Health Services Research最新文献

{"title":"The impact of Medicaid expansion on state expenditures through the COVID-19 era","authors":"Jenny Markell BA,&nbsp;Mark Katz Meiselbach PhD","doi":"10.1111/1475-6773.14331","DOIUrl":"10.1111/1475-6773.14331","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To investigate the impact of Medicaid expansion on state expenditures through the end of 2022.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources</h3>\u0000 \u0000 <p>We used data from the National Association of State Budget Officers (NASBO)'s State Expenditure Report, Kaiser Family Foundation (KFF)'s Medicaid expansion tracker, US Bureau of Labor Statistics data (BLS), US Bureau of Economic Analysis data (BEA), and Pandemic Response Accountability Committee Oversight (PRAC).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Design</h3>\u0000 \u0000 <p>We investigated spending per capita (by state population) across seven budget categories, including Medicaid spending, and four spending sources. We performed a difference-in-differences (DiD) analysis that compared within-state changes in spending over time in expansion and nonexpansion states to estimate the effect of Medicaid expansion on state budgets. We adjusted for annual state unemployment rate, annual state per capita personal income, and state spending of Coronavirus Relief Funds (CRF) from 2020 to 2022 and included state and year fixed effects.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Collection/Extraction Methods</h3>\u0000 \u0000 <p>We linked annual state-level data on state-reported fiscal year expenditures from NASBO with state-level characteristics from BLS and BEA data and with CRF state spending from PRAC.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>Medicaid expansion was associated with an average increase of 21% (95% confidence interval [CI]: 16%–25%) in per capita Medicaid spending after Medicaid expansion among states that expanded prior to 2020. After inclusion of an interaction term to separate between the coronavirus disease (COVID) era (2020–2022) and the prior period following expansion (2015–2019), we found that although Medicaid expansion led to an average increase of 33% (95% CI: 21%–45%) in federal funding of state expenditures in the post-COVID years, it was not significantly associated with increased state spending.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>There was no evidence of crowding out of other state expenditure categories or a substantial impact on total state spending, even in the COVID-19 era. Increased federal expenditures may have shielded states from substantial budgetary impacts.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141157969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Association between claims-based setting of diagnosis and treatment initiation among Medicare patients with hepatitis C","authors":"Hao Zhang PhD,&nbsp;Yuhua Bao PhD,&nbsp;Kayla Hutchings MPH,&nbsp;Martin F. Shapiro MD PhD,&nbsp;Shashi N. Kapadia MD","doi":"10.1111/1475-6773.14330","DOIUrl":"10.1111/1475-6773.14330","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To develop a claims-based algorithm to determine the setting of a disease diagnosis.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Study Setting</h3>\u0000 \u0000 <p>Medicare enrollment and claims data from 2014 to 2019.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Design</h3>\u0000 \u0000 <p>We developed a claims-based algorithm using facility indicators, revenue center codes, and place of service codes to identify settings where HCV diagnosis first appeared. When the first appearance was in a laboratory, we attempted to associate HCV diagnoses with subsequent clinical visits. Face validity was assessed by examining association of claims-based diagnostic settings with treatment initiation.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Collection/Extraction Methods</h3>\u0000 \u0000 <p>Patients newly diagnosed with HCV and continuously enrolled in traditional Medicare Parts A, B, and D (12 months before and 6 months after index diagnosis) were included.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>Among 104,454 patients aged 18–64 and 66,726 aged ≥65, 70.1% and 69%, respectively, were diagnosed in outpatient settings, and 20.2% and 22.7%, respectively in laboratory or unknown settings. Logistic regression revealed significantly lower odds of treatment initiation after diagnosis in emergency departments/urgent cares, hospitals, laboratories, or unclassified settings, than in outpatient visits.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The algorithm identified the setting of HCV diagnosis in most cases, and found significant associations with treatment initiation, suggesting an approach that can be adapted for future claims-based studies.