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<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>To assess trends in hospital price disclosures after the Centers for Medicare &amp; Medicaid Services (CMS) Final Rule went into effect.</jats:p></jats:sec><jats:sec><jats:title>Data Sources and Study Setting</jats:title><jats:p>The Turquoise Health Price Transparency Dataset was used to identify all US hospitals that publicly displayed pricing from 2021 to 2023.</jats:p></jats:sec><jats:sec><jats:title>Study Design</jats:title><jats:p>Price‐disclosing versus nondisclosing hospitals were compared using Pearson's Chi‐squared and Wilcoxon rank sum tests. Bayesian structural time‐series modeling was used to determine if enforcement of increased penalties for nondisclosure was associated with a change in the trend of hospital disclosures.</jats:p></jats:sec><jats:sec><jats:title>Data Collection/Extraction Methods</jats:title><jats:p>Not applicable.</jats:p></jats:sec><jats:sec><jats:title>Principal Findings</jats:title><jats:p>As of January 2023, 5162 of 6692 (77.1%) US hospitals disclosed pricing of their services, with the majority (2794 of 5162 [54.1%]) reporting their pricing within the first 6 months of the final rule going into effect in January 2021. An increase in hospital disclosures was observed after penalties for nondisclosure were enforced in January 2022 (relative effect size 20%, <jats:italic>p</jats:italic> = 0.002). Compared with nondisclosing hospitals, disclosing hospitals had higher annual revenue, bed number, and were more likely to be have nonprofit ownership, academic affiliation, provide emergency services, and be in highly concentrated markets (<jats:italic>p</jats:italic> &lt; 0.001).</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>Hospital pricing disclosures are continuously in flux and influenced by regulatory and market factors.</jats:p></jats:sec>

<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>To examine whether certain Medicare Advantage (MA) plan characteristics are associated with driving beneficiaries to providers that generate fewer avoidable hospital stays.</jats:p></jats:sec><jats:sec><jats:title>Data Sources</jats:title><jats:p>This paper primarily used 2018–2019 MA encounter data and traditional Medicare (TM) claims data for a nationally representative 20% sample of Medicare beneficiaries.</jats:p></jats:sec><jats:sec><jats:title>Study Design</jats:title><jats:p>For each plan design aspect—plan type, carrier, star rating, and network breadth—we estimated two adjusted Poisson regressions of avoidable hospital stays: one without clinician fixed effects and the other with. We calculated the difference between the coefficients to evaluate the extent to which patient sorting affected avoidable hospital stays relative to TM.</jats:p></jats:sec><jats:sec><jats:title>Data Extraction Methods</jats:title><jats:p>Our sample included Medicare beneficiaries 65 years and older who were continuously enrolled in either MA or TM during 2018–2019. Beneficiaries in our sample had one or more chronic, ambulatory care‐sensitive conditions.</jats:p></jats:sec><jats:sec><jats:title>Principal Findings</jats:title><jats:p>Patient sorting can be attributed to certain characteristics of plan design aspects. For plan type, HMOs account for 86%, with PPOs accounting for only 14%. For carriers, Humana and smaller carriers account for 89%. For star ratings, high‐star contracts account for 94%, with other stars only accounting for 6%. By network design, narrow network plan‐counties explained 20% of the patient sorting effect.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>While MA plans were found to be associated with driving beneficiaries to providers that generate fewer avoidable hospital stays, the effect is not homogeneous across the characteristics of MA plans. HMOs and high‐star contracts are drivers of this MA phenomenon.</jats:p></jats:sec>

