract surgeries. More than 50% of CC surgeries occurred farther than the Closest CC facility providing cataract surgery (median Closest CC facility 8.7 miles vs. Actual CC facility, 19.7 miles). Most (57%) Veterans receiving cataract surgery at a VA facility used the Closest VA facility (median Closest VA facility 28.1 miles vs. Actual VA facility at 31.2 miles). In all, 26.1% of CC procedures occurred in facilities farther away than the Closest VA facility. CONCLUSIONS: Although many Veterans drove farther than needed to get cataract surgery in CC, this was not true for obtaining care in the VA. Our findings suggest that there may be additional reasons, besides driving distance, that affect whether Veterans choose CC and, if they do, where they seek CC.

Veterans' Experiences With Outpatient Care: Comparing The Veterans Affairs System With Community-Based Care.

Timely access to outpatient care was a primary driver behind the Department of Veterans Affairs' (VA's) increased purchase of community-based care under the Veterans Access, Choice, and Accountability Act of 2014, known as the Choice Act. To compare veterans' experiences in VA-delivered and community-based outpatient care after implementation of the act, we assessed veterans' scores on four dimensions of experience-access, communication, coordination, and provider rating-for outpatient specialty, primary, and mental health care received during 2016-17. Patient experiences were better for VA than for community care in all respects except access. For specialty care, access scores were better in the community; for primary and mental health care, access scores were similar in the two settings. Although all specialty care scores and the primary care coordination score improved over time, the gaps between settings did not shrink. As purchased care further expands under the VA Maintaining Internal Systems and Strengthening Integrated Outside Networks Act of 2018, which replaced the Choice Act in 2019, monitoring of meaningful differences between settings should continue, with the results used to inform both VA purchasing decisions and patients' care choices.

Provider Practice Competition and Adoption of Medicare's Oncology Care Model.

BACKGROUND: There is a concern that the Oncology Care Model (OCM), a voluntary bundled payment program, may incentivize mergers and acquisitions among physician practices leading to reduced competition and price increases. These concerns are heightened if OCM is preferentially adopted in competitive health care markets because it could result in reduced competition, but little is known about the characteristics of markets where OCM is adopted. OBJECTIVE: To measure the association between regional market competition among medical oncologists with the initial adoption of OCM. RESEARCH DESIGN: The Herfindahl-Hirschman Index (HHI), a measure of competition, was calculated for hospital referral regions (HRRs) using secondary data from the Centers for Medicare and Medicaid Services. The relationship between HHI and OCM adoption was assessed using a 2-part regression model adjusting for the market-level number of practices, physician density, average practice size, sociodemographic characteristics, and medical resources. A count model on all HRRs was also estimated to assess an overall effect. SUBJECTS: A total of 10,788 physicians in 3,537 practices who billed Medicare for oncology services in 2015. RESULTS: OCM was adopted in 114 (37%) of the 306 HRRs. We found that practices in competitive health care markets were more likely to adopt OCM than in noncompetitive markets. Two-part regression analysis indicated a nonlinear relationship between HHI and OCM adoption. Average practice size, number of practices in an HRR, and the hospital bed rate were positively associated with adoption, whereas the rate of full-time equivalent hospital employees to 1000 residents was negatively associated with adoption. CONCLUSIONS: OCM adoption was higher in HRRs with greater competition. Careful monitoring of market-level changes among OCM adopters should be undertaken to ensure that the benefits of the OCM outweigh the negative consequences of possible changes in competition.

Veteran Satisfaction with Early Experiences of Health Care Through the Veterans Choice Program: a Concurrent Mixed Methods Study.