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1475-6773.14330","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141076907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of the Affordable Care Act on access to accredited facilities for cancer treatment","authors":"Lindsay M. Sabik, Youngmin Kwon, Coleman Drake, Jonathan Yabes, Manisha Bhattacharya, Zhaojun Sun, Cathy J. Bradley, Bruce L. Jacobs","doi":"10.1111/1475-6773.14315","DOIUrl":"https://doi.org/10.1111/1475-6773.14315","url":null,"abstract":"ObjectiveTo examine differential changes in receipt of surgery at National Cancer Institute (NCI)‐designated comprehensive cancer centers (NCI‐CCC) and Commission on Cancer (CoC) accredited hospitals for patients with cancer more likely to be newly eligible for coverage under Affordable Care Act (ACA) insurance expansions, relative to those less likely to have been impacted by the ACA.Data Sources and Study SettingPennsylvania Cancer Registry (PCR) for 2010–2019 linked with discharge records from the Pennsylvania Health Care Cost Containment Council (PHC4).Study DesignOutcomes include whether cancer surgery was performed at an NCI‐CCC or a CoC‐accredited hospital. We conducted a difference‐in‐differences analysis, estimating linear probability models for each outcome that control for residence in a county with above median county‐level pre‐ACA uninsurance and the interaction between county‐level baseline uninsurance and cancer treatment post‐ACA to capture differential changes in access between those more and less likely to become newly eligible for insurance coverage (based on area‐level proxy). All models control for age, sex, race and ethnicity, cancer site and stage, census‐tract level urban/rural residence, Area Deprivation Index, and year‐ and county‐fixed effects.Data Collection/Extraction MethodsWe identified adults aged 26–64 in PCR with prostate, lung, or colorectal cancer who received cancer‐directed surgery and had a corresponding surgery discharge record in PHC4.Principal FindingsWe observe a differential increase in receiving care at an NCI‐CCC of 6.2 percentage points (95% CI: 2.6–9.8; baseline mean = 9.8%) among patients in high baseline uninsurance areas (<jats:italic>p</jats:italic> = 0.001). Our estimate of the differential change in care at the larger set of CoC hospitals is positive (3.9 percentage points [95% CI: −0.5‐8.2; baseline mean = 73.7%]) but not statistically significant (<jats:italic>p</jats:italic> = 0.079).ConclusionsOur findings suggest that insurance expansions under the ACA were associated with increased access to NCI‐CCCs.","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140837958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance of health care service area definitions for capturing variation in inpatient care and social determinants of health","authors":"Hannah Crook BSPH,&nbsp;Manuel Horta MEd,&nbsp;Kenneth A. Michelson MD, MPH,&nbsp;John A. Graves PhD","doi":"10.1111/1475-6773.14312","DOIUrl":"10.1111/1475-6773.14312","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To quantify the degree to which health care service area (HCSA) definitions captured hospitalizations and heterogeneity in social determinants of health (SDOH).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Study Setting</h3>\u0000 \u0000 <p>Geospatial data from the Centers for Medicare and Medicaid Services, the Census Bureau, and the Dartmouth Institute. Drive-time isochrones from MapBox. Area Deprivation Index (ADI) data. 2017 inpatient discharge data from Arizona, Florida, Iowa, Maryland, Nebraska, New Jersey, New York, and Wisconsin, State Emergency Department Databases and State Inpatient Databases, Healthcare Cost and Utilization Project, Agency for Healthcare Research and Quality; and Fee-For-Service Medicare data in 48 states.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Design</h3>\u0000 \u0000 <p>Cross-sectional, descriptive analysis.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Collection/Extraction Methods</h3>\u0000 \u0000 <p>The capture rate was the percentage of inpatient discharges occurring in the same HCSA as the hospital. We compared capture rates for each HCSA definition for different populations and by hospital type. We measured SDOH heterogeneity using the coefficient of variation of the ADI among ZIP codes within each HCSA.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>HCSA definitions captured a wide range of inpatient discharges, ranging from 20% to 50% for Public Use Microdata Areas (PUMAs) to 93%–97% for Metropolitan Statistical Areas (MSAs). Three-quarters of inpatient discharges were from facilities within the same county as the patient's residential ZIP code, while nearly two-thirds were within the same Hospital Service Area. From the hospital perspective, 74.7% of inpatient discharges originated from within a 30-min drive and 90.1% within a 60-min drive. Capture rates were the lowest for teaching hospitals. PUMAs and drive-time-based HCSAs encompassed more homogenous populations while MSAs, Commuting Zones, and Hospital Referral Regions captured the most variation.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The proportion of hospital discharges captured by each HCSA varied, with MSAs capturing the highest proportion of discharges and PUMAs capturing the lowest. Additionally, researchers face a trade-off between capture rate and population homogeneity when deciding which HCSA to use.