Health Services Research最新文献

{"title":"The Impact of Increased Medicaid Eligibility During Pregnancy on Medicaid Utilization and Gestational Age.","authors":"Nicolas P Goldstein Novick, Peter J Veazie, Elaine L Hill, Eva K Pressman, Peter G Szilagyi, Timothy D Nelin, Scott A Lorch","doi":"10.1111/1475-6773.70037","DOIUrl":"https://doi.org/10.1111/1475-6773.70037","url":null,"abstract":"<p><strong>Objective: </strong>To assess the impact of increased Medicaid income eligibility during pregnancy on payment source for prenatal care and birth and on gestational age at birth (GAb).</p><p><strong>Study setting and design: </strong>We performed a quasi-experimental, difference-in-differences study comparing two increases in Medicaid income eligibility during pregnancy to two control states with data from 2007 to 2010: (Dyad 1) Ohio (expanded from 150% to 200% of the Federal Poverty level [FPL]) versus Pennsylvania and (Dyad 2) Wisconsin (185% to 250% FPL) versus Michigan. We performed multinomial logistic regression to assess the impact of increased Medicaid eligibility on the following key outcome variables: payment source for prenatal care and birth and GAb.</p><p><strong>Data sources and analytic sample: </strong>We utilized CDC Pregnancy Risk Assessment Monitoring System (PRAMS) data (2007-2010) and limited analysis to singleton, in-state live births. After re-weighting for PRAMS survey design, our analytical sample represented about 540,000 births.</p><p><strong>Principal findings: </strong>In the higher-income Wisconsin-Michigan dyad, increased Medicaid eligibility during pregnancy significantly increased exclusive Medicaid coverage for prenatal care (7.0%, 95% CI 2.9% to 11.1%) and birth (8.3%, 4.3% to 12.4%). Simultaneously, private insurance coverage dropped for prenatal care (-4.0%, -7.7% to -0.3%) and birth (-3.7%, -7.2% to -0.2%) while self-payment decreased only for birth (-1.8%, -3.5% to -0.2%). In the lower-income Ohio-Pennsylvania dyad, the only statistically significant effects on payment source were decreases in the likelihood of a payment source of other for prenatal care (-3.3%, -6.2% to -0.3%) and birth (-4.7%, -7.9% to -1.6%). There were no statistically significant effects on GAb across both dyads.</p><p><strong>Conclusions: </strong>Increased Medicaid eligibility during pregnancy for individuals of higher income seems to improve utilization of exclusive Medicaid with diminished uninsurance but also less private insurance after accounting for indicators of socioeconomic advantage but has no clear impact on GAb. Medicaid policy should balance reducing uninsurance with directing scarce resources to high-risk individuals.</p>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":" ","pages":"e70037"},"PeriodicalIF":3.2,"publicationDate":"2025-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145066230","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":"Bundled Payment Programs and Changes in Practice Patterns and Episode Spending in Major Gastrointestinal Surgery.","authors":"Cody Lendon Mullens, David Schwartzman, Samantha L Savitch, Jyothi R Thumma, Scott E Regenbogen, Justin B Dimick, Edward C Norton, Kyle H Sheetz","doi":"10.1111/1475-6773.70046","DOIUrl":"https://doi.org/10.1111/1475-6773.70046","url":null,"abstract":"<p><strong>Objective: </strong>To evaluate the association between enrollment in the Bundled Payments for Care Improvement -Advanced (BPCI-A) program and changes in utilization of minimally invasive surgery and 90-day episode spending for patients undergoing major gastrointestinal surgery.</p><p><strong>Study setting and design: </strong>We compared hospitals that voluntarily enrolled in BPCI-A to control hospitals that did not participate. We used entropy balancing to reweight controls to match the BPCI-A cohort based on observable patient and hospital characteristics. We then used a difference-in-differences approach to estimate the association between surgical approach and 90-day episode payments.</p><p><strong>Data sources and analytic sample: </strong>We used Medicare claims and American Hospital Association data between 2013 and 2021 to evaluate whether hospital enrollment in the BPCI-A program was associated with changes in 90-day episode spending and utilization of minimally invasive surgical approaches. Using entropy balancing, we reweighted the control group to achieve covariate balance with beneficiaries who obtained care at BPCI-A program hospitals. We performed a difference-in-differences analysis using multivariable linear and generalized linear models, adjusting for patient demographics, comorbidities, and hospital characteristics, with standard errors clustered at the hospital-year level to evaluate these outcomes.