Sarah H. Gordon, Amelia Whitman, Thomas Buchmueller, Benjamin D. Sommers
Objective
To identify the Medicaid eligibility category at delivery and 6 months prior among those with Medicaid and Children's Health Insurance Program (CHIP)-financed births.
Study Setting and Design
Descriptive analysis of 2018 national Medicaid claims data.
Data Sources and Analytic Sample
We used the 2018 Transformed Medicaid Statistical Information System Analytic Files to assess Medicaid/CHIP eligibility category at the time of birth and 6 months prior during pregnancy among enrollees with Medicaid/CHIP-paid births in 2018, stratifying by age, race/ethnicity, and state.
Principal Findings
Just over half (56.2%) of those enrolled in Medicaid/CHIP in 2018 were enrolled in the pregnancy eligibility category at delivery, while 29.5% were enrolled as parents, 8.2% as low-income adults, and 6.1% in other categories. The proportion of pregnant women enrolled via the pregnancy eligibility category varied widely by state, from 11.9% in Kentucky to 97.5% in Texas.
Conclusions
Nearly half of pregnant Medicaid/CHIP enrollees were not enrolled via pregnancy Medicaid eligibility when they delivered. It is important for states to be aware of pregnancy status to apply correct eligibility criteria and benefits for pregnant and postpartum enrollees, including the 12 months of extended postpartum coverage newly available and elected in nearly all states.
{"title":"Medicaid Eligibility Category Among Enrollees With Medicaid-Paid Births in 2018","authors":"Sarah H. Gordon, Amelia Whitman, Thomas Buchmueller, Benjamin D. Sommers","doi":"10.1111/1475-6773.70053","DOIUrl":"10.1111/1475-6773.70053","url":null,"abstract":"<div>\u0000 <section>\u0000 <h3> Objective</h3>\u0000 <p>To identify the Medicaid eligibility category at delivery and 6 months prior among those with Medicaid and Children's Health Insurance Program (CHIP)-financed births.</p>\u0000 </section>\u0000 <section>\u0000 <h3> Study Setting and Design</h3>\u0000 <p>Descriptive analysis of 2018 national Medicaid claims data.</p>\u0000 </section>\u0000 <section>\u0000 <h3> Data Sources and Analytic Sample</h3>\u0000 <p>We used the 2018 Transformed Medicaid Statistical Information System Analytic Files to assess Medicaid/CHIP eligibility category at the time of birth and 6 months prior during pregnancy among enrollees with Medicaid/CHIP-paid births in 2018, stratifying by age, race/ethnicity, and state.</p>\u0000 </section>\u0000 <section>\u0000 <h3> Principal Findings</h3>\u0000 <p>Just over half (56.2%) of those enrolled in Medicaid/CHIP in 2018 were enrolled in the pregnancy eligibility category at delivery, while 29.5% were enrolled as parents, 8.2% as low-income adults, and 6.1% in other categories. The proportion of pregnant women enrolled via the pregnancy eligibility category varied widely by state, from 11.9% in Kentucky to 97.5% in Texas.</p>\u0000 </section>\u0000 <section>\u0000 <h3> Conclusions</h3>\u0000 <p>Nearly half of pregnant Medicaid/CHIP enrollees were not enrolled via pregnancy Medicaid eligibility when they delivered. It is important for states to be aware of pregnancy status to apply correct eligibility criteria and benefits for pregnant and postpartum enrollees, including the 12 months of extended postpartum coverage newly available and elected in nearly all states.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"61 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145460644","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}
Alexander O. Everhart, Peter F. Lyu, Jason M. Hockenberry, Karen E. Joynt Maddox, Kenton J. Johnston
Objective
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.
Study Setting and Design
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.
Data Sources and Analytic Sample
We used 100% traditional fee-for-service Medicare (TM) clinician data and beneficiary claims from 2017 to 2022.
Principal Findings
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% less 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% more 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.
Conclusions
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.