</p>\u0000 </section>\u0000 ","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1475-6773.14312","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140838169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Partnership building for scale‐up in the Veteran Sponsorship Initiative: Strategies for harnessing collaboration to accelerate impact in suicide prevention","authors":"Erin P. Finley, Sheila B. Frankfurt, Nipa Kamdar, David E. Goodrich, Elyse Ganss, Chien J. Chen, Christine Eickhoff, Alison Krauss, Brigid Connelly, Richard W. Seim, Marianne Goodman, Joseph Geraci","doi":"10.1111/1475-6773.14309","DOIUrl":"https://doi.org/10.1111/1475-6773.14309","url":null,"abstract":"ObjectiveTo evaluate the implementation and trust‐building strategies associated with successful partnership formation in scale‐up of the Veteran Sponsorship Initiative (VSI), an evidence‐based suicide prevention intervention enhancing connection to U.S. Department of Veterans Affairs (VA) and other resources during the military‐to‐civilian transition period.Data Sources and Study SettingScaling VSI nationally required establishing partnerships across VA, the U.S. Department of Defense (DoD), and diverse public and private Veteran‐serving organizations. We assessed partnerships formalized with a signed memorandum during pre‐ and early implementation periods (October 2020–October 2022). To capture implementation activities, we conducted 39 periodic reflections with implementation team members over the same period.Study DesignWe conducted a qualitative case study evaluating the number of formalized VSI partnerships alongside directed qualitative content analysis of periodic reflections data using Atlas.ti 22.0.Data Collection/Extraction MethodsWe first independently coded reflections for implementation strategies, following the Expert Recommendations for Implementing Change (ERIC) taxonomy, and for trust‐building strategies, following the Theoretical Model for Trusting Relationships and Implementation; a second round of inductive coding explored emergent themes associated with partnership formation.Principal FindingsDuring this period, VSI established 12 active partnerships with public and non‐profit agencies. The VSI team reported using 35 ERIC implementation strategies, including building a coalition and developing educational and procedural documents, and trust‐building strategies including demonstrating competence and credibility, frequent interactions, and responsiveness. Cultural competence in navigating DoD and VA and accepting and persisting through conflict also appeared to support scale‐up.ConclusionsVSI's partnership‐formation efforts leveraged a variety of implementation strategies, particularly around strengthening stakeholder interrelationships and refining procedures for coordination and communication. VSI implementation activities were further characterized by an intentional focus on trust‐building over time. VSI's rapid scale‐up highlights the value of partnership formation for achieving coordinated interventions to address complex problems.","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140837954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A more complete measure of vertical integration between physicians and hospitals","authors":"Qian (Eric) Luo PhD,&nbsp;Bernard Black JD,&nbsp;David J. Magid MD, MPH,&nbsp;Frederick A. Masoudi MD, MSPH,&nbsp;Vinay Kini MD, MSHP,&nbsp;Ali Moghtaderi PhD","doi":"10.1111/1475-6773.14314","DOIUrl":"10.1111/1475-6773.14314","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To develop an accurate and reproducible measure of vertical integration between physicians and hospitals (defined as hospital or health system employment of physicians), which can be used to assess the impact of integration on healthcare quality and spending.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Study Setting</h3>\u0000 \u0000 <p>We use multiple data sources including from the Internal Revenue Service, the Centers for Medicare and Medicaid Services, and others to determine the Tax Identification Numbers (TINs) that hospitals and physicians use to bill Medicare for services, and link physician billing TINs to hospital-related TINs.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Design</h3>\u0000 \u0000 <p>We developed a new measure of vertical integration, based on the TINs that hospitals and physicians use to bill Medicare, using a broad set of sources for hospital-related TINs. We considered physicians as hospital-employed if they bill Medicare primarily or exclusively using hospital-related TINs. We assessed integration status for all physicians who billed Medicare from 1999 to 2019. We compared this measure with others used in the existing literature. We conducted a simulation study which highlights the importance of accurately identifying integrated physicians when study the effects of integration.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Collection/Extraction Methods</h3>\u0000 \u0000 <p>We extracted physician and hospital-related TINs from multiple sources, emphasizing specificity (a small proportion of nonintegrated physicians identified as integrated).