</p><p><strong>Principal findings: </strong>Changes in 90-day episode payments at BPCI-A program hospitals versus non-program hospitals were not significantly different (-$172, 95% CI: -$1104 to $760). In comparing trends at BPCI-A program and control hospitals, we identified no significant differences in utilization trends for minimally invasive surgical approaches (relative risk difference: -0.003, 95% CI: -0.10 to 0.04). The similarity in utilization trends between BPCI-A program and control hospitals was observed in the context of increasing overall utilization of MIS approaches from 40.3 to 38.4 to 43.9 to 42.9 during the study period, respectively.</p><p><strong>Conclusions: </strong>We found no evidence that hospitals participating in BPCI-A's major bowel surgery episodes led to differences in episode spending or utilization of minimally invasive surgical approaches.</p>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":" ","pages":"e70046"},"PeriodicalIF":3.2,"publicationDate":"2025-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145066191","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 of School-Based Health Center Availability With Child Mental Health Outcomes.","authors":"Carrie E Fry, Mason Shero, Melinda B Buntin, Carolyn J Heinrich","doi":"10.1111/1475-6773.70042","DOIUrl":"https://doi.org/10.1111/1475-6773.70042","url":null,"abstract":"<p><strong>Objective: </strong>To estimate changes in student mental health outcomes after the adoption of a school-based health center (SBHC).</p><p><strong>Study setting/design: </strong>Using a retrospective, quasi-experimental design, this study compared changes in mental health diagnoses and healthcare utilization among students in school districts that adopted an SBHC to students in districts that did not adopt an SBHC, before and after adoption. A stacked difference-in-differences estimator was used to address the staggered adoption of SBHCs and the potential for heterogeneous treatment effects. Health conditions (measured via diagnosis codes) and health care use (measured via procedure codes and place-of-service codes) were obtained from Medicaid inpatient, outpatient, physician, and pharmacy claims.</p><p><strong>Data sources and analytic sample: </strong>Information on the availability of SBHCs was obtained via census of 142 of Tennessee's 147 public school districts. Using secondary data from administrative health and education records, we probabilistically linked Tennessee students enrolled in Medicaid to public-school records from 2006 to 2021. We linked approximately 70% of students enrolled in a Tennessee public school to Medicaid records.</p><p><strong>Principal findings: </strong>We identified 41 districts with an SBHC between 2007 and 2019. After the adoption of an SBHC, districts with an SBHC had a 0.5 (95% CI: -0.9, -0.2) percentage point decline in the proportion of students with any mental health diagnosis, which corresponds to a 6.6% relative decline. This was driven by a decrease in the diagnosis of depression, anxiety, and attention deficit and hyperactivity disorder (ADHD). We also found a significant increase in outpatient mental health care visits and a decrease in emergency department visits for mental health conditions after the adoption of an SBHC.</p><p><strong>Conclusions: </strong>SBHCs are one mechanism through which the mental health needs of school-aged children are met. Timely and adequate resources are needed to ensure SBHCs can sustain their services in this time of need.</p>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":" ","pages":"e70042"},"PeriodicalIF":3.2,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145042475","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":"Racial Disparities in Medication Adherence and the Patient-Provider Relationship: Does Racial/Ethnic Concordance Matter?","authors":"Alyson Ma, Jason Campbell, Alison Sanchez, Steven Sumner, Mindy Ma","doi":"10.1111/1475-6773.70040","DOIUrl":"https://doi.org/10.1111/1475-6773.70040","url":null,"abstract":"<p><strong>Objective: </strong>To examine the impact of patient-provider racial/ethnic concordance on adherence to a prescribed medication regimen in marginalized populations with a focus on health issues related to hypertension, heart condition/disease, elevated cholesterol, and diabetes.