Health Services Research最新文献

{"title":"Exploring the Early Effects of State Consumer Protection Policies on Medical Debt in Collections","authors":"Fredric Blavin,&nbsp;Breno Braga,&nbsp;Michael Karpman,&nbsp;Dulce Gonzalez,&nbsp;Maanasa Kona","doi":"10.1111/1475-6773.70068","DOIUrl":"10.1111/1475-6773.70068","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To test if state consumer protection policies reduce the share of consumers with medical debt in collections on their credit reports.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Setting and Design</h3>\u0000 \u0000 <p>This study uses a quasi-experimental research design to estimate the impact of consumer protection laws implemented between 2020 and 2022 in Illinois, Maryland, New Mexico, and Oregon on the share of consumers with medical debt in collections. These laws primarily aim to protect consumers against medical debt by expanding access to hospital financial assistance. We use a synthetic control approach to estimate changes in medical debt following the implementation of policies in treatment states relative to changes in select control states. We also assess the effects of earlier policies implemented between 2013 and 2019 in Washington, Utah, and North Carolina.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Analytic Sample</h3>\u0000 \u0000 <p>This analysis relies on two extracts of credit bureau data from one of the country's three main credit bureau agencies. The first extract consists of random samples from June 2017 to June 2024 of approximately 125,000 consumers in each treatment state and 500,000 residents from the pool of 14 selected comparison states in each year. The second extract is based on a 2%–4% random sample of consumers in each year from 2011 to 2022.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>We did not observe a statistically significant reduction in medical debt associated with policies implemented in these states within the study timeframe. In most states in our primary analysis, point estimates of the treatment effects are near zero, and in nearly all state-years, we can only rule out declines in medical debt larger than 1–3 percentage points following policy implementation.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Though we did not detect statistically significant effects of recent consumer protection policies on medical debt in collections, additional research is needed on whether these policies benefited consumers in ways that are not measured in this analysis and whether states that continue to move forward with similar laws can improve their effectiveness by extending consumer protections to a wider group of patients and providers and addressing implementation and enforcement challenges.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"61 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145544279","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":"Nursing Homes as Insurers? The Effect of Provider-Led Institutional Special Needs Plans","authors":"Amanda C. Chen,&nbsp;J. Michael McWilliams,&nbsp;Mary Beth Landrum,&nbsp;David C. Grabowski","doi":"10.1111/1475-6773.70067","DOIUrl":"10.1111/1475-6773.70067","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To estimate the effect of starting a provider-led Institutional Special Needs Plan (I-SNP) arrangement on facility-level enrollment, utilization, and quality.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Setting and Design</h3>\u0000 \u0000 <p>I-SNPs are a type of Medicare Advantage (MA) plan that allows insurers to differentiate their benefits exclusively for long-term residents in nursing homes. Since I-SNPs first became available in 2006, there has been growth in provider-led I-SNPs where nursing homes are financially integrated or partnered with an insurer to operate a plan for their own residents. We used a difference-in-differences design to estimate the effect of starting a provider-led I-SNP arrangement on several facility-level outcomes, including the share of a facility's long-stay residents who were enrolled in an I-SNP, hospitalizations, medication use, pressure ulcers, physical restraints, falls, and mortality.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Analytic Sample</h3>\u0000 \u0000 <p>We used Medicare claims and nursing home resident assessments (2004–2021) to identify Medicare long-stay nursing home residents.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>The start of a provider-led I-SNP arrangement led to a 17.0 percentage point (pp) increase (standard error [SE]: 0.006) in I-SNP enrollment among facility residents within 4 years relative to control nursing homes. We also estimate that the start of a provider-led I-SNP arrangement significantly decreased hospitalizations (−1.0 pp, SE: 0.002), increased the use of antipsychotic (0.4 pp, SE: 0.002) and hypnotic drugs (0.3 pp, SE: 0.001), and reporting of pressure ulcers (0.4 pp, SE: 0.002).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Provider-led I-SNPs allow nursing homes to bear financial risk for their residents. These results suggest that this form of risk bearing may successfully reduce utilization (e.g., hospitalizations), but with unclear implications for quality as increased use of sedating drugs and rates of pressure ulcers could either reflect poorer care or retention of sicker patients due to lower hospitalization rates.