目的：比较慢性病专家和初级保健提供者（pcp）作为常规护理提供者（UPC，定义为提供最多的访问量）的比率与在负责任的护理组织（ACO）计划中使用的“pcp优先”分配下负责的比率，并比较基于风险的ACO参与。研究设置和设计：我们对PCP和慢性病专科临床医生进行了一项回顾性队列研究，比较了他们为复杂慢性病患者担任UPC的比率、“PCP优先”分配机制下的患者分配以及参与基于风险的ACOs。然后，我们估计了线性概率模型，预测临床医生参与基于风险的ACOs，作为其作为UPC的比率的函数。数据来源和分析样本：我们使用2017年至2022年100%的传统按服务收费的医疗保险（TM）临床医生数据和受益人索赔。主要发现：该研究包括pcp和慢性病专家（心脏病学、内分泌学、肾脏病学、肺病学）分别2,065,755和254,918临床年。专家更多地担任UPC，而不是在pcp优先分配算法下负责（7.9%的UPC vs. 3.3%的pcp优先分配）；pcp则相反（19.2% vs 29.8%）。作为UPC的前五分之一的专家参与基于风险的ACOs的可能性比后五分之一的专家低19.0%（4.4个百分点[pp]绝对差异，95% CI, 3.7-5.1 pp）。在UPC前五分之一的pcp有18.7%的可能性（3.8个）。绝对差异，95% CI, 3.6-4.1 pp)参加基于风险的ACOs的比例高于最低五分之一的pcp。结论：现有的医疗保险ACOs分配机制可能低估了专家对慢性病患者的护理。需要作出更多努力，让专家参与负责任的护理。
{"title":"Care of Patients With Chronic Conditions and Clinician Participation in Accountable Care Organizations","authors":"Alexander O. Everhart, Peter F. Lyu, Jason M. Hockenberry, Karen E. Joynt Maddox, 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}
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).
Study Setting and Design
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.
Data Sources and Analytic Sample
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.
Principal Findings
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.
Conclusions
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.
{"title":"Adapting the American Community Survey for the Affordable Care Act","authors":"Joanne Pascale, 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}
Andrea Baron, Jennifer D. Hall, Jordan Byers, Stephan Lindner, Deborah J. Cohen
Objective
To identify state strategies to increase access to medications for opioid use disorder (MOUD) through Section 1115 Substance Use Disorder waivers.
Study Setting and Design
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.
Data Sources and Analytic Sample
After excluding five states due to insufficient information, we analyzed 22 applications.
Principal Findings
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 & 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.
Conclusions
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.
{"title":"State Proposed Strategies to Expand Access to Medications for Opioid Use Disorder","authors":"Andrea Baron, Jennifer D. Hall, Jordan Byers, Stephan Lindner, 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 & 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}
To develop and validate a hierarchical algorithm for assigning prenatal care (PNC) encounters using claims data while ensuring continuity of care.
Study Setting and Design
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.
Data Sources and Analytic Sample
We used South Carolina Medicaid data on live-birth pregnancies from 2016 to 2021. We followed participants from conception until delivery.
Principal Findings
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).
Conclusions
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.
{"title":"Development and Validation of an Algorithm to Identify Prenatal Care in Administrative Data: Predictive Validity for Adverse Birth Outcomes","authors":"Songyuan Deng, Greg Barabell, 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}
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.
Study Setting and Design
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.
Data Sources and Analytic Sample
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.
Principal Findings
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.
Conclusions
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.
{"title":"Provider Attribution in Medicare: Challenges and Solutions","authors":"Caroline S. Carlin, Roger Feldman, 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}
Lena Imhof, Robin Heber, Kai Svane Blume, Jonas Schreyoegg, Vera Winter
Objective
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.
Study Setting and Design
Umbrella review (“review of systematic reviews”).
Data Sources
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.
Principal Findings
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.
Conclusion
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.