</p><p><strong>Study setting and design: </strong>Applying the Andersen-Newman Behavioral Model of Health Service Use, we estimate multivariate linear models to analyze the number of prescriptions filled by patients within a calendar year using publicly available data from the Medical Expenditure Panel Survey (MEPS), a set of large-scale surveys of families and individuals, their medical providers, and employers across the United States.</p><p><strong>Data sources and analytic sample: </strong>Data from MEPS on patient race/ethnicity and provider race/ethnicity were collected from survey years 2007 to 2017 as well as data to control for demographic, socioeconomic, and health factors. Our sample includes 238,355 observations, including 46.1% White respondents, 27.1% Hispanic respondents, 19.3% Black respondents, and 7.5% Asian respondents. There are 52,069 (about 22%) cases of patient-provider concordance.</p><p><strong>Principal findings: </strong>We find a positive association between adherence to a prescribed medication regime and racial/ethnic patient-provider concordance. Patients identifying as non-White fill their prescriptions approximately three times less often than White patients. Relative to White patients in racial/ethnic concordance with their providers, there is an increase in the number of filled prescriptions for Black patients in racial/ethnic concordance with their providers (coef = 0.715; p = 0.02). For patients with hypertension, being in a racial/ethnic concordant relationship with their providers increases the number of prescription refills (White: coef = 1.884, p < 0.001; Black: coef = 2.360, p < 0.001; Hispanic: 1.925, p < 0.001; Asian: 1.461, p = 0.003). The number of prescription refills also increases for White (coef = 1.665, p < 0.001), Hispanic (coef = 3.469, p < 0.001), and Asian (3.796, p < 0.001) patients with heart condition/disease and in racial/ethnic concordance with their providers.</p><p><strong>Conclusions: </strong>The results provide evidence supporting that patients in racial/ethnic concordant relationships with their providers have a greater predisposition to medication adherence even after controlling for enabling, need, and other predisposing factors, particularly for patients with certain chronic medical diseases. Health policy implications are discussed.</p>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":" ","pages":"e70040"},"PeriodicalIF":3.2,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145034573","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":"Hospitals in Some States Under Report Medicaid Discharge Counts in Cost Report Data.","authors":"Kelsey Chalmers, Omkar Waghmare, Valérie Gopinath, Vikas Saini","doi":"10.1111/1475-6773.70043","DOIUrl":"https://doi.org/10.1111/1475-6773.70043","url":null,"abstract":"<p><strong>Objective: </strong>To investigate discrepancies in Medicaid enrollees' hospital discharges reported in two data sources widely used in health services research: the CMS Hospital Cost Report Information System (HCRIS) and the T-MSIS Analytic Files (TAF).</p><p><strong>Study setting and design: </strong>This is a descriptive study comparing inpatient discharges reported in the two data sets. We included inpatient admissions at general hospitals in 2020-2021.</p><p><strong>Data sources and analytic sample: </strong>We used HCRIS data covering reporting periods starting in 2020 and ending sometime in 2021 (this varied by hospital) and extracted the reported total and Health Maintenance Organization (HMO) funded Medicaid discharges and patient days. We used the 2020 and 2021 TAF inpatient files and included inpatient admissions within each hospital's HCRIS reporting period, and calculated discharges for each hospital.</p><p><strong>Principal findings: </strong>There were 25 states where some hospitals had higher TAF discharge counts than HCRIS, and these same hospitals had inconsistent reporting of HMO-funded Medicaid discharges and patient days in HCRIS. This included California, New York, and Texas. There were 20 states with similar values reported in both HCRIS and TAF, and 9 of these were in states with < 5% of their enrolled Medicaid population in a comprehensive managed care plan.</p><p><strong>Conclusions: </strong>The discrepancies between HCRIS and TAF data indicate that HCRIS may not reliably capture hospital discharge volumes for Medicaid patients, particularly those funded by managed care. These inconsistencies can misinform policy decisions and evaluations of hospital performance. Policymakers and researchers should exercise caution when using HCRIS data for Medicaid discharge counts and consider supplementing it with TAF or other sources.