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"61 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145483933","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":"Incidence, Persistence, and Steady-State Prevalence in Coding Intensity for Health Plan Payment","authors":"Thomas G. McGuire,&nbsp;Oana M. Enache,&nbsp;Michael Chernew,&nbsp;J. Michael McWilliams,&nbsp;Tram Nham,&nbsp;Sherri Rose","doi":"10.1111/1475-6773.70065","DOIUrl":"10.1111/1475-6773.70065","url":null,"abstract":"<div>\u0000 <section>\u0000 <h3> Objective</h3>\u0000 <p>To define measures of Medicare diagnosis coding intensity that capture the dynamics of changes in coding practices.</p>\u0000 </section>\u0000 <section>\u0000 <h3> Study Setting and Design</h3>\u0000 <p>Retrospective analysis of coding for risk adjustment using observational claims data from Medicare beneficiaries.</p>\u0000 </section>\u0000 <section>\u0000 <h3> Data Sources</h3>\u0000 <p>Enrollment and claims data from 2017 and 2018 of a random 20% sample of Medicare beneficiaries were subset to those assigned to an Accountable Care Organization in 2018.</p>\u0000 </section>\u0000 <section>\u0000 <h3> Principal Findings</h3>\u0000 <p>We decompose the prevalence of a diagnosis code into incidence (proportion of beneficiaries that newly have the code) and persistence (proportion of beneficiaries who previously had the code and continue to do so). Together these define steady-state prevalence, the hypothetical long-run prevalence implied by no changes in current rates of incidence and persistence of coding. Steady-state prevalence can help explain why observed prevalence tends to grow over time without continued behavioral change. For example, our measures suggest that the prevalence of the Specified Heart Arrhythmias diagnosis would continue to rise from 18.7% in 2018 to 28.0% without changes in coding practices.</p>\u0000 </section>\u0000 <section>\u0000 <h3> Conclusions</h3>\u0000 <p>Researchers and policymakers can better understand why changes in coding practices can take years to be fully reflected in data and monitor coding behavior by using our proposed measures.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"61 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12857499/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145472503","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":"Care of Patients With Chronic Conditions and Clinician Participation in Accountable Care Organizations","authors":"Alexander O. Everhart,&nbsp;Peter F. Lyu,&nbsp;Jason M. Hockenberry,&nbsp;Karen E. Joynt Maddox,&nbsp;Kenton J. Johnston","doi":"10.1111/1475-6773.70064","DOIUrl":"10.1111/1475-6773.70064","url":null,"abstract":"<div>\u0000 <section>\u0000 <h3> Objective</h3>\u0000 <p>To compare chronic condition specialists to primary care providers (PCPs) on rates of serving as the usual provider of care (UPC, defined as providing the most visits) versus being accountable under “PCP-first” assignment used in accountable care organization (ACO) programs, and to compare risk-based ACO participation.</p>\u0000 </section>\u0000 <section>\u0000 <h3> Study Setting and Design</h3>\u0000 <p>We conducted a retrospective cohort study of PCP versus chronic condition specialty clinicians on their rates of serving as UPC for patients with complex chronic conditions, patient assignment under a “PCP-first” assignment mechanism, and participation in risk-based ACOs. We then estimated linear probability models predicting clinician participation in risk-based ACOs as a function of their rates of serving as the UPC.</p>\u0000 </section>\u0000 <section>\u0000 <h3> Data Sources and Analytic Sample</h3>\u0000 <p>We used 100% traditional fee-for-service Medicare (TM) clinician data and beneficiary claims from 2017 to 2022.</p>\u0000 </section>\u0000 <section>\u0000 <h3> Principal Findings</h3>\u0000 <p>The study included 2,065,755 and 254,918 clinician-years for PCPs and chronic condition specialists (cardiology, endocrinology, nephrology, pulmonology), respectively. Specialists more often served as the UPC than they were accountable under PCP-first assignment algorithms (7.9% UPC vs. 3.3% PCP-first assignment); the opposite was true of PCPs (19.2% vs. 29.8%). Specialists in the top quintile for serving as UPC were 19.0% <i>less</i> likely (4.4 percentage point [pp] absolute difference, 95% CI, 3.7–5.1 pp) to participate in risk-based ACOs than specialists in the bottom quintile. PCPs in the top UPC quintile were 18.7% <i>more</i> likely (3.8 pp. absolute difference, 95% CI, 3.6–4.1 pp) to participate in risk-based ACOs than PCPs in the bottom quintile.</p>\u0000 </section>\u0000 <section>\u0000 <h3> Conclusions</h3>\u0000 <p>Existing assignment mechanisms in Medicare ACOs may undervalue specialists' care for patients with chronic conditions. More efforts are needed to engage specialists in accountable care.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"61 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145454074","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":"Adapting the American Community Survey for the Affordable Care Act","authors":"Joanne Pascale,&nbsp;Angela R. Fertig","doi":"10.1111/1475-6773.70066","DOIUrl":"10.1111/1475-6773.70066","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To measure the accuracy of questions on health insurance premiums and subsidies added to the American Community Survey (ACS) and their utility in categorizing coverage type following the Affordable Care Act (ACA).