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>We identified 12,269 hospital-related TINs, used for billing by 546,775 physicians. We estimate that the percentage of integrated physicians rose from 19% in 1999 to 43% in 2019. Our approach identifies many additional physician practices as integrated; a simpler TIN measure, comparable with prior work, identifies only 30% (3877) of the TINs we identify. A service location measure, used in prior work, has both many false positives and false negatives.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>We developed a new measure of hospital-physician integration. This measure is reproducible and identifies many additional physician practices as integrated.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140837969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Split-sample reliability estimation in health care quality measurement: Once is not enough","authors":"Kenneth J. Nieser PhD,&nbsp;Alex H. S. Harris PhD, MS","doi":"10.1111/1475-6773.14310","DOIUrl":"10.1111/1475-6773.14310","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To examine the sensitivity of split-sample reliability estimates to the random split of the data and propose alternative methods for improving the stability of the split-sample method.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Study Setting</h3>\u0000 \u0000 <p>Data were simulated to reflect a variety of real-world quality measure distributions and scenarios. There is no date range to report as the data are simulated.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Design</h3>\u0000 \u0000 <p>Simulation studies of split-sample reliability estimation were conducted under varying practical scenarios.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Collection/Extraction Methods</h3>\u0000 \u0000 <p>All data were simulated using functions in <i>R</i>.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>Single split-sample reliability estimates can be very dependent on the random split of the data, especially in low sample size and low variability settings. Averaging split-sample estimates over many splits of the data can yield a more stable reliability estimate.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Measure developers and evaluators using the split-sample reliability method should average a series of reliability estimates calculated from many resamples of the data without replacement to obtain a more stable reliability estimate.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140660137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"New evidence on the impacts of cross-market hospital mergers on commercial prices and measures of quality.","authors":"Daniel R Arnold, Jaime S King, Brent D. Fulton, Alexandra D. Montague, Katherine L. Gudiksen, Thomas L Greaney, Richard M. Scheffler","doi":"10.1111/1475-6773.14291","DOIUrl":"https://doi.org/10.1111/1475-6773.14291","url":null,"abstract":"OBJECTIVE\u0000To examine the impact of \"cross-market\" hospital mergers on prices and quality and the extent to which serial acquisitions contribute to any measured effects.\u0000\u0000\u0000DATA SOURCES\u00002009-2017 commercial claims from the Health Care Cost Institute (HCCI) and quality measures from Hospital Compare.\u0000\u0000\u0000STUDY DESIGN\u0000Event study models in which the treated group consisted of hospitals that acquired hospitals further than 50 miles, and the control group was hospitals that were not part of any merger activity (as a target or acquirer) during the study period.\u0000\u0000\u0000DATA EXTRACTION METHODS\u0000We extracted data for 214 treated hospitals and 955 control hospitals.\u0000\u0000\u0000PRINCIPAL FINDINGS\u0000Six years after acquisition, cross-market hospital mergers had increased acquirer prices by 12.9% (CI: 0.6%-26.6%) relative to control hospitals, but had no discernible impact on mortality and readmission rates for heart failure, heart attacks and pneumonia. For serial acquirers, the price effect increased to 16.3% (CI: 4.8%-29.1%). For all acquisitions, the price effect was 21.8% (CI: 4.6%-41.7%) when the target's market share was greater than the acquirer's market share versus 9.7% (CI: -0.5% to 20.9%) when the opposite was true. The magnitude of the price effect was similar for out-of-state and in-state cross-market mergers.\u0000\u0000\u0000CONCLUSIONS\u0000Additional evidence on the price and quality effects of cross-market mergers is needed at a time when over half of recent hospital mergers have been cross-market. To date, no hospital mergers have been challenged by the Federal Trade Commission on cross-market grounds. Our study is the third to find a positive price effect associated with cross-market mergers and the first to show no quality effect and how serial acquisitions contribute to the price effect. More research is needed to identify the mechanism behind the price effects we observe and analyze price effect heterogeneity.","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140668808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Association between physician-hospital integration and inpatient care delivery in accountable care organizations: An instrumental variable analysis.","authors":"Meng-Yun Lin, A. Hanchate, Austin B Frakt, James F. Burgess, Kathleen Carey","doi":"10.1111/1475-6773.14311","DOIUrl":"https://doi.org/10.1111/1475-6773.14311","url":null,"abstract":"OBJECTIVE\u0000To investigate the relationship between physician-hospital integration within accountable care organizations (ACOs) and inpatient care utilization and expenditure.