</p>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":" ","pages":"e70043"},"PeriodicalIF":3.2,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145024896","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 Critical Examination of the Certified Community Behavioral Health Clinic Model: Provider Perceptions and Themes.","authors":"Tugba Olgac, Emma McCann, Michelle Riske-Morris, David L Hussey","doi":"10.1111/1475-6773.70041","DOIUrl":"https://doi.org/10.1111/1475-6773.70041","url":null,"abstract":"<p><strong>Objective: </strong>To explore the experiences of providers from two community behavioral health agencies involved in the implementation of Certified Community Behavioral Health Clinics (CCBHCs).</p><p><strong>Study setting and design: </strong>This qualitative study was conducted as part of a larger evaluation of CCBHC implementation outcomes in two community-based behavioral health agencies. Ninety-one participants, including case managers, counselors, care coordinators, and leadership teams from both agencies, participated in focus group discussions to share their experiences regarding the implementation of the CCBHC model within their organizations.</p><p><strong>Data sources and analytic sample: </strong>Three rounds of focus group discussions were held between 2021 and 2023. A total of 24 focus groups were audio-recorded and transcribed by one of the researchers. Qualitative data was analyzed by two researchers using the systematic text condensation method.</p><p><strong>Principal findings: </strong>Six themes emerged from the focus groups reflecting both positive impacts and implementation challenges. Providers reported the implementation of CCBHCs improved service accessibility and effective care coordination; however, staff noted difficulties connecting clients with essential community resources, including housing and transportation. Both agencies underwent significant organizational transformation, although communication strategies varied by agency size. Finally, providers observed improved communication, client benefits (e.g., reduced hospitalizations), and positive organizational change. Despite these successes, agencies expressed significant concerns about long-term program viability due to reliance on temporary grant funding.</p><p><strong>Conclusion: </strong>The CCBHC model of integrated care has expanded significantly in recent years. Most participants reported a positive cultural shift within their agencies following CCBHC implementation. However, limited community resources continue to restrict agencies' ability to address clients' basic needs. Since the CCBHC model was implemented through temporary grant funding, sustainability remains a concern. Both issues underscore the need for policies that increase the availability of community resources and ensure the long-term viability of CCBHCs.</p>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":" ","pages":"e70041"},"PeriodicalIF":3.2,"publicationDate":"2025-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145014476","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 Impact of Team-Based Ordering Workflows on Ambulatory Physician EHR Time, Order Volume, and Visit Volume.","authors":"Nate C Apathy, Alice S Yan, A Jay Holmgren","doi":"10.1111/1475-6773.70038","DOIUrl":"https://doi.org/10.1111/1475-6773.70038","url":null,"abstract":"<p><strong>Objective: </strong>To analyze national rates of team-based ordering and evaluate changes in key outcomes following adoption.</p><p><strong>Study setting and design: </strong>We conducted an observational pre-post intervention-comparison study of 249,463 ambulatory physicians across 401 organizations using the Epic EHR. Our intervention was the adoption of team-based ordering, measured as the proportion of orders involving team support. Outcomes include active ordering time, overall EHR time, order volume, and visit volume among adopter physicians.</p><p><strong>Data sources and analytic sample: </strong>We analyzed the distribution and trends in team-based ordering rates from Epic Signal (September 2019-March 2022). We used multi-variable regression in a difference-in-differences framework to evaluate changes in our outcomes among 115 adopters of team-based ordering and 3115 non-adopters. We defined adopters as physicians who demonstrated a one-time shift from 0% of orders to a consistent non-zero share of orders, and non-adopters as those who demonstrated constant 0% teamwork for at least 18 months.