Medical Care最新文献

{"title":"Response to Letter to the Editor: Compliance With Recommendations of the Surveillance, Epidemiology, and End Results (SEER) Treatment Data Use Agreement: A Review of Published Studies.","authors":"Yoon Duk Hong, Angela B Mariotto, Denise R Lewis, Anne-Michelle Noone, Nadia Howlader, Steve Scoppa, Eric J Feuer","doi":"10.1097/MLR.0000000000002327","DOIUrl":"10.1097/MLR.0000000000002327","url":null,"abstract":"","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":" ","pages":"471-472"},"PeriodicalIF":1.9,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147675052","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":"Diabetes Diagnosis Patterns in Medicaid: How State Policy, Managed Care, and Social Vulnerability Shape Detection in Medicaid.","authors":"Maria L Alva, Slun Booppasiri, Alexander Crowell, Michelle F Magee, Boon Peng Ng","doi":"10.1097/MLR.0000000000002328","DOIUrl":"10.1097/MLR.0000000000002328","url":null,"abstract":"<p><strong>Objective: </strong>To describe patterns of type 2 diabetes diagnosis prevalence in Medicaid administrative data by individual and contextual factors.</p><p><strong>Methods: </strong>Diagnosed prevalence was estimated using the 2016-2021 Transformed Medicaid Statistical Information System (T-MSIS) and a Bayesian multilevel regression framework. The first level included patient characteristics (age, sex, and race/ethnicity), the Social Vulnerability Index (SVI), plan-level characteristics, payment models, and federal level of poverty (FLP) eligibility thresholds. The second level incorporates partial pooling over the state to model state fixed effects. Adults ages 21-64 years with 24+ months consecutive enrollment were included. States with a high degree of missing data were excluded.</p><p><strong>Results: </strong>Among 4.41 million eligible Medicaid beneficiaries in N=11 states with high-quality T-MSIS data, 10.3% had a type 2 diabetes diagnosis record. State-level diagnosed prevalence based on administrative data ranged from 6.5%-13.0%, and increased with age (4.1% for 21-35 vs. 23.9% for 55-64 y), was higher among \"Other\" race (14.3%), Hispanic (12.0%), and Black (10.9%) enrollees versus White (9.1%). Higher county-level obesity was associated with increased odds (third vs. first tertile OR 1.26, 95% CI: 1.25-1.27), while living in urban areas (OR 0.91, 95% CI: 0.90-0.91) and in the highest SVI quartile (OR 0.74, 95% CI: 0.73-0.75) with lower odds. Race interactions were most pronounced for contextual socioeconomic modifiers (SVI, FPL, and plan type).</p><p><strong>Conclusions: </strong>Evidence generated confirms that odds of having a recorded type 2 diabetes diagnosis are shaped by age, comorbidities, race/ethnicity, and social vulnerability. Notable variations by state and care plans were identified. Documentation and data improvements are needed.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":" ","pages":"419-427"},"PeriodicalIF":1.9,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13162211/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147775653","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":"The Paradoxical Association of Disaggregated Data Collection With Diabetes Control Among Latino Patients.","authors":"John D Heintzman, Dang Dinh, Jorge Kaufmann, JenniferA Lucas, Wyatt P Bensken, Danielle M Crookes, Karen Florez, Sandra Echeverria, Miguel Marino","doi":"10.1097/MLR.0000000000002331","DOIUrl":"10.1097/MLR.0000000000002331","url":null,"abstract":"<p><strong>Background: </strong>Data disaggregation in Latino patients has been called for by national organizations. However, analyses of the association between the collection of this data point, especially the place of birth, and common disease-specific factors, including disease severity, are lacking.</p><p><strong>Objective: </strong>To examine the relationship between the collection of place of birth data and diabetes control in a national network of community health centers (CHCs).</p><p><strong>Participants: </strong>Hispanic/Latino adult patients, aged 18 to 79 years, with diabetes.</p><p><strong>Measures: </strong>We described the following groups by demographic, clinical, and neighborhood factors, and compared odds of hemoglobin a1c (HbA1c) always <7% or ever >9%, stratified by preferred language: (1) patients whose clinics never collected place of birth; (2) those whose clinics did collect this information, but they personally did not have a country of birth in their record; and (3) those with country of birth documented.</p><p><strong>Results: </strong>In our study population (n=81,107), Spanish-preferring Latinos with their place of birth recorded (HbA1c always <7: OR=0.75, 95% CI: 0.65-0.87; HbA1c ever >9: OR=1.68, 95% CI: 1.44-1.95) had worse HbA1c measures than Spanish-preferring Latino patients in clinics that did not collect country of birth.</p><p><strong>Conclusions: </strong>In a national CHC network, Spanish-preferring Latino patients with country of birth information in their records had less well-controlled HbA1cs than those who were served by clinics that did not collect these data. These surprising findings underscore the need to assess disaggregated data collection in Latino patients to better understand the data necessary for high-quality primary care in Latino communities.