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Setting and Design</h3>\u0000 \u0000 <p>A reverse record check study where households in Minnesota with individuals enrolled in five different types of coverage—employer-sponsored insurance (ESI), non-group (outside the marketplace), marketplace, Medicaid and MinnesotaCare (a public plan requiring premium contributions from the enrollee)—were administered a telephone survey that included the ACS health insurance module appended with experimental questions on premiums and subsidies.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Analytic Sample</h3>\u0000 \u0000 <p>Enrollment records from a private insurer were used as the sample for primary survey data collection in the spring of 2015 using the ACS health insurance module. Survey data were matched back to enrollment records, which indicated coverage status at the time of the survey. The analytic sample includes matched data on about 600 individuals.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>In total, 100%, 95.3%, and 86.9% of marketplace, non-group, and ESI enrollees, respectively, were correctly reported to have a premium. 74.6% of Medicaid enrollees were correctly reported NOT to have a premium and 77.4% of MinnesotaCare enrollees were correctly reported to HAVE a premium. For the subsidy item, correct reports of no subsidy were 99.1%, 93.8%, and 80.9% for ESI, non-group, and unsubsidized marketplace enrollees, respectively. A total of 72.4% of subsidized marketplace enrollees were correctly reported to have a subsidy. Analysis also indicates that an algorithm leveraging these two new data points can be used to separate the overall “direct purchase” category into two sub-groups: subsidized marketplace and unsubsidized marketplace combined with individual non-group.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Results indicate high levels of reporting accuracy for questions about premiums and subsidies. Thus, this post-ACA module of the ACS is capable of rendering more detailed coverage types than previously possible.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"61 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12857494/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145446627","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":"State Proposed Strategies to Expand Access to Medications for Opioid Use Disorder","authors":"Andrea Baron,&nbsp;Jennifer D. Hall,&nbsp;Jordan Byers,&nbsp;Stephan Lindner,&nbsp;Deborah J. Cohen","doi":"10.1111/1475-6773.70061","DOIUrl":"10.1111/1475-6773.70061","url":null,"abstract":"<div>\u0000 <section>\u0000 <h3> Objective</h3>\u0000 <p>To identify state strategies to increase access to medications for opioid use disorder (MOUD) through Section 1115 Substance Use Disorder waivers.</p>\u0000 </section>\u0000 <section>\u0000 <h3> Study Setting and Design</h3>\u0000 <p>We conducted a qualitative analysis of 27 waiver applications that were implemented between 2015 and 2020. We identified barriers and proposed strategies for expanding MOUD access and utilization.</p>\u0000 </section>\u0000 <section>\u0000 <h3> Data Sources and Analytic Sample</h3>\u0000 <p>After excluding five states due to insufficient information, we analyzed 22 applications.</p>\u0000 </section>\u0000 <section>\u0000 <h3> Principal Findings</h3>\u0000 <p>We identified six barriers and eight corresponding strategies. Barriers included care delays, limited MOUD facilities, lack of care transition support, limited MOUD access in residential treatment, insufficient care coordination, and prescriber shortages. Commonly proposed strategies were requiring access to MOUD in residential treatment, which was stipulated by the Centers for Medicare &amp; Medicaid Services, addressing prescriber shortages through education and technical assistance, campaigns to address stigma, and increased reimbursement. Other strategies included changes to prior authorization requirements, efforts to increase the number of facilities that offer MOUD, and changes to improve care transitions.</p>\u0000 </section>\u0000 <section>\u0000 <h3> Conclusions</h3>\u0000 <p>States proposed a variety of strategies to expand access to and use of MOUD. Future research could investigate how these approaches, implemented individually or in combination, are associated with outcome change and impact.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"61 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145410838","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":"Development and Validation of an Algorithm to Identify Prenatal Care in Administrative Data: Predictive Validity for Adverse Birth Outcomes","authors":"Songyuan Deng,&nbsp;Greg Barabell,&nbsp;Kevin J. Bennett","doi":"10.1111/1475-6773.70063","DOIUrl":"10.1111/1475-6773.70063","url":null,"abstract":"<div>\u0000 <section>\u0000 <h3> Objective</h3>\u0000 <p>To develop and validate a hierarchical algorithm for assigning prenatal care (PNC) encounters using claims data while ensuring continuity of care.</p>\u0000 </section>\u0000 <section>\u0000 <h3> Study Setting and Design</h3>\u0000 <p>We conducted a retrospective cohort study among South Carolina Medicaid beneficiaries. Using a six-step hierarchical algorithm—incorporating specialty designations, diagnostic/procedure codes, and adjustments for inpatient stays and supplemental visits—we assigned PNC encounters and identified predominant PNC providers. To assess predictive validity, we examined associations between predominant provider status and adverse birth outcomes (obtained from linked birth certificates and claims data) using logit-binomial generalized estimating equations with robust standard errors, and we compared models' performance using both model fit statistics and 10-fold cross-validation.