Health Services Research最新文献

{"title":"Using social risks to predict unplanned hospital readmission and emergency care among hospitalized Veterans.","authors":"Portia Y Cornell, Cassandra L Hua, Zachary M Buchalksi, Gina R Chmelka, Alicia J Cohen, Marguerite M Daus, Christopher W Halladay, Alita Harmon, Jennifer W Silva, James L Rudolph","doi":"10.1111/1475-6773.14353","DOIUrl":"10.1111/1475-6773.14353","url":null,"abstract":"<p><strong>Objectives: </strong>(1) To estimate the association of social risk factors with unplanned readmission and emergency care after a hospital stay. (2) To create a social risk scoring index.</p><p><strong>Data sources and setting: </strong>We analyzed administrative data from the Department of Veterans Affairs (VA) Corporate Data Warehouse. Settings were VA medical centers that participated in a national social work staffing program.</p><p><strong>Study design: </strong>We grouped socially relevant diagnoses, screenings, assessments, and procedure codes into nine social risk domains. We used logistic regression to examine the extent to which domains predicted unplanned hospital readmission and emergency department (ED) use in 30 days after hospital discharge. Covariates were age, sex, and medical readmission risk score. We used model estimates to create a percentile score signaling Veterans' health-related social risk.</p><p><strong>Data extraction: </strong>We included 156,690 Veterans' admissions to a VA hospital with discharged to home from 1 October, 2016 to 30 September, 2022.</p><p><strong>Principal findings: </strong>The 30-day rate of unplanned readmission was 0.074 and of ED use was 0.240. After adjustment, the social risks with greatest probability of readmission were food insecurity (adjusted probability = 0.091 [95% confidence interval: 0.082, 0.101]), legal need (0.090 [0.079, 0.102]), and neighborhood deprivation (0.081 [0.081, 0.108]); versus no social risk (0.052). The greatest adjusted probabilities of ED use were among those who had experienced food insecurity (adjusted probability 0.28 [0.26, 0.30]), legal problems (0.28 [0.26, 0.30]), and violence (0.27 [0.25, 0.29]), versus no social risk (0.21). Veterans with social risk scores in the 95th percentile had greater rates of unplanned care than those with 95th percentile Care Assessment Needs score, a clinical prediction tool used in the VA.</p><p><strong>Conclusions: </strong>Veterans with social risks may need specialized interventions and targeted resources after a hospital stay. We propose a scoring method to rate social risk for use in clinical practice and future research.</p>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141556001","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":"Differences across race and ethnicity in the quality of antidepressant medication management","authors":"Alex H. S. Harris PhD, MS,&nbsp;Pingyang Liu PhD, MS,&nbsp;Jessica Y. Breland PhD,&nbsp;Kenneth J. Nieser PhD,&nbsp;Eric M. Schmidt PhD","doi":"10.1111/1475-6773.14347","DOIUrl":"10.1111/1475-6773.14347","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To illustrate the importance of a multidimensional view of disparities in quality of antidepressant medication management (AMM), as well as discriminating “within-facility” disparities from disparities that exist between facilities.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Study Setting</h3>\u0000 \u0000 <p>We used data from the Veterans Health Administration's (VA) Corporate Data Warehouse (CDW) which contains clinical and administrative data from VA facilities nationally.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Design</h3>\u0000 \u0000 <p>CDW data were used to measure five indicators of AMM quality, including the HEDIS Effective Acute-Phase and Effective Continuation-Phase measures. Mixed effects regression models were used to examine differences in quality indicators between racial/ethnic groups, controlling for other demographic and clinical factors. An adaptation of the Kitagawa-Blinder-Oaxaca (KBO) method was used to decompose mean differences in treatment quality between racial and ethnic groups into within- and between-facility effects.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Extraction Methods</h3>\u0000 \u0000 <p>Demographic, clinical, and health service utilization data were extracted for patients in fiscal year 2017 with a diagnosis of depression and a new start of an antidepressant medication.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>The decomposition of the overall differences between White and Black patients on receiving an initial 90-day prescription (46.7% vs. 32.7%), Effective Acute-Phase (79.7% vs. 66.8%), and Effective Continuation-Phase (64.0% vs. 49.6%) HEDIS measures revealed that most of the overall effects were “within-facility,” meaning that Black patients are less likely to meet these measures regardless of where they are treated. Although the overall magnitude of disparities between White and Hispanic patients on these three measures was very similar (46.7% vs. 32.7%; 79.7% vs. 69.2%; 64.0% vs. 53.6%), the differences were more attributable to Hispanic patients being treated in facilities with overall lower performance on these measures.