目的：全面概述不同类型的出院计划（DP）干预措施和系统综述中检查的结果，并评估DP与这些结果之间关联的证据强度（SoE）。研究设置和设计：总括性评价（“系统评价的评价”）。数据来源：我们检索了五个数据库（PubMed， CINAHL, Web of Science， Cochrane和Business Source Complete），从成立到2024年2月，对医院DP与各种结果之间的关系进行了系统评价。我们进行了反向和正向引文检索，以确定额外的系统评价。这些检索共产生1817条记录，其中34条符合纳入标准。我们使用AMSTAR 2工具评估纳入的综述的方学质量，总结DP干预类型和综述的亚组分析，并使用最新开发的方法评估19个结果的SoE。主要发现：我们确定了20种不同的DP干预类型，并将其分为6个干预类别。患者教育是最常被调查的类型。我们在5个结果中将SoE评为高，8个结果为中等，6个结果为低。我们发现医院DP与减少再入院、减少用药差异和提高患者满意度之间存在最有力的关联。然而，与生活质量、急诊科就诊、死亡率和患者整体健康相关的证据很弱或缺乏。我们综合综述的亚组分析表明，医院DP的效果因患者群体和干预类型而异。总体而言，最有效的干预措施似乎是高强度的捆绑方案，结合药物相关干预和随访，特别是减少再入院。结论：本综述综合了医院DP与各种预后之间的关联证据。研究结果支持量身定制的DP策略的发展，并指出了研究差距。未来的研究应优先考虑标准化干预定义、结果测量和亚组分类，并调查未经检验的因果机制。
{"title":"Hospital Discharge Planning—An Investigation of Outcomes and Interventions","authors":"Lena Imhof, Robin Heber, Kai Svane Blume, Jonas Schreyoegg, 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}
Emily Gruber, Rachel Grisham, Hannah Arem, Claire M. Starling, Marjanna Smith, Felicia Dortch, Chad Perman
Objective
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.
Study Setting and Design
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.
Data Sources and Analytic Sample
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.
Principal Findings
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.
Conclusions
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.
{"title":"Experiences of Maryland Primary Care Practices in Addressing Social Needs Through a Novel Value-Based Payment","authors":"Emily Gruber, Rachel Grisham, Hannah Arem, Claire M. Starling, Marjanna Smith, Felicia Dortch, 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}
Allison Dorneo, Yi-Jung Shen, Aigerim Kabdiyeva, Daniel Asfaw, Melissa M. Garrido, Steven D. Pizer, Jacob Rachlin, Hillary J. Mull
Objective
To investigate the relationship between community care (CC) treatment, surgical complexity, and postoperative surgical outcomes in spine surgeries among Veterans.
Data Sources and Study Setting
Veterans Health Administration (VHA) sample with data sourced from the Corporate Data Warehouse and CC claims.
Study Design
To evaluate differences in VHA and CC spine surgical complexity and outcomes, we first characterized VHA patients with lumbar spinal stenosis (LSS) who received spine surgery in the VHA or CC. Then, we estimated adjusted naïve logistic regression models to calculate the effect of CC on the probability of having a complex spine surgery, 30-day readmission, 30-day complication, and 1-year reoperation. Finally, we estimated adjusted 2-stage models using an instrument for primary care provider's historical CC referral rates and imaging rates as a semi-parametric Newey correction for sample selection.
Analytic Sample
LSS-diagnosed patients living ≤ 80 miles from a VHA facility that performed at least one spine surgery between January 1, 2019 and December 31, 2022.
Principal Findings
Of the 41,726 LSS-diagnosed patients, 7496 (18.0%) had spine surgery within 1 year of diagnosis. 2920 (39.0%) were VHA surgeries and 4576 (61.0%) were in CC. In the naïve model, CC surgery was associated with a 26.61 percentage point (pp) increase in the probability of having a complex surgery (95% CI 24.17, 29.05), a 4.31 pp increase in readmission (95% CI 2.76, 5.85), and a 6.80 pp increase in reoperation (95% CI 5.21, 8.40). After accounting for characteristics associated with the likelihood of surgery, CC, and outcomes, only the effect of CC use on the probability of a complex surgery was significant (36.48; 95% CI 22.69, 50.27).
Conclusions
We found no difference in surgical outcomes between VHA and CC patients. Since CC patients were more likely to receive complex spine surgeries, the VHA paid for more costly, resource-intensive procedures with no improvements in quality.