<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>To evaluate racial and ethnic differences in patient experience among VA primary care users at the Veterans Integrated Service Network (VISN) level.</jats:p></jats:sec><jats:sec><jats:title>Data Source and Study Setting</jats:title><jats:p>We performed a secondary analysis of the VA Survey of Healthcare Experiences of Patients‐Patient Centered Medical Home for fiscal years 2016–2019.</jats:p></jats:sec><jats:sec><jats:title>Study Design</jats:title><jats:p>We compared 28 patient experience measures (six each in the domains of access and care coordination, 16 in the domain of person‐centered care) between minoritized racial and ethnic groups (American Indian or Alaska Native [AIAN], Asian, Black, Hispanic, Multi‐Race, Native Hawaiian or Other Pacific Islander [NHOPI]) and White Veterans. We used weighted logistic regression to test differences between minoritized and White Veterans, controlling for age and gender.</jats:p></jats:sec><jats:sec><jats:title>Data Collection/Extraction Methods</jats:title><jats:p>We defined meaningful difference as both statistically significant at two‐tailed <jats:italic>p</jats:italic> &lt; 0.05 with a relative difference ≥10% or ≤−10%. Within VISNs, we included tests of group differences with adequate power to detect meaningful relative differences from a minimum of five comparisons (domain agnostic) per VISN, and separately for a minimum of two for access and care coordination and four for person‐centered care domains. We report differences as disparities/large disparities (relative difference ≥10%/≥ 25%), advantages (experience worse or better, respectively, than White patients), or equivalence.</jats:p></jats:sec><jats:sec><jats:title>Principal Findings</jats:title><jats:p>Our analytic sample included 1,038,212 Veterans (0.6% AIAN, 1.4% Asian, 16.9% Black, 7.4% Hispanic, 0.8% Multi‐Race, 0.8% NHOPI, 67.7% White). Across VISNs, the greatest proportion of comparisons indicated disparities for three of seven eligible VISNs for AIAN, 6/10 for Asian, 3/4 for Multi‐Race, and 2/6 for NHOPI Veterans. The plurality of comparisons indicated advantages or equivalence for 17/18 eligible VISNs for Black and 12/14 for Hispanic Veterans. AIAN, Asian, Multi‐Race, and NHOPI groups had more comparisons indicating disparities by VISN in the access domain than person‐centered care and care coordination.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>We found meaningful differences in patient experience measures across VISNs for minoritized compared to White groups, especially for groups with lower population representation.</jats:p></jats:sec>

<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>To estimate a causal relationship between mental health staffing and time to initiation of mental health care for new patients.</jats:p></jats:sec><jats:sec><jats:title>Data Sources and Study Setting</jats:title><jats:p>As the largest integrated health care delivery system in the United States, the Veterans Health Administration (VHA) provides a unique setting for isolating the effects of staffing on initiation of mental health care where demand is high and out‐of‐pocket costs are not a relevant confounder. We use data from the Department of Defense and VHA to obtain patient and facility characteristics and health care use.</jats:p></jats:sec><jats:sec><jats:title>Study Design</jats:title><jats:p>To isolate exogenous variation in mental health staffing, we used an instrumental variables approach—two‐stage residual inclusion with a discrete time hazard model. Our outcome is time to initiation of mental health care after separation from active duty (first appointment) and our exposure is mental health staffing (standardized clinic time per 1000 VHA enrollees per pay period).</jats:p></jats:sec><jats:sec><jats:title>Data Collection/Extraction Methods</jats:title><jats:p>Our cohort consists of all Veterans separating from active duty between July 2014 and September 2017, who were enrolled in the VHA, and had at least one diagnosis of post‐traumatic stress disorder, major depressive disorder, and/or substance use disorder in the year prior to separation from active duty (<jats:italic>N</jats:italic> = 54,209).</jats:p></jats:sec><jats:sec><jats:title>Principal Findings</jats:title><jats:p>An increase of 1 standard deviation in mental health staffing results in a higher likelihood of initiating mental health care (adjusted hazard ratio: 3.17, 95% confidence interval: 2.62, 3.84, <jats:italic>p</jats:italic> &lt; 0.001). Models stratified by tertile of mental health staffing exhibit decreasing returns to scale.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>Increases in mental health staffing led to faster initiation of care and are especially beneficial in facilities where staffing is lower, although initiation of care appears capacity‐limited everywhere.</jats:p></jats:sec>