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":" ","pages":"456-462"},"PeriodicalIF":1.9,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147775714","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":"Physician Recommendations Against Pregnancy Among Young Women With Disabilities in the United States.","authors":"Ilhom Akobirshoev, Robyn Powell, Willi Horner-Johnson, Ruby Siegel, Justine Wu, Monika Mitra","doi":"10.1097/MLR.0000000000002316","DOIUrl":"10.1097/MLR.0000000000002316","url":null,"abstract":"<p><strong>Background: </strong>Women with disabilities face systematic barriers to reproductive autonomy, yet physician bias in pregnancy counseling has not been quantified using nationally representative data.</p><p><strong>Methods: </strong>We analyzed pooled data from 4 waves of the National Survey of Family Growth (2011-2019), including 8018 US women aged 15-25. Disability status was defined using the American Community Survey's 6-item measure. The primary outcome was self-reported physician recommendation against pregnancy. Among women who received such recommendations (n=122), we examined physician-reported reasons using categories: \"dangerous for you,\" \"dangerous for your baby,\" and \"some other reason.\" Poisson regression was used to estimate adjusted prevalence ratios (aPRs).</p><p><strong>Results: </strong>Disabled women comprised 21.2% of the sample and were significantly more likely to report receiving physician recommendations against pregnancy compared with nondisabled women (2.6% vs. 1.1%; aPR:1.91, 95% CI: 1.10-3.29). The disparity was highest among women with physical disabilities (aPR:5.73, 95% CI: 1.79-18.29), followed by those with hearing (aPR: 4.24, 95% CI: 1.49-12.07) and vision disabilities (aPR:3.99, 95% CI: 1.60-9.99). A substantial proportion of recommendations were attributed to \"some other reason\" (disabled women: 44%; nondisabled women: 40%).</p><p><strong>Conclusions: </strong>Young disabled women face systematic bias during reproductive health care counseling, with nearly double the likelihood of receiving recommendations against pregnancy. The high proportion of recommendations under \"some other reasons,\" beyond maternal or fetal health concerns, suggests potential subjective biases may influence clinical recommendations. These findings underscore the need for evidence-based clinical guidelines and monitoring to ensure respectful, individualized, and medically appropriate reproductive health care regardless of disability status.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":" ","pages":"368-375"},"PeriodicalIF":1.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13491452/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147581798","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 Diagnosis: Evaluation of Diagnostic Experiences Classified as Problems, Mistakes, or Both by Patients and Care Partners From a Nationally Representative Survey.","authors":"Kelly T Gleason, Kathryn M McDonald, Rachel Grob, Christina T Yuan, Isha Dhingra, Emily Warne, Mark Schlesinger","doi":"10.1097/MLR.0000000000002302","DOIUrl":"10.1097/MLR.0000000000002302","url":null,"abstract":"<p><strong>Background: </strong>Understanding patients' experiences with diagnosis is crucial for improving care. Clarifying how best to ask about these experiences is an essential step in doing so.</p><p><strong>Objective: </strong>To elicit and then categorize diagnostic problems and mistakes in a national, population-based survey.</p><p><strong>Methods: </strong>Drawing from a nationally representative panel, respondents were asked whether they had experienced (directly or as a care partner) a diagnostic problem or mistake in the last 4 years. They then classified the experience as a mistake, a problem only, or a combination of both. Respondents who experienced multiple problems and mistakes reported on their most memorable experience. We compared responses among those reporting a problem, a mistake, or a combination of both.</p><p><strong>Results: </strong>A third [1216/3684 (33.0%)] of screened households reported diagnostic problems or mistakes involving themselves [697/3684 (18.9%)] or someone close to them [519/3684 (14.1%)]. A plurality [448/3684 (12.2%)] categorized these as a mistake, 382 (10.4%) as a problem, and 371 (10.1%) as both. Experiences reported as problems versus mistakes were equally likely to be associated with harmful consequences and concrete clinician responses. The distribution of problems and mistakes across the 3 categories did not significantly differ when reported by patients versus care partners, though there were checkered differences in the reported impact.</p><p><strong>Conclusions: </strong>Whether labeled as problems or mistakes, diagnostic experiences reported by patients and care partners are accompanied by substantial emotional, physical, and financial impacts. Responding to this full range of patient experiences is important for guiding improvements in diagnostic quality within learning health systems.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":" ","pages":"335-343"},"PeriodicalIF":1.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13014693/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147290387","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":"Comparing Approaches to Identify Mobile Crisis Services in Medicaid Administrative Claims.","authors":"Andrew Anderson, Brigham Walker, Alene Kennedy-Hendricks, Jingwei Maggie Li, Mathew Eisenberg, Karen Shen","doi":"10.1097/MLR.0000000000002317","DOIUrl":"10.1097/MLR.0000000000002317","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Mobile crisis services offer a community-based alternative to law enforcement and emergency departments for individuals experiencing behavioral health crises. Medicaid claims represent a promising but underused tool for national surveillance of these services. Despite growing federal investment and expansion of crisis response systems, there is no standardized method for identifying mobile crisis encounters in administrative claims, limiting the ability to monitor access, evaluate implementation, or compare delivery across states.