BACKGROUND: The 2014 Veterans Access, Choice and Accountability Act (i.e., "Choice") allows eligible Veterans to receive covered health care outside the Veterans Affairs (VA) Healthcare System. The initial implementation of Choice was challenging, and use was limited in the first year. OBJECTIVE: To assess satisfaction with Choice, and identify reasons for satisfaction and dissatisfaction during its early implementation. DESIGN AND PARTICIPANTS: Semi-structured telephone interviews from July to September 2015 with Choice-eligible Veterans from 25 VA facilities across the USA. MAIN MEASURES: Satisfaction was assessed with 5-point Likert scales and open-ended questions. We compared ratings of satisfaction with Choice and VA health care, and identified reasons for satisfaction/dissatisfaction with Choice in a thematic analysis of open-ended qualitative data. RESULTS: Of 195 participants, 35 had not attempted to use Choice; 43 attempted but had not received Choice care (i.e., attempted only); and 117 attempted and received Choice care. Among those who attempted only, a smaller percentage were somewhat/very satisfied with Choice than with VA health care (17.9% and 71.8%, p < 0.001); among participants who received Choice, similar percentages were somewhat/very satisfied with Choice and VA health care (66.6% and 71.1%, p = 0.45). When asked what contributed to Choice ratings, participants who attempted but did not receive Choice care reported poor access (50%), scheduling problems (20%), and care coordination issues (10%); participants who received Choice care reported improved access (27%), good quality of care (19%), and good distance to Choice provider (16%). Regardless of receipt of Choice care, most participants expressed interest in using Choice in the future (70-82%). CONCLUSIONS: Access and scheduling barriers contributed to dissatisfaction for Veterans unsuccessfully attempting to use Choice during its initial implementation, whereas improved access and good care contributed to satisfaction for those receiving Choice care. With Veterans' continued interest in using services outside VA facilities, subsequent policy changes should address Veterans' barriers to care.

Recommendations for the Evaluation of Cross-System Care Coordination from the VA State-of-the-art Working Group on VA/Non-VA Care.

In response to widespread concerns regarding Veterans' access to VA care, Congress enacted the Veterans Access, Choice and Accountability Act of 2014, which required VA to establish the Veterans Choice Program (VCP). Since the inception of VCP, more than two million Veterans have received care from community providers, representing approximately 25% of Veterans enrolled in VA care. However, expanded access to non-VA care has created challenges in care coordination between VA and community health systems. In March 2018, the VA Health Services Research & 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.

Veterans' Reliance on VA Care by Type of Service and Distance to VA for Nonelderly VA-Medicaid Dual Enrollees.

BACKGROUND: Not much is known about nonelderly veterans and their reliance on care from the Veterans Affairs (VA) health care system when they have access to non-VA care. OBJECTIVES: To estimate VA reliance for nonelderly veterans enrolled in VA and Medicaid. RESEARCH DESIGN: Retrospective, longitudinal analysis of Medicaid claims data and VA administrative data to compare patients' utilization of VA and Medicaid services 12 months before and for up to 12 months after Medicaid enrollment began. SUBJECTS: Nonelderly veterans (below 65 y) receiving VA care and newly enrolled in Medicaid, calendar years 2006-2010 (N=19,890). MEASURES: VA reliance (proportion of care received in VA) for major categories of outpatient and inpatient care. RESULTS: Patients used VA outpatient care at similar levels after enrolling in Medicaid with the exceptions of emergency department (ED) and obstetrics/gynecology care, which decreased. VA inpatient utilization was similar after Medicaid enrollment for most types of care. VA-adjusted outpatient reliance was highest for mental health care (0.99) and lowest for ED care (0.02). VA-adjusted inpatient reliance was highest for respiratory (0.80) and cancer stays (0.80) and lowest for musculoskeletal stays (0.20). Associations between VA reliance and distance to VA providers varied by type of care. CONCLUSIONS: Veterans dually enrolled in Medicaid received most of their outpatient care from the VA except ED, obstetrics/gynecology, and dental care. Patients received most of their inpatient care from Medicaid except mental health, respiratory, and cancer care. Sensitivity to travel distance to VA providers explained some of these differences.

The Impact of Medicaid Enrollment on Veterans Health Administration Enrollees' Behavioral Health Services Use.

OBJECTIVE: To examine Veterans Health Administration (VA) enrollees' use of VA services for treatment of behavioral health conditions (BHCs) after gaining Medicaid, and if VA reliance varies by complexity of BHCs. DATA SOURCES/STUDY SETTING: VA and Medicaid Analytic eXtract utilization data from 31 states, 2006-2010. STUDY DESIGN: A retrospective, longitudinal study of Veterans enrolled in VA care in the year before and year after enrollment in Medicaid among 7,249 nonelderly Veterans with serious mental illness (SMI), substance use disorder (SUD), posttraumatic stress disorder (PTSD), depression, or other BHCs. DATA COLLECTION/EXTRACTION METHODS: Utilization and VA reliance (proportion of care received at VA) for BH outpatient and inpatient services in unadjusted and adjusted analyses. PRINCIPAL FINDINGS: In adjusted analyses, we found that overall Veterans did not significantly change their use of VA outpatient BH services after Medicaid enrollment. In beta-binomial models predicting VA BH outpatient reliance, veterans with SMI (IRR = 1.38, p < .05), PTSD (IRR = 1.62, p < .01), and depression (IRR = 1.36, p < .05) had higher reliance than veterans with other BHCs after Medicaid enrollment. CONCLUSIONS: While veterans did not change the amount of VA outpatient BH services they used after enrolling in Medicaid, the proportion of care they received through VA or Medicaid varied by BHC.