</p>\u0000 </section>\u0000 <section>\u0000 <h3> Data Sources and Analytic Sample</h3>\u0000 <p>We used South Carolina Medicaid data on live-birth pregnancies from 2016 to 2021. We followed participants from conception until delivery.</p>\u0000 </section>\u0000 <section>\u0000 <h3> Principal Findings</h3>\u0000 <p>Initial screening identified 302 package/bundle payment claims, leading to the exclusion of 299 pregnancies (0.3%) from further analysis. The final analytic dataset contained 1,072,615 confirmed PNC encounters for 90,581 (97%) pregnancies. This study identified predominant providers for 87,573 pregnancies (98% of cases with at least two PNC encounters). The analysis of predictive validity revealed significant protective associations for two outcomes when comparing pregnancies with versus without predominant providers: preterm birth (adjusted RR: 0.68, 95% CI: 0.59–0.77) and low-birth-weight (adjusted RR: 0.68, 95% CI: 0.57–0.80).</p>\u0000 </section>\u0000 <section>\u0000 <h3> Conclusions</h3>\u0000 <p>This study developed and validated a claims-based algorithm to identify PNC utilization in South Carolina Medicaid data. Predictive validity tests revealed that predominant provider status was associated with reduced adverse birth outcomes, suggesting care continuity may improve perinatal health. Future research could apply this algorithm to examine causal relationships between predominant provider status and specific outcomes (e.g., preterm birth, low birth weight), while accounting for institutional and socioeconomic confounders. These findings offer a foundation for optimizing PNC delivery through continuity-focused interventions.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"61 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12857457/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145395242","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":"Provider Attribution in Medicare: Challenges and Solutions","authors":"Caroline S. Carlin,&nbsp;Roger Feldman,&nbsp;Jeah Jung","doi":"10.1111/1475-6773.70062","DOIUrl":"10.1111/1475-6773.70062","url":null,"abstract":"<div>\u0000 <section>\u0000 <h3> Objective</h3>\u0000 <p>To enhance National Provider Identifier (NPI) and specialty information available in Medicare Advantage (MA) encounter data and use the enhanced data to evaluate methods for retrospective attribution of the patient's usual clinician, comparing results across MA and Traditional Medicare (TM) populations.</p>\u0000 </section>\u0000 <section>\u0000 <h3> Study Setting and Design</h3>\u0000 <p>We fill in missing clinician identifiers and specialty codes in MA encounter data using Centers for Medicare and Medicaid Services (CMS) and publicly available provider datasets. We attributed patients to the usual clinician using 16 methodological options, comparing the performance of these attribution methods in MA and TM.</p>\u0000 </section>\u0000 <section>\u0000 <h3> Data Sources and Analytic Sample</h3>\u0000 <p>We used a 20% sample of MA encounter data and TM claims data for 2016–2022, incorporating information from CMS's Medicare Data on Provider Practice and Specialty, archived data from the National Plan and Provider Enumeration System, and specialty-taxonomy crosswalks derived from CMS publications.</p>\u0000 </section>\u0000 <section>\u0000 <h3> Principal Findings</h3>\u0000 <p>For MA, we identified individual NPIs for 83% of medical claims in 2016, improving to 89% in 2022. Among MA medical claims billed by physicians and advanced practice providers, 95% of NPIs were for individual clinicians by 2022. In total, we identified individual or organization NPIs and specialty codes for over 99% of medical encounters in both TM and MA in all years. Rates of patient attribution were stable over time, and most methods had similar performance in MA and TM. We recommend a hierarchical attribution method that resulted in the highest fraction attributed with good consistency of attributed clinician year over year. Published reference files and SAS code make these NPI identification and patient attribution methods accessible.</p>\u0000 </section>\u0000 <section>\u0000 <h3> Conclusions</h3>\u0000 <p>Our methods allow researchers to identify provider NPIs that can be matched to external clinician data, used to attribute patients to a usual source of care, or to fit clinician fixed effects in studies of MA and TM.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"61 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12857508/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145395199","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":"Hospital Discharge Planning—An Investigation of Outcomes and Interventions","authors":"Lena Imhof,&nbsp;Robin Heber,&nbsp;Kai Svane Blume,&nbsp;Jonas Schreyoegg,&nbsp;Vera Winter","doi":"10.1111/1475-6773.70060","DOIUrl":"10.1111/1475-6773.70060","url":null,"abstract":"<div>\u0000 <section>\u0000 <h3> Objective</h3>\u0000 <p>To provide a comprehensive overview of the different types of hospital discharge planning (DP) interventions and outcomes examined in systematic reviews, and to assess the strength of evidence (SoE) for the associations between DP and these outcomes.