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;To develop and evaluate claims-based approaches for identifying mobile crisis service delivery in Medicaid administrative data.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We conducted a cross-sectional analysis of 2021 Medicaid claims from the Transformed Medicaid Statistical Information System Analytic Files. We used the 2022 KFF Behavioral Health Services Survey data as the most recent standardized benchmark for Medicaid mobile crisis coverage. We tested 3 claims-based identification approaches that combine national and state-specific procedure codes (eg, H2011) with mobile-relevant place-of-service (POS) codes: strict [POS 15 (mobile unit)], moderate [POS 15 (mobile unit), POS 12 (home), or POS 04 (homeless shelter)], and inclusive [POS 15, POS 12, POS 04, or POS 99 (other)]. We additionally constructed a claims-based score summarizing state-level use of mobile-relevant POS codes and examined its alignment with reported coverage in exploratory analyses.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Among 44 states and the District of Columbia with complete survey data, 33 reported Medicaid coverage of mobile crisis services. We identified 97,088 claims under the strict approach, 395,141 under the moderate approach, and 1,152,653 under the inclusive approach. Across all approaches, states reporting coverage had higher median claim counts than noncoverage states, though substantial overlap remained. Under the strict approach, median claim counts were 0 in both coverage and noncoverage states. The moderate approach demonstrated the greatest separation (median, 1324 vs. 364), while the inclusive approach captured larger volumes overall but with greater overlap between groups. Predicted probabilities derived from a claims-based score increased monotonically across score values, indicating stronger directional alignment with reported coverage at higher score levels.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;A claims-based approach combining mobile unit (POS 15), home (POS 12), and homeless shelter (POS 04) codes with crisis intervention procedure codes provides a practical framework for identifying billed mobile crisis encounters in Medicaid data. This moderate approach showed the strongest directional alignment with state-reported coverage and may support cross-state monitoring of crisis service delivery. Further work is needed to validate these methods using provider-level or encounter-l","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":" ","pages":"376-380"},"PeriodicalIF":1.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147623270","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":"Leveraging Machine Learning to Predict Mental Health Referral Follow-Up Among US Military Personnel.","authors":"Juan Diego Vera, Sarah M Jurick, Amber L Dougherty, Andrew J MacGregor","doi":"10.1097/MLR.0000000000002321","DOIUrl":"10.1097/MLR.0000000000002321","url":null,"abstract":"<p><strong>Background: </strong>Noncompliance with mental health referrals among US military personnel remains a significant barrier to care. Operational deployments and military stressors contribute to mental health challenges, impacting treatment access and increasing costs for the Department of Defense. Identifying service members unlikely to follow through on referrals may enable targeted interventions.</p><p><strong>Objectives: </strong>To develop machine learning (ML) models to predict noncompliance with mental health referrals and identify key predictors among active-duty personnel.</p><p><strong>Research design: </strong>This study utilized retrospective data to create predictive models for referral noncompliance.</p><p><strong>Subjects: </strong>The study sample consisted of 14,289 active-duty personnel who received mental health referrals through the Periodic Health Assessment (PHA) from 2016 to 2020.</p><p><strong>Measures: </strong>Predictors included demographics, health screenings, medical history, and prior health care utilization. Outcome measures focused on noncompliance within 90 days of referral.</p><p><strong>Results: </strong>Noncompliance with referrals occurred in 34.0% of the sample. Among predictive models, extreme gradient boosting (XGBoost) achieved the highest performance (AUC ≈ 0.80), with prior health care utilization (eg, previous clinic visits and mental health diagnoses) identified as the strongest predictor, followed by alcohol screening and age.</p><p><strong>Conclusions: </strong>ML models demonstrated strong potential for identifying at-risk individuals, supporting targeted interventions to improve mental health care follow-up. Future research will emphasize validation and explore mechanisms influencing noncompliance.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":" ","pages":"403-410"},"PeriodicalIF":1.