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Discriminating within- and between-facility disparities and taking a multidimensional view of quality are essential to informing efforts to address disparities in AMM quality.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"59 5","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141536027","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":"Real-world impacts from a decade of Quality Enhancement Research Initiative-partnered projects to translate the Diabetes Prevention Program in the Veterans Health Administration","authors":"Laura J. Damschroder MS, MPH,&nbsp;Alison Hamilton PhD, MPH,&nbsp;Melissa M. Farmer PhD,&nbsp;Bevanne Bean-Mayberry MD, MHS,&nbsp;Caroline Richardson MD,&nbsp;Catherine Chanfreau PhD,&nbsp;Rebecca S. Oberman MSW, MPH,&nbsp;Rachel Lesser MPH,&nbsp;Jackie Lewis,&nbsp;Sue D. Raffa PhD,&nbsp;Micheal G. Goldstein MD,&nbsp;Sally Haskell MD,&nbsp;Erin Finley PhD,&nbsp;Tannaz Moin MD, MBA, MSHS","doi":"10.1111/1475-6773.14349","DOIUrl":"10.1111/1475-6773.14349","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Objectives&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;To describe the impacts of four Veterans Health Administration (VA) Quality Enhancement Research Initiative (QUERI) projects implementing an evidence-based lifestyle intervention known as the Diabetes Prevention Program (DPP).&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Data Sources and Study Setting&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;2012–2024 VA administrative and survey data.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Study Design&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;This is a summary of findings and impacts from four effectiveness-implementation projects focused on in-person and/or online DPP across VA sites.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Data Collection/Extraction Methods&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Patient demographics, participation data, and key findings and impacts were summarized across reports from the VA Diabetes-Mellitus Quality Enhancement Research Initiative (QUERI-DM) Diabetes Prevention Program (VA DPP) Trial, QUERI-DM Online DPP Trial, the Enhancing Mental and Physical Health of Women through Engagement and Retention (EMPOWER) QUERI DPP Project, and EMPOWER 2.0 QUERI Program.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Principal Findings&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Between 2012 and 2024, four VA QUERI studies enrolled 963 Veterans in DPP across 16 VA sites. All participants had overweight/obesity with one additional risk factor for type 2 diabetes (i.e., prediabetes, elevated risk score, or history of gestational diabetes) and 56% (&lt;i&gt;N&lt;/i&gt; = 536) were women. In addition to enhancing the reach of and engagement in diabetes prevention services among Veterans, these projects resulted in three key impacts as follows: (1) informing the national redesign of VA MOVE! including recommendations to increase the number of MOVE! sessions and revise guidelines across 150+ VA sites, (2) enhancing the national evidence base to support online DPP delivery options with citations in national care guidelines outside VA, and (3) demonstrating the importance of gender-tailoring of preventive care services by and for women Veterans to enhance engagement in preventive services.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Conclusions&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Over the past decade, the evolution of VA QUERI DPP projects increased the reach of and engagement in diabetes prevention services among Veterans, including women Veterans who have been harder to engage in lifestyle change programs in VA, and resulted in three key impacts informing type 2 diabetes and obesity prevention effo","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"59 S2","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11540559/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141536028","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":"Processing and validation of inpatient Medicare Advantage data for use in hospital outcome measures","authors":"Kelly A. Kyanko MD, MHS,&nbsp;Kashika M. Sahay PhD, MPH,&nbsp;Yongfei Wang MS,&nbsp;Michelle Schreiber MD,&nbsp;Melissa Hager MSN, BSN, RN,&nbsp;Raquel Myers PhD, JD, MPH,&nbsp;Wanda Johnson BS, RN,&nbsp;Jing Zhang MBA, MPhil, MS,&nbsp;Bing-Jie Yen MPH, MA,&nbsp;Lisa G. Suter MD,&nbsp;Elizabeth W. Triche PhD,&nbsp;Shu-Xia Li PhD","doi":"10.1111/1475-6773.14350","DOIUrl":"10.1111/1475-6773.14350","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To determine the feasibility of integrating Medicare Advantage (MA) admissions into the Centers for Medicare &amp; Medicaid Services (CMS) hospital outcome measures through combining Medicare Advantage Organization (MAO) encounter- and hospital-submitted inpatient claims.