</p>\u0000 </section>\u0000 <section>\u0000 <h3> Study Setting and Design</h3>\u0000 <p>Umbrella review (“review of systematic reviews”).</p>\u0000 </section>\u0000 <section>\u0000 <h3> Data Sources</h3>\u0000 <p>We searched five databases (PubMed, CINAHL, Web of Science, Cochrane, and Business Source Complete) from inception through February 2024 for systematic reviews examining associations between hospital DP and various outcomes. We conducted backward and forward citation searches to identify additional systematic reviews. Altogether, these searches yielded 1817 records, of which 34 met the inclusion criteria. We assessed the methodological quality of the included reviews using the AMSTAR 2 tool, summarized DP intervention types and the reviews' subgroup analyses narratively, and evaluated the SoE for 19 outcomes using a recently developed method.</p>\u0000 </section>\u0000 <section>\u0000 <h3> Principal Findings</h3>\u0000 <p>We identified 20 distinct DP intervention types which we grouped into six intervention categories. Patient education was the most frequently investigated type. We rated SoE as high for five outcomes, moderate for eight, and low for six. We found the strongest evidence for associations between hospital DP and reduced readmissions, fewer medication discrepancies, and greater patient satisfaction. Evidence for associations with quality of life, emergency department visits, mortality, and overall patient health, however, was weak or lacking. Our synthesis of the reviews' subgroup analyses indicated that the effects of hospital DP varied across patient populations and intervention types. Overall, the most effective interventions appeared to be high-intensity, bundled programs, incorporating medication-related interventions and follow-ups, particularly for reducing readmissions.</p>\u0000 </section>\u0000 <section>\u0000 <h3> Conclusion</h3>\u0000 <p>This umbrella review synthesizes evidence on associations between hospital DP and various outcomes. The findings support the development of tailored DP strategies and point to research gaps. Future studies should prioritize standardizing intervention definitions, outcome measures, and subgroup classifications, and investigate unexamined causal mechanisms.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"61 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12857470/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145356883","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":"Experiences of Maryland Primary Care Practices in Addressing Social Needs Through a Novel Value-Based Payment","authors":"Emily Gruber,&nbsp;Rachel Grisham,&nbsp;Hannah Arem,&nbsp;Claire M. Starling,&nbsp;Marjanna Smith,&nbsp;Felicia Dortch,&nbsp;Chad Perman","doi":"10.1111/1475-6773.70058","DOIUrl":"10.1111/1475-6773.70058","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To understand perceived successes and challenges of the HEART payment, and opportunities for similar value-based payment mechanisms aiming to address health-related social needs.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Setting and Design</h3>\u0000 \u0000 <p>This study analyzes perceptions of primary care practices participating in the Maryland Primary Care Program (MDPCP) on the HEART payment, a value-based payment designed to support patients' social and medical needs. After a year of payment implementation, we gathered feedback through participant surveys and focus groups.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Analytic Sample</h3>\u0000 \u0000 <p>From February to March 2023, we administered a survey with 112 responses and held seven focus groups to collect primary data. For quantitative survey data, we summarized descriptive statistics and performed regression analyses to determine predictors of perceived value of the HEART payment. For qualitative focus group data, we coded and analyzed data to understand key themes on success factors and barriers to HEART payment implementation.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>The HEART payment was rated as high value for 61.3% of survey respondents. In bivariate regression analysis, the level of funds received and affiliation with a Care Transformation Organization (CTO) were associated with perceived value of the HEART payment; however, these associations were not significant in multivariate models. In focus groups, we found that the biggest perceived success of HEART was its unique ability to enable direct support for patients' health-related social needs, with practices using the payment to provide patients with resources such as transportation, medically necessary home remediations, and support for loneliness. Perceived challenges included the need for more precise patient eligibility targeting and administrative burdens.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The HEART payment is a promising new payment model that enables primary care practices to directly address patients' social needs. Future value-based payment models that incorporate social risk adjustments in provider payments may consider alternate methods to identify patients with a high burden of health-related social needs. This may include adjusting data points used to identify beneficiaries or allowing providers to directly identify patients.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"61 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145349899","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}