Canadian Medical Association Journal

ImportancePrevention-focused health policy requires analytic frameworks capable of detecting changes in population health and associated costs within policy-relevant time horizons, particularly in managed care systems where premiums reflect actuarial risk rather than realized medical expenditures. ObjectiveTo evaluate a healthstate-based analytic framework (CareMaps) for measuring population health dynamics, disease progression, and associated costs using longitudinal Medicaid managed care claims data. Design, Setting, and ParticipantsRetrospective longitudinal analysis of deidentified Medicaid managed care claims in New Mexico from 2011 through 2014. The study included individuals aged 0 to 64 years enrolled in managed care plans. ExposuresChronic disease burden categorized into mutually exclusive, ordered healthstates based on the number of chronic conditions. Main Outcomes and MeasuresCounty- and managed care organization (MCO) level prevalence of healthstates, transition rates between healthstates, and healthstate-specific cost estimates derived from capitation premiums and medical loss ratio defined medical expenditures. ResultsThe CareMaps framework identified specific geographic and MCO level variation in chronic disease prevalence, healthstate transition rates, and per-member spending patterns that were not fully explained by actuarial risk adjustment. Transitions from nonchronic to chronic healthstates varied markedly across counties, indicating heterogeneity in disease progression and prevention related outcomes. Conclusions and RelevanceA healthstate based analytic framework applied to longitudinal Medicaid managed care data enables standardized measurement of population health dynamics and associated costs within policy relevant time horizons. Such approaches may support evaluation of preventive care performance, inform risk adjustment, and enhance public-sector oversight of managed care programs.

Importance: Sickle cell disease (SCD) affects approximately 100,000 people in the United States, causes life-threatening complications, and shortens life expectancy by decades. Adults with SCD routinely encounter undertreated pain, provider bias, and structural barriers in hospital settings. Objective: To describe patterns of leave against medical advice (LAMA) among adults hospitalized for SCD. Design, Setting, and Participants: Retrospective analysis of inpatient discharge records among adults ages 18 and older in New York City hospitals, 2022-2023, hospitalized for SCD or any reason. Main Outcomes and Measures: The primary outcome was hospital-level LAMA, measured by crude rates and rates adjusting for patient characteristics using Bayesian hierarchical models. The secondary outcome was 30-day all-cause readmissions, stratified by LAMA status. Results: LAMA discharges comprised 14% of SCD hospitalizations and 4% of all-cause hospitalizations. Adjusted hospital-level SCD LAMA ranged from under 5% to 30% (IQR: 10-20%) and was higher than all-cause LAMA in most facilities. Crude SCD LAMA rates exceeded 30% in several hospitals, including those with more than 100 SCD hospitalizations during the study period. Patients with 10 or more SCD hospitalizations accounted for 40% of total SCD volume. Sensitivity analyses accounting for this concentration showed attenuated but persistent variation in SCD LAMA. Over 50% of SCD LAMA discharges were followed by a 30-day readmission compared to 38% of non-LAMA discharges. LAMA was associated with higher adjusted odds of readmissions in both SCD and all-cause hospitalizations. Conclusions: Our findings challenge the assumption that patients are solely responsible for early departures. Leaving against medical advice should be monitored as a signal of unmet care needs in SCD.

Background: Advance care planning (ACP) documentation, including living wills and durable power of attorney (DPOA), is intended to support goal concordant end of life care. However, it is unknown if comprehensive documentation confers additional benefits, and how these associations vary across clinical contexts. Methods: We used 2010 to 2022 Health and Retirement Study exit interview data to examine associations between ACP documentation and end of life care among U.S. adults aged 50 years and older. Documentation was categorized as none, one document (living will or DPOA), or two documents (both). Outcomes included intensive care unit (ICU) use, life sustaining treatment, hospice enrollment, and out-of-hospital death. Modified Poisson regression models were used to estimate adjusted risk ratios (aRRs), and temporal trends in documentation were assessed using joinpoint regression. Results: Among 5,622 decedents representing 23.2 million individuals, 42.7% had two documents and 28.9% had none, documentation increased substantially around 2014. Compared with no documentation, having any documentation was associated with lower likelihood of life-sustaining treatment (aRR=0.85, 95% CI: 0.74 to 0.98) and higher likelihood of hospice enrollment (aRR=1.43, 95% CI: 1.28 to 1.60) and out-of-hospital death (aRR=1.11, 95% CI: 1.06 to 1.18), but not ICU use. Having two documents showed similar patterns, with modest differences compared with one document after adjustment. Associations were stronger among decedents with expected death and attenuated among those with unexpected death. Conclusions: Comprehensive ACP documentation is associated with less aggressive end of life care and greater hospice use, though the incremental benefits of two documents are modest. Findings highlight the importance of documentation within care planning processes and the clinical context.

PurposeTo characterize maternal transport patterns in Iowa, a state with levels of maternal care and without formal perinatal regions, and assess whether transport decisions reflect efficient, risk-appropriate coordination. MethodsWe analyzed 2010-2023 Iowa birth records, which included 2,251 maternal transports between obstetric facilities across 106 unique routes. We characterized transport patterns and applied a community detection algorithm to identify "communities" of obstetric facilities that disproportionately transport among themselves. FindingsSuburban and rural counties have elevated transport rates compared to urban counties. 2,189 transports (97%) were from lower-to higher-level facilities. Among these, 2,037 (93%) were to Level III tertiary care centers. 567 transports (25.2%) bypassed a closer facility offering an equivalent or higher level of care than its destination facility. Health system affiliation was associated with bypassing transport, indicating potential organizational rather than purely geographic drivers of transport decisions. Three "communities" of obstetric facilities largely shaped by geographic proximity were identified. ConclusionsAlthough Iowa does not have formal perinatal regions, patterns of maternal transport are mostly in line with three de facto regions. Some potential inefficiencies were identified, such as obstetric facilities transporting to a farther facility when a closer facility offered the same level of care or higher. These findings may help identify opportunities to enhance care coordination among obstetric facilities, optimize maternal transport networks, and improve regionalization of maternal care.

Introduction Antibiotic use drives antimicrobial resistance, and optimizing prescribing in skilled nursing facilities (SNFs) - which care for medically complex residents in congregate settings characterized by frequent care transitions and diagnostic uncertainty - presents unique challenges. Antimicrobial stewardship (AMS) in SNFs has therefore become a focus of quality improvement efforts by federal and state health agencies. We aimed to identify factors that facilitate and hinder AMS implementation in SNFs. Methods A qualitative study of AMS implementation was conducted in Southern Arizona SNFs randomly sampled to represent urban/suburban, border, and rural regions. Semi-structured interviews were conducted with administrators, clinicians