</p><p><strong>Principal findings: </strong>Across our study period, 26.2% of orders involved team support, with surgical specialists averaging greater team-based ordering (43.1%) than primary care (22.2%) and medical specialists (23.0%). There was no association between team-based ordering adoption and time spent ordering (-0.13 min/visit, 95% CI: [-0.48 to 0.22]) or total EHR time (-1.42 min/visit, [-3.79 to 0.95]). Adoption was associated with a 26.8% relative increase in order volume (0.47 orders/visit, [0.14-0.80]) and a 22.3% relative increase in visit volume (6.50 visits/week [2.81-10.19]).</p><p><strong>Conclusions: </strong>Team-based ordering rates are relatively low, and new adoption of team-based ordering was not associated with physicians' time spent ordering or in the EHR overall. Teamwork may facilitate substantial increases in both order and visit volume, but a greater level of team-based ordering may be required to realize EHR time savings.</p>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":" ","pages":"e70038"},"PeriodicalIF":3.2,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145006839","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 Causal Machine Learning Framework for Estimating the Impact of Cancer Diagnosis on Receipt of Advance Care Planning.","authors":"Aaron Baird, Yichen Cheng, Jason Lesandrini, Yusen Xia","doi":"10.1111/1475-6773.70039","DOIUrl":"https://doi.org/10.1111/1475-6773.70039","url":null,"abstract":"<p><strong>Objective: </strong>Develop a causal machine learning (causal ML) framework for estimating how a diagnosis (cancer in this study) affects the likelihood of receiving a specific health care service (advance care planning in this study) and associated heterogeneity.</p><p><strong>Study setting and design: </strong>Our proposed framework leverages the causal forest method, combined with a population-weighted resampling and averaging over estimations strategy, to estimate average treatment effects (ATEs) and conditional average treatment effects (CATEs). Post hoc, we used best linear projections to identify covariates associated with variation in the CATEs. We illustrate the framework by applying it to a stratified random sample of patients, where the strata are defined by the crosstabulation of cancer diagnosis (diagnosed vs. not diagnosed) and ACP receipt (documented vs. not documented).</p><p><strong>Data sources and analytic sample: </strong>We extracted deidentified patient data from October 2019 to October 2024 (n = 87,772) with explanatory variables in three categories: demographics, morbidity, and health care system utilization.</p><p><strong>Principal findings: </strong>In application of the causal ML framework, we found that patients diagnosed with cancer at this health care system to be at least 17.2% more likely to have documented ACP than similar patients not diagnosed with cancer. We also found significant heterogeneity. For instance, a one standard deviation increase in in-person outpatient visits was associated with an on-average increase in the CATE estimate (by 6.1 percentage points), while a one standard deviation increase in hospital admissions, inpatient days, and surgical duration in minutes was associated with an on-average decrease in the CATE estimate (by -1.3, -5.6, and -0.5 percentage points, respectively).</p><p><strong>Conclusions: </strong>The proposed causal ML framework enables estimation of the effect of a diagnosis on receiving a relevant health care service. In the cancer diagnosis context, it can identify patient groups less likely to receive ACP, thus informing service allocation strategies.</p>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":" ","pages":"e70039"},"PeriodicalIF":3.2,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145006899","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":"Clinician Specialties, Quality Score and Shared Savings Receipt in Accountable Care Organizations.","authors":"Mariétou H Ouayogodé, Xiaodan Liang","doi":"10.1111/1475-6773.70033","DOIUrl":"https://doi.org/10.1111/1475-6773.70033","url":null,"abstract":"<p><strong>Objective: </strong>To assess the relationship between the changing Accountable Care Organizations-ACO workforce and ACOs' shared savings earnings and quality performance.</p><p><strong>Data sources: </strong>Medicare Shared Savings Program-MSSP provider-level research identifiable files, performance year financial and quality report public use files, and National Physician Compare data (2013-2021).