目的：探讨退伍军人脊柱手术中社区护理（CC）治疗、手术复杂性与术后手术效果的关系。数据来源和研究设置：退伍军人健康管理局（VHA）样本，数据来自公司数据仓库和CC索赔。研究设计：为了评估VHA和CC脊柱手术复杂性和结局的差异，我们首先对在VHA或CC中接受脊柱手术的VHA合并腰椎管狭窄（LSS）患者进行了特征分析，然后，我们估计了调整后的naïve逻辑回归模型，以计算CC对复杂脊柱手术、30天再入院、30天并发症和1年再手术概率的影响。最后，我们使用初级保健提供者的历史CC转诊率和成像率作为样本选择的半参数Newey校正的仪器来估计调整后的两阶段模型。分析样本：lss诊断的患者居住在距离2019年1月1日至2022年12月31日期间至少进行过一次脊柱手术的VHA设施≤80英里的地方。主要发现：在41726例lss诊断患者中，7496例（18.0%）在诊断1年内进行了脊柱手术。2920例（39.0%）为VHA手术，4576例（61.0%）为CC手术。在naïve模型中，CC手术与发生复杂手术的概率增加26.61个百分点（95% CI 24.17, 29.05），再入院增加4.31个百分点（95% CI 2.76, 5.85），再手术增加6.80个百分点（95% CI 5.21, 8.40）相关。在考虑了与手术可能性、CC和结局相关的特征后，只有CC使用对复杂手术可能性的影响是显著的（36.48;95% CI 22.69, 50.27）。结论：我们发现VHA和CC患者的手术结果没有差异。由于CC患者更有可能接受复杂的脊柱手术，VHA支付了更昂贵、资源密集的手术，但质量没有提高。
{"title":"The Impact of Community Care on Spine Surgical Complexity and Outcomes in the Veterans Health Administration","authors":"Allison Dorneo, Yi-Jung Shen, Aigerim Kabdiyeva, Daniel Asfaw, Melissa M. Garrido, Steven D. Pizer, Jacob Rachlin, Hillary J. Mull","doi":"10.1111/1475-6773.70057","DOIUrl":"10.1111/1475-6773.70057","url":null,"abstract":"<div>\u0000 <section>\u0000 <h3> Objective</h3>\u0000 <p>To investigate the relationship between community care (CC) treatment, surgical complexity, and postoperative surgical outcomes in spine surgeries among Veterans.</p>\u0000 </section>\u0000 <section>\u0000 <h3> Data Sources and Study Setting</h3>\u0000 <p>Veterans Health Administration (VHA) sample with data sourced from the Corporate Data Warehouse and CC claims.</p>\u0000 </section>\u0000 <section>\u0000 <h3> Study Design</h3>\u0000 <p>To evaluate differences in VHA and CC spine surgical complexity and outcomes, we first characterized VHA patients with lumbar spinal stenosis (LSS) who received spine surgery in the VHA or CC. Then, we estimated adjusted naïve logistic regression models to calculate the effect of CC on the probability of having a complex spine surgery, 30-day readmission, 30-day complication, and 1-year reoperation. Finally, we estimated adjusted 2-stage models using an instrument for primary care provider's historical CC referral rates and imaging rates as a semi-parametric Newey correction for sample selection.</p>\u0000 </section>\u0000 <section>\u0000 <h3> Analytic Sample</h3>\u0000 <p>LSS-diagnosed patients living ≤ 80 miles from a VHA facility that performed at least one spine surgery between January 1, 2019 and December 31, 2022.</p>\u0000 </section>\u0000 <section>\u0000 <h3> Principal Findings</h3>\u0000 <p>Of the 41,726 LSS-diagnosed patients, 7496 (18.0%) had spine surgery within 1 year of diagnosis. 2920 (39.0%) were VHA surgeries and 4576 (61.0%) were in CC. In the naïve model, CC surgery was associated with a 26.61 percentage point (pp) increase in the probability of having a complex surgery (95% CI 24.17, 29.05), a 4.31 pp increase in readmission (95% CI 2.76, 5.85), and a 6.80 pp increase in reoperation (95% CI 5.21, 8.40). After accounting for characteristics associated with the likelihood of surgery, CC, and outcomes, only the effect of CC use on the probability of a complex surgery was significant (36.48; 95% CI 22.69, 50.27).</p>\u0000 </section>\u0000 <section>\u0000 <h3> Conclusions</h3>\u0000 <p>We found no difference in surgical outcomes between VHA and CC patients. Since CC patients were more likely to receive complex spine surgeries, the VHA paid for more costly, resource-intensive procedures with no improvements in quality.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"61 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145310058","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}
Projesh P. Ghosh, Wafa W. Tarazi, Nwanneamaka Ume, Emily E. Ferrara, Paul Hogan, Emily D. Parker
Objective
To assess the impact of pandemic-related changes in state scope of practice law (SOPL) for certified registered nurse anesthetists (CRNAs) on the utilization of anesthesia services during the COVID-19 pandemic, which served as a natural experiment.