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147513353","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":"Can Patient-Reported Outcome Measures Help Predict Unplanned Hospital Readmission?","authors":"Maggie Yu, Mark Harrison, Hubert Wong, Logan Trenaman, Stirling Bryan, Lisa Lix, Rick Sawatzky, Lena Cuthbertson, Fatima Al Sayah, Nick Bansback","doi":"10.1097/MLR.0000000000002315","DOIUrl":"10.1097/MLR.0000000000002315","url":null,"abstract":"<p><strong>Background: </strong>Administrative data used to predict unplanned hospital readmissions often lack patient-reported symptoms and functional status. Integrating patient-reported outcome measures (PROMs) may improve risk prediction.</p><p><strong>Objectives: </strong>To assess the incremental value of PROMs in predicting unplanned readmissions to inform postdischarge monitoring and ongoing care management.</p><p><strong>Methods: </strong>This population-based retrospective cohort study used linked administrative and PROMs data from British Columbia, Canada. Adults discharged from acute care who provided response to the EQ-5D-5L and Veterans RAND 12-Item Health Survey (VR-12) within 60 days were included. Aggregated Cox proportional hazards models were fitted to estimate unplanned readmission risk across 30-, 180-, and 360-day horizons. The primary prediction horizons were 30 and 180 days. The 360-day horizon was a secondary focus. Model performance was assessed using the concordance statistics and calibration, with subgroup analysis for Ambulatory Care Sensitive Conditions (ACSC).</p><p><strong>Results: </strong>Among 11,177 individuals, observed unplanned readmission rates within 30, 180, and 360 days of discharge were 5.6%, 18.4%, and 25.0%, respectively. Conditional on surviving to weekly landmarks (23-60 days postdischarge), PROMs modestly improved discrimination. For the 180-day horizon following landmarks, the C-index was 0.762 (95% CI, 0.761-0.763) using predictors from administrative data alone, increasing to 0.774 (95% CI, 0.773-0.774) with EQ-5D-5L and 0.782 (95% CI, 0.781-0.783) with VR-12. Similar gains in discrimination were observed at 30-day and 360-day horizons. All models showed adequate calibration. Among patients with ACSCs, including PROMs improved discrimination by 2.4%-3.0%.</p><p><strong>Conclusions: </strong>PROMs added predictive value for unplanned hospital readmissions, particularly among patients with ACSCs.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":" ","pages":"387-395"},"PeriodicalIF":1.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13166071/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147491291","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":"Anesthesia Provider Type and Veteran Satisfaction: The Authors' Reply.","authors":"Kevin N Griffith, Julia Harris, Jeffrey Darna, Richard P Dutton, Hillary J Mull","doi":"10.1097/MLR.0000000000002314","DOIUrl":"10.1097/MLR.0000000000002314","url":null,"abstract":"","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":" ","pages":"411-412"},"PeriodicalIF":1.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147690977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Patient Experience Divide: How Income Relates to Care Quality in the Primary Care Setting.","authors":"Steven C Martino, Kerry A Reynolds, Andrew M Parker, Sarah M Zelazny, Mary Slaughter, Rachel Grob, Marc N Elliott, Ron D Hays","doi":"10.1097/MLR.0000000000002313","DOIUrl":"10.1097/MLR.0000000000002313","url":null,"abstract":"<p><strong>Background: </strong>Income is closely linked to morbidity and mortality in the United States, potentially due in part to differences in patient experience. However, existing studies on income and care experiences are outdated and have other important limitations.</p><p><strong>Methods: </strong>Using data from a recent national sample of adults (N=5016), we conducted a mixed-methods investigation of the relationship between income and primary care experiences. Patient experience was measured using the CAHPS Clinician and Group survey (CG-CAHPS) and its associated Narrative Item Set (NIS). Closed-ended responses were used to create 4 composite measures, for example, access to care, while open-ended NIS responses were coded for positive and negative mentions of 7 aspects of care: access, coordination, communication, office staff, efficiency, thoroughness, and emotional rapport.</p><p><strong>Results: </strong>Contingent on mentioning an aspect of care in their narratives, low-income participants had lower unadjusted odds of making positive mentions of access, coordination, communication, office staff, efficiency, and emotional rapport (all P- values ≤0.02). Conversely, they had higher unadjusted odds of making negative mentions of coordination, communication, efficiency, and thoroughness (all P -values ≤0.006). Patterns were similar after controlling for education and other characteristics. Low-income participants also had scores on all CG-CAHPS composite measures that were 3-5 points lower than scores for higher-income participants (all P- values <0.001).</p><p><strong>Conclusion: </strong>Low-income patients report fewer positive and more negative health care experiences than higher-income patients across multiple aspects of care. These deficits may contribute to their higher morbidity and mortality. Further research is needed to uncover underlying causes and inform policies and practices to ensure high-quality care for all patients.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":" ","pages":"353-360"},"PeriodicalIF":1.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13037676/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147574580","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}