</p><p><strong>Study setting and design: </strong>We characterized 865 MSSPs, separately pre- (2013-2019) and post-pandemic (2020-2021) according to the percentage of primary care physicians (PCPs), non-physicians, specialists, and other specialty, financial risk model, assigned Medicare beneficiary demographics, clinical risk factors, and provider supply by specialty within the MSSP's primary service state, (total and per-capita) shared savings earnings/losses owed and quality score. Longitudinal ordinary least-squares regressions with random effects were estimated to assess the association between MSSP provider specialty mix and annual (1) per-capita shared savings/losses and (2) quality score, controlling for risk model, beneficiary characteristics, provider supply, and year factors. We also compared outcomes across MSSPs, 32 Pioneers and 62 Next Generation-NGACOs.</p><p><strong>Principal findings: </strong>PCPs represented 33.9% of MSSP's workforce, on average. Higher percentages of PCPs and non-physicians were associated with higher per-capita earned shared savings and quality scores among MSSPs. A 1-percentage-point (ppt) increase in PCPs and non-physicians was associated with higher per-capita shared savings of $2.25 (p < 0.01) and $1.82 (p = 0.03), respectively, pre-COVID, and $2.73 (p < 0.01) and $1.81 (p = 0.14) post-COVID. We estimated increases in quality scores among MSSPs of ~0.1 ppt with a 1 ppt increase in PCPs, non-physicians, and specialists only pre-pandemic. No statistically significant relationships were estimated between provider specialty mix and performance measures in Pioneers and NGACOs.</p><p><strong>Conclusions: </strong>Higher percentages of PCPs and non-physicians were associated with higher per-capita shared savings earnings and quality scores among MSSPs. As new federal initiatives continue to unfold, value-based payment models increasing incentives for primary care should be monitored to determine their ability to further improve care efficiency.</p>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":" ","pages":"e70033"},"PeriodicalIF":3.2,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145001916","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":"Clinician Specialization in Skilled Nursing Facility Practice and Post-Acute Outcomes of Patients With Dementia.","authors":"Seiyoun Kim, Hye-Young Jung, Derek Lake, Rebecca T Brown, Rachel M Werner, Jason Karlawish, Kira Ryskina","doi":"10.1111/1475-6773.70035","DOIUrl":"https://doi.org/10.1111/1475-6773.70035","url":null,"abstract":"<p><strong>Objective: </strong>To evaluate the effects of physician and advanced practitioner specialization in skilled nursing facility (SNF)-based practice (SNFists) on the outcomes of patients with Alzheimer's disease and related dementias (ADRD) admitted to SNF for post-acute care.</p><p><strong>Study setting and design: </strong>Taking advantage of the natural experiment provided by the growth of SNFists, we conducted a within-SNF difference-in-differences analysis with cross-temporal matching. Our primary outcome was functional improvement at SNF discharge, measured using a validated activities of daily living (ADL) score. Secondary outcomes included unplanned rehospitalization, emergency department (ED) visits, observational stays within 30 days of SNF admission, successful discharge to the community, SNF length of stay, admission into long-term nursing home care within 6 months of SNF discharge, and 30- and 60-day Medicare payments for professional and facility services.</p><p><strong>Data sources and analytic sample: </strong>Medicare facility and professional claims and Nursing Home Minimum Data Set (MDS) data from 2012 and 2019 were used. The study sample included 338,574 community-dwelling fee-for-service Medicare beneficiaries with ADRD, age 65 or older, discharged from an acute care hospital to one of the 5196 SNFs that experienced an increase in patients treated by SNFists.</p><p><strong>Principal findings: </strong>We did not observe an association between SNFist care and patient post-acute care outcomes or costs.</p><p><strong>Conclusions: </strong>Specialization in SNF-based practice among physicians and advanced practitioners alone may not be an effective strategy to improve post-acute care outcomes or reduce costs to Medicare for patients with ADRD.</p>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":" ","pages":"e70035"},"PeriodicalIF":3.2,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144979391","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}