\u0000\u0000\u0000DATA SOURCES\u0000The primary data were Massachusetts All-Payer Claims Database (2009-2013).\u0000\u0000\u0000STUDY SETTING\u0000Fifteen provider organizations that entered a commercial ACO contract with a major private payer in Massachusetts between 2009 and 2013.\u0000\u0000\u0000STUDY DESIGN\u0000Using an instrumental variable approach, the study compared inpatient care delivery between patients of ACOs demonstrating high versus low integration. We measured physician-hospital integration within ACOs by the proportion of primary care physicians in an ACO who billed for outpatient services with a place-of-service code indicating employment or practice ownership by a hospital. The study sample comprised non-elderly adults who had continuous insurance coverage and were attributed to one of the 15 ACOs. Outcomes of interest included total medical expenditure during an episode of inpatient care, length of stay (LOS) of the index hospitalization, and 30-day readmission. An inpatient episode was defined as 30, 45, and 60 days from the admission date.\u0000\u0000\u0000DATA COLLECTION/EXTRACTION METHODS\u0000Not applicable.\u0000\u0000\u0000PRINCIPAL FINDINGS\u0000The study examined 33,535 admissions from patients served by the 15 ACOs. Average medical expenditure within 30 days of admission was $24,601, within 45 days was $26,447, and within 60 days was $28,043. Average LOS was 3.5 days, and 5.4% of patients were readmitted within 30 days. Physician-hospital integration was associated with a 10.6% reduction in 30-day expenditure (95% CI, -15.1% to -5.9%). Corresponding estimates for 45 and 60 days were - 9.7% (95%CI, -14.2% to -4.9%) and - 9.6% (95%CI, -14.3% to -4.7%). Integration was associated with a 15.7% decrease in LOS (95%CI, -22.6% to -8.2%) but unrelated to 30-day readmission rate.\u0000\u0000\u0000CONCLUSIONS\u0000Our instrumental variable analysis shows physician-hospital integration with ACOs was associated with reduced inpatient spending and LOS, with no evidence of elevated readmission rates.","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140668774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The business case for hospital mobility programs in the veterans health care system: Results from multi‐hospital implementation of the STRIDE program","authors":"Brystana G. Kaufman, S. Nicole Hastings, Cassie Meyer, Karen M. Stechuchak, Ashley Choate, Kasey Decosimo, Caitlin Sullivan, Virginia Wang, Kelli D. Allen, Courtney H. Van Houtven","doi":"10.1111/1475-6773.14307","DOIUrl":"https://doi.org/10.1111/1475-6773.14307","url":null,"abstract":"ObjectiveTo conduct a business case analysis for Department of Veterans Affairs (VA) program STRIDE (ASsisTed EaRly MobIlization for hospitalizeD older VEterans), which was designed to address immobility for hospitalized older adults.Data Sources and Study SettingThis was a secondary analysis of primary data from a VA 8‐hospital implementation trial conducted by the Function and Independence Quality Enhancement Research Initiative (QUERI). In partnership with VA operational partners, we estimated resources needed for program delivery in and out of the VA as well as national implementation facilitation in the VA. A scenario analysis using wage data from the Bureau of Labor Statistics informs implementation decisions outside the VA.Study DesignThis budget impact analysis compared delivery and implementation costs for two implementation strategies (Replicating Effective Programs [REP]+CONNECT and REP‐only). To simulate national budget scenarios for implementation, we estimated the number of eligible hospitalizations nationally and varied key parameters (e.g., enrollment rates) to evaluate the impact of uncertainty.Data CollectionPersonnel time and implementation outcomes were collected from hospitals (2017–2019). Hospital average daily census and wage data were estimated as of 2022 to improve relevance to future implementation.Principal FindingsAverage implementation costs were $9450 for REP+CONNECT and $5622 for REP‐only; average program delivery costs were less than $30 per participant in both VA and non‐VA hospital settings. Number of walks had the most impact on delivery costs and ranged from 1 to 5 walks per participant. In sensitivity analyses, cost increased to $35 per participant if a physical therapist assistant conducts the walks. Among study hospitals, mean enrollment rates were higher among the REP+CONNECT hospitals (12%) than the REP‐only hospitals (4%) and VA implementation costs ranged from $66 to $100 per enrolled.ConclusionsSTRIDE is a low‐cost intervention, and program participation has the biggest impact on the resources needed for delivering STRIDE.Trial Registration<jats:ext-link xmlns:xlink=\"http://www.w3.org/1999/xlink\" xlink:href=\"http://clinicalstrials.gov\">ClinicalsTrials.gov</jats:ext-link> NCT03300336. Prospectively registered on 3 October 2017.","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140626754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}