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Study Setting</h3>\u0000 \u0000 <p>Beneficiary enrollment data and inpatient claims from the Integrated Data Repository for 2018 Medicare discharges.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Design</h3>\u0000 \u0000 <p>We examined timeliness of MA claims, compared diagnosis and procedure codes for admissions with claims submitted both by the hospital and the MAO (overlapping claims), and compared demographic characteristics and principal diagnosis codes for admissions with overlapping claims versus admissions with a single claim.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Collection/Extraction Methods</h3>\u0000 \u0000 <p>We combined hospital- and MAO-submitted claims to capture MA admissions from all hospitals and identified overlapping claims. For admissions with only an MAO-submitted claim, we used provider history data to match the National Provider Identifier on the claim to the CMS Certification Number used for reporting purposes in CMS outcome measures.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>After removing void and duplicate claims, identifying overlapped claims between the hospital- and MAO-submitted datasets, restricting claims to acute care and critical access hospitals, and bundling same admission claims, we identified 5,078,611 MA admissions. Of these, 76.1% were submitted by both the hospital and MAO, 14.2% were submitted only by MAOs, and 9.7% were submitted only by hospitals. Nearly all (96.6%) hospital-submitted claims were submitted within 3 months after a one-year performance period, versus 85.2% of MAO-submitted claims. Among the 3,864,524 admissions with overlapping claims, 98.9% shared the same principal diagnosis code between the two datasets, and 97.5% shared the same first procedure code.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Inpatient MA data are feasible for use in CMS claims-based hospital outcome measures. We recommend prioritizing hospital-submitted over MAO-submitted claims for analyses. Monitoring, data audits, and ongoing policies to improve the quality of MA data are important approaches to address potential missing data and errors.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"59 6","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141499686","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":"Effects of Medicaid managed care on early detection of cancer: Evidence from mandatory Medicaid managed care program in Pennsylvania","authors":"Youngmin Kwon PhD,&nbsp;Eric T. Roberts PhD,&nbsp;Evan S. Cole PhD,&nbsp;Howard B. Degenholtz PhD,&nbsp;Bruce L. Jacobs MD, MPH,&nbsp;Lindsay M. Sabik PhD","doi":"10.1111/1475-6773.14348","DOIUrl":"10.1111/1475-6773.14348","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To examine changes in late- versus early-stage diagnosis of cancer associated with the introduction of mandatory Medicaid managed care (MMC) in Pennsylvania.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Study Setting</h3>\u0000 \u0000 <p>We analyzed data from the Pennsylvania cancer registry (2010–2018) for adult Medicaid beneficiaries aged 21–64 newly diagnosed with a solid tumor. To ascertain Medicaid and managed care status around diagnosis, we linked the cancer registry to statewide hospital-based facility records collected by an independent state agency (Pennsylvania Health Care Cost Containment Council).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Design</h3>\u0000 \u0000 <p>We leveraged a natural experiment arising from county-level variation in mandatory MMC in Pennsylvania. Using a stacked difference-in-differences design, we compared changes in the probability of late-stage cancer diagnosis among those residing in counties that newly transitioned to mandatory managed care to contemporaneous changes among those in counties with mature MMC programs.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Collection/Extraction Methods</h3>\u0000 \u0000 <p>N/A.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>Mandatory MMC was associated with a reduced probability of late-stage cancer diagnosis (−3.9 percentage points; 95% CI: −7.2, −0.5; <i>p</i> = 0.02), particularly for screening-amenable cancers (−5.5 percentage points; 95% CI: −10.4, −0.6; <i>p</i> = 0.03). We found no significant changes in late-stage diagnosis among non-screening amenable cancers.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>In Pennsylvania, the implementation of mandatory MMC for adult Medicaid beneficiaries was associated with earlier stage of diagnosis among newly diagnosed cancer patients with Medicaid, especially those diagnosed with screening-amenable cancers. Considering that over half of the sample was diagnosed with late-stage cancer even after the transition to mandatory MMC, Medicaid programs and managed care organizations should continue to carefully monitor receipt of cancer screening and design strategies to reduce barriers to guideline-concordant screening or diagnostic procedures.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"59 5","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141494238","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' collection and use of data to address social needs and social determinants of health","authors":"Chelsea Richwine PhD,&nbsp;Samantha Meklir MPA","doi":"10.1111/1475-6773.14341","DOIUrl":"10.1111/1475-6773.14341","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To assess differences in hospitals' collection and use of data on patients' health-related social needs (HRSN) by availability of programs or strategies in place to address patients' HRSN and social determinants of health (SDOH) of communities.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources</h3>\u0000 \u0000 <p>The 2021 American Hospital Association Annual Survey and 2022 Information Technology (IT) Supplement.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Design</h3>\u0000 \u0000 <p>This cross-sectional study described hospitals' engagement in screening and the availability of programs or strategies to address nine different HRSN. We assessed differences in screening rates and uses of data collected through screening among hospitals with and without programs or strategies in place to address HRSN or SDOH using Chi-squared tests of independence.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Collection/Extraction Methods</h3>\u0000 \u0000 <p>Analyses were restricted to IT Supplement respondents with complete data for social needs questions asked in the Annual Survey (<i>N</i> = 1997).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>In 2022, hospitals used social needs data collected through screening for various purposes including discharge planning and clinical decision-making at their hospital as well as to refer patients to needed resources and assess community-level needs. Hospitals with a program or strategy in place had higher rates of screening across all domains and higher rates of using of data collected through screening for uses involving exchange or coordination with external entities.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Collection of social needs data may help inform the development of programs or strategies to address HRSN and SDOH, which in turn can enable providers to screen for these needs and use the data in the near term for care delivery and in the long term to address community and population needs.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"59 6","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11622261/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141478016","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":"Implementation, intervention, and downstream costs for implementation of a multidisciplinary complex pain clinic in the Veterans Health Administration","authors":"Sarah I. Daniels PhD,&nbsp;Shayna Cave MS,&nbsp;Todd H. Wagner PhD,&nbsp;Taryn A. Perez MS,&nbsp;Sara N. Edmond PhD,&nbsp;William C. Becker MD,&nbsp;Amanda M. Midboe PhD","doi":"10.1111/1475-6773.14345","DOIUrl":"10.1111/1475-6773.14345","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Objective&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;To determine the budget impact of implementing multidisciplinary complex pain clinics (MCPCs) for Veterans Health Administration (VA) patients living with complex chronic pain and substance use disorder comorbidities who are on risky opioid regimens.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Data Sources and Study Setting&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;We measured implementation costs for three MCPCs over 2 years using micro-costing methods. Intervention and downstream costs were obtained from the VA Managerial Cost Accounting System from 2 years prior to 2 years after opening of MCPCs.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Study Design&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Staff at the three VA sites implementing MCPCs were supported by Implementation Facilitation. The intervention cohort was patients at MCPC sites who received treatment based on their history of chronic pain and risky opioid use. Intervention costs and downstream costs were estimated with a quasi-experimental study design using a propensity score-weighted difference-in-difference approach. The healthcare utilization costs of treated patients were compared with a control group having clinically similar characteristics and undergoing the standard route of care at neighboring VA medical centers. Cancer and hospice patients were excluded.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Data Collection/Extraction Methods&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Activity-based costing data acquired from MCPC sites were used to estimate implementation costs. Intervention and downstream costs were extracted from VA administrative data.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Principal Findings&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Average Implementation Facilitation costs ranged from $380 to $640 per month for each site. Upon opening of three MCPCs, average intervention costs per patient were significantly higher than the control group at two intervention sites. Downstream costs were significantly higher at only one of three intervention sites. Site-level differences were due to variation in inpatient costs, with some confounding likely due to the COVID-19 pandemic. This evidence suggests that necessary start-up investments are required to initiate MCPCs, with allocations of funds needed for implementation, intervention, and downstream costs.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Conclusions&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Incorporating implementation, intervention, and downstream costs in this evaluation provides a thorough budget impact an","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"59 S2","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11540574/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141494239","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":"Improving access to buprenorphine for rural veterans in a learning health care system","authors":"Jessica J. Wyse PhD,&nbsp;Katherine Mackey MD,&nbsp;Kim A. Kauzlarich PharmD,&nbsp;Benjamin J. Morasco PhD,&nbsp;Kathleen F. Carlson PhD,&nbsp;Adam J. Gordon MD,&nbsp;P. Todd Korthuis MD,&nbsp;Alison Eckhardt MA,&nbsp;Summer Newell PhD,&nbsp;Sarah S. Ono PhD,&nbsp;Travis I. Lovejoy PhD","doi":"10.1111/1475-6773.14346","DOIUrl":"10.1111/1475-6773.14346","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To describe a learning health care system research process designed to increase buprenorphine prescribing for the treatment of opioid use disorder (OUD) in rural primary care settings within U.S. Department of Veterans Affairs (VA) treatment facilities.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Study Setting</h3>\u0000 \u0000 <p>Using national administrative data from the VA Corporate Data Warehouse, we identified six rural VA health care systems that had improved their rate of buprenorphine prescribing within primary care from 2015 to 2020 (positive deviants). We conducted qualitative interviews with leaders, clinicians, and staff involved in buprenorphine prescribing within primary care from these sites to inform the design of an implementation strategy.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Design</h3>\u0000 \u0000 <p>Qualitative interviews to inform implementation strategy development.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Collection/Extraction Methods</h3>\u0000 \u0000 <p>Interviews were audio-recorded, transcribed verbatim, and coded by a primary coder and secondary reviewer. Analysis utilized a mixed inductive/deductive approach. To develop an implementation strategy, we matched clinical needs identified within interviews with resources and strategies participants had utilized to address these needs in their own sites.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>Interview participants (<i>n</i> = 30) identified key clinical needs and strategies for implementing buprenorphine in rural, primary care settings. Common suggestions included the need for clinical mentorship or a consult service, buprenorphine training, and educational resources. Building upon interview findings and in partnership with a clinical team, we developed an implementation strategy composed of an engaging case-based training, an audit and feedback process, and educational resources (e.g., Buprenorphine Frequently Asked Questions, Rural Care Model Infographic).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>We describe a learning health care system research process that leveraged national administrative data, health care provider interviews, and clinical partnership to develop an implementation strategy to encourage buprenorphine prescribing in rural primary care settings.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"59 S2","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11540581/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141494332","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":"Skilled nursing facility staffing shortages: Sources, strategies, and impacts on staff who stayed","authors":"Natalie E. Leland PhD, OTR/L,&nbsp;Rachel A. Prusynski DPT, PhD,&nbsp;Amanda D. Shore COTA/L,&nbsp;Michael P. Cary Jr. PhD, RN,&nbsp;Jason Falvey DPT, PhD,&nbsp;Tracy Mroz PhD, OTR/L,&nbsp;Debra Saliba MD, MPH","doi":"10.1111/1475-6773.14355","DOIUrl":"10.1111/1475-6773.14355","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To examine skilled nursing facility (SNF) staffing shortages across job roles during the COVID-19 pandemic. We aimed to capture the perspectives of leaders on the breadth of staffing shortages and their implications on staff that stayed throughout the pandemic in order to provide recommendations for policies and practices used to strengthen the SNF workforce moving forward.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Setting and Design</h3>\u0000 \u0000 <p>For this qualitative study, we engaged a purposive national sample of SNF leaders (<i>n</i> = 94) in one-on-one interviews between January 2021 and December 2022.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Source and Analytic Sample</h3>\u0000 \u0000 <p>Using purposive sampling (i.e., Centers for Medicare &amp; Medicaid quality rating, region, ownership) to capture variation in SNF organizations, we conducted in-depth, semi-structured qualitative interviews, guided a priori by the Institute of Medicine's Model of Healthcare System Framework. Interviews were conducted via phone, audio-recorded, and transcribed. Rigorous rapid qualitative analysis was used to identify emergent themes, patterns, and relationships.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>SNF leaders consistently described staffing shortages spanning all job roles, including direct care (e.g., activities, nursing, social services), support services (e.g., laundry, food, environmental services), administrative staff, and leadership. Ascribed sources of shortages were multidimensional (e.g., competing salaries, family caregiving needs, burnout). The impact of shortages was felt by all staff that stayed. In addition to existing job duties, those remaining staff experienced re-distribution of essential day-to-day operational tasks (e.g., laundry) and allocation of new COVID-19 pandemic-related activities (e.g., screening). Cross-training was used to cover a wide range of job duties, including patient care.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Policies are needed to support SNF staff across roles beyond direct care staff. These policies must address the system-wide drivers perpetuating staffing shortages (i.e., pay differentials, burnout) and leverage strategies (i.e., cross-training, job role flexibility) that emerged from the pandemic to ensure a sustainable SNF workforce that can meet patient needs.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"59 6","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11622260/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141460829","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":"Medicaid coverage for gender-affirming surgery: A state-by-state review","authors":"Jonnby S. LaGuardia MD,&nbsp;Madeline G. Chin MD,&nbsp;Sarah Fadich PA-C, MCHS,&nbsp;Katarina B. J. Morgan DDS, MPH2,&nbsp;Halena H. Ngo BA,&nbsp;Meiwand Bedar MD, MSc,&nbsp;Shahrzad Moghadam MD,&nbsp;Kelly X. Huang BS,&nbsp;Christy Mallory JD,&nbsp;Justine C. Lee MD, PhD, FACS","doi":"10.1111/1475-6773.14338","DOIUrl":"10.1111/1475-6773.14338","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To systematically review Medicaid policies state-by-state for gender-affirming surgery coverage.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Study Setting</h3>\u0000 \u0000 <p>Primary data were collected for each US state utilizing the LexisNexis legal database, state legislature publications, and Medicaid manuals.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Design</h3>\u0000 \u0000 <p>A cross-sectional study evaluating Medicaid coverage for numerous gender-affirming surgeries.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Collection/Extraction Methods</h3>\u0000 \u0000 <p>We previously reported on state health policies that protect gender-affirming care under Medicaid coverage. Building upon our prior work, we systematically assessed the 27 states with protective policies to determine coverage for each type of gender-affirming surgery. We analyzed Medicaid coverage for gender-affirming surgeries in four domains: chest, genital, craniofacial and neck reconstruction, and miscellaneous procedures. Medicaid coverage for each type of surgery was categorized as explicitly covered, explicitly noncovered, or not described.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>Among the 27 states with protective Medicaid policies, 17 states (63.0%) provided explicit coverage for at least one gender-affirming chest procedure and at least one gender-affirming genital procedure, while only eight states (29.6%) provided explicit coverage for at least one craniofacial and neck procedure (<i>p</i> = 0.04). Coverage for specific surgical procedures within these three anatomical domains varied. The most common explicitly covered procedures were breast reduction/mastectomy and hysterectomy (<i>n</i> = 17, 63.0%). The most common explicitly noncovered surgery was reversal surgery (<i>n</i> = 12, 44.4%). Several states did not describe the specific surgical procedures covered; thus, final coverage rates are indeterminate.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>In 2022, 52.9% of states had health policies that protected gender-affirming care under Medicaid; however, coverage for various gender-affirming surgical procedures remains both variable and occasionally unspecified. When specified, craniofacial and neck reconstruction is the least covered anatomical area compared with chest and genital reconstruction.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"59 6","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11622269/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141332462","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}