<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>To examine changes in hospitalization trends and healthcare utilization among Veterans following Maintaining Internal Systems and Strengthening Integrated Outside Networks (MISSION) Act implementation.</jats:p></jats:sec><jats:sec><jats:title>Data Sources and Study Setting</jats:title><jats:p>VA Corporate Data Warehouse and Centers for Medicare and Medicaid Services datasets.</jats:p></jats:sec><jats:sec><jats:title>Study Design</jats:title><jats:p>Retrospective cohort study to compare 7‐ and 30‐day rates for unplanned readmission and emergency department visits following index hospital stays based on payor type (VHA facility stay, VA‐funded stay in community facility [CC], or Medicare‐funded community stay [CMS]). Segmented regression models were used to compare payors and estimate changes in outcome levels and slopes following MISSION Act implementation.</jats:p></jats:sec><jats:sec><jats:title>Data Collection/Extraction Methods</jats:title><jats:p>Veterans with active VA primary care utilization and ≥1 acute hospitalization between January 1, 2016 and December 31, 2021.</jats:p></jats:sec><jats:sec><jats:title>Principal Findings</jats:title><jats:p>Monthly index stays increased for all payors until MISSION Act implementation, when VHA and CMS admissions declined while CC admissions accelerated and overtook VHA admissions. In December 2021, CC admissions accounted for 54% of index admissions, up from 25% in January 2016. From adjusted models, just prior to implementation (May 2019), Veterans with CC admissions had 47% greater risk of 7‐day readmission (risk ratio [RR]: 1.47, 95% confidence interval [CI]: 1.43, 1.51) and 20% greater risk of 30‐day readmission (RR: 1.20, 95% CI: 1.19, 1.22) compared with those with VHA admissions; both effects persisted post‐implementation. Pre‐implementation CC admissions were also associated with higher 7‐ and 30‐day ED visits, but both risks were substantially lower by study termination (RR: 0.90, 95% CI: 0.88, 0.91) and (RR: 0.89, 95% CI: 0.87, 0.90), respectively.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>MISSION Act implementation was associated with substantial shifts in treatment site and federal payor for Veteran hospitalizations. Post‐implementation readmission risk was estimated to be higher for those with CC and CMS index admissions, while post‐implementation risk of ED utilization following CC admissions was estimated to be lower compared with VHA index admissions. Reasons for this divergence require further investigation.</jats:p></jats:sec>

<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>To examine the impact of the Affordable Care Act (ACA) health insurance exchanges (Marketplace) on the rate of uninsured discharges in Texas.</jats:p></jats:sec><jats:sec><jats:title>Data Source and Study Setting</jats:title><jats:p>Secondary discharge data from 2011 to 2019 from Texas.</jats:p></jats:sec><jats:sec><jats:title>Study Design</jats:title><jats:p>We conducted a retrospective study estimating the effects of the ACA Marketplace using difference‐in‐difference regressions, with the main outcome being the uninsured discharge rate. We stratified our sample by patient's race, age, gender, urbanicity, major diagnostic categories (MDC), and emergent type of admissions.</jats:p></jats:sec><jats:sec><jats:title>Data Collection/Extraction Methods</jats:title><jats:p>We used Texas hospital discharge records for non‐elderly adults collected by the state of Texas and included acute care hospitals who reported data from 2011 to 2019.</jats:p></jats:sec><jats:sec><jats:title>Principal Findings</jats:title><jats:p>The expansion of insurance through ACA Marketplaces led to reductions in the uninsured discharge rate by 9.9% (95% CI, −17.5%, −2.3%) relative to the baseline mean. The effects of the ACA were felt strongest in counties with any share of Hispanic, in counties with a larger population of Black, and other racial groups, in counties with a significant share of female and older age individuals, in counties considered to be urban, in high‐volume diagnoses, and emergent type of admissions.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>These findings indicate that the ACA facilitated a shift in hospital payor mix from uninsured to insured.</jats:p></jats:sec>