Differences in Risk Scores of Veterans Receiving Community Care Purchased by the Veterans Health Administration.

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.

Medicaid Spending Differences for Child/Youth Community-Based Care in California's Decentralized Public Mental Health System.

This study evaluated spending differences across counties during the decade after California decentralized its public mental health system. Medicaid data for 0-25 year olds using mental health services were collapsed to the county-year level (n = 627). Multivariate models with county fixed effects were used to predict per capita spending for community-based mental health care. While counties increased their spending over time, those with relatively low initial expenditures per user continued to spend less than counties with historically higher spending levels. Spending differences per user were most noticeable in counties with larger racial/ethnic minority populations that also had historically lower spending levels.

Use of Veterans Affairs and Medicaid Services for Dually Enrolled Veterans.

OBJECTIVES: To examine how dual coverage for nonelderly, low-income veterans by Veterans Affairs (VA) and Medicaid affects their demand for care. DATA SOURCES: Veterans Affairs utilization data and Medicaid Analytic Extract Files. STUDY DESIGN: A retrospective, longitudinal study of VA users prior to and following enrollment in Medicaid 2006-2010. DATA COLLECTION/EXTRACTION METHODS: Veterans Affairs reliance, or proportion of care provided by VA, was estimated with beta-binomial models, adjusting for patient and state Medicaid program factors. PRINCIPAL FINDINGS: In a cohort of 19,890 nonelderly veterans, VA utilization levels were similar before and after enrolling in Medicaid. VA outpatient reliance was 0.65, and VA inpatient reliance was 0.53 after Medicaid enrollment. Factors significantly associated with greater VA reliance included sociodemographic factors, having a service-connected disability, comorbidity, and higher state Medicaid reimbursement. Factors significantly associated with less VA reliance included months enrolled in Medicaid, managed care enrollment, Medicaid eligibility type, longer drive time to VA care, greater Medicaid eligibility generosity, and better Medicaid quality. CONCLUSION: 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.

Are Improvements in Measured Performance Driven by Better Treatment or "Denominator Management"?

BACKGROUND: Process measures of healthcare quality are usually formulated as the number of patients who receive evidence-based treatment (numerator) divided by the number of patients in the target population (denominator). When the systems being evaluated can influence which patients are included in the denominator, it is reasonable to wonder if improvements in measured quality are driven by expanding numerators or contracting denominators. OBJECTIVE: In 2003, the US Department of Veteran Affairs (VA) based executive compensation in part on performance on a substance use disorder (SUD) continuity-of-care quality measure. The first goal of this study was to evaluate if implementing the measure in this way resulted in expected improvements in measured performance. The second goal was to examine if the proportion of patients with SUD who qualified for the denominator contracted after the quality measure was implemented, and to describe the facility-level variation in and correlates of denominator contraction or expansion. DESIGN: Using 40 quarters of data straddling the implementation of the performance measure, an interrupted time series design was used to evaluate changes in two outcomes. PARTICIPANTS: All veterans with an SUD diagnosis in all VA facilities from fiscal year 2000 to 2009. MAIN MEASURES: The two outcomes were 1) measured performance-patients retained/patients qualified and 2) denominator prevalence-patients qualified/patients with SUD program contact. KEY RESULTS: Measured performance improved over time (P < 0.001). Notably, the proportion of patients with SUD program contact who qualified for the denominator decreased more rapidly after the measure was implemented (p = 0.02). Facilities with higher pre-implementation denominator prevalence had steeper declines in denominator prevalence after implementation (p < 0.001). CONCLUSIONS: These results should motivate the development of measures that are less vulnerable to denominator management, and also the exploration of "shadow measures" to monitor and reduce undesirable denominator management.