Study Setting and Design
We used a difference-in-differences approach to compare changes in the utilization of anesthesia services during the COVID-19 pandemic in areas that expanded SOPL (removed requirements for supervision or direction) to those that did not. Additionally, we examined if the impact of changes in SOPL on patient access differed by underserved status.
Data Sources and Analytic Sample
To understand patient access to anesthesia care, we used a large, national administrative claims database of privately insured and Medicare Advantage enrollees to measure utilization rates per 1000 members from 2018 through 2022. We used the county-level density of anesthesia providers to identify underserved areas. We used data on the changes in SOPL at the state level and assessed changes in utilization prior to and during COVID-19.
Principal Findings
In the areas that changed SOPL, removing requirements for supervision or direction, utilization of anesthesia procedures increased by 18 procedures per 1000 members over the study period (17% increase; p-value 0.066) compared with an increase of 9 procedures per 1000 members (7% increase; p-value 0.031) in areas that maintained SOPL requiring supervision. However, increases in utilization in underserved and not underserved areas were similar across SOPL statuses.
Conclusions
This study provides evidence that the SOPL that allows CRNAs to practice without the requirement of supervision or direction results in greater access to anesthesia services compared with a more restrictive SOPL requiring supervision.
{"title":"Impact of Scope of Practice Laws for Certified Registered Nurse Anesthetists on the Utilization of Anesthesia Services","authors":"Projesh P. Ghosh, Wafa W. Tarazi, Nwanneamaka Ume, Emily E. Ferrara, Paul Hogan, Emily D. Parker","doi":"10.1111/1475-6773.70052","DOIUrl":"10.1111/1475-6773.70052","url":null,"abstract":"<div>\u0000 <section>\u0000 <h3> Objective</h3>\u0000 <p>To assess the impact of pandemic-related changes in state scope of practice law (SOPL) for certified registered nurse anesthetists (CRNAs) on the utilization of anesthesia services during the COVID-19 pandemic, which served as a natural experiment.</p>\u0000 </section>\u0000 <section>\u0000 <h3> Study Setting and Design</h3>\u0000 <p>We used a difference-in-differences approach to compare changes in the utilization of anesthesia services during the COVID-19 pandemic in areas that expanded SOPL (removed requirements for supervision or direction) to those that did not. Additionally, we examined if the impact of changes in SOPL on patient access differed by underserved status.</p>\u0000 </section>\u0000 <section>\u0000 <h3> Data Sources and Analytic Sample</h3>\u0000 <p>To understand patient access to anesthesia care, we used a large, national administrative claims database of privately insured and Medicare Advantage enrollees to measure utilization rates per 1000 members from 2018 through 2022. We used the county-level density of anesthesia providers to identify underserved areas. We used data on the changes in SOPL at the state level and assessed changes in utilization prior to and during COVID-19.</p>\u0000 </section>\u0000 <section>\u0000 <h3> Principal Findings</h3>\u0000 <p>In the areas that changed SOPL, removing requirements for supervision or direction, utilization of anesthesia procedures increased by 18 procedures per 1000 members over the study period (17% increase; <i>p</i>-value 0.066) compared with an increase of 9 procedures per 1000 members (7% increase; <i>p</i>-value 0.031) in areas that maintained SOPL requiring supervision. However, increases in utilization in underserved and not underserved areas were similar across SOPL statuses.</p>\u0000 </section>\u0000 <section>\u0000 <h3> Conclusions</h3>\u0000 <p>This study provides evidence that the SOPL that allows CRNAs to practice without the requirement of supervision or direction results in greater access to anesthesia services compared with a more restrictive SOPL requiring supervision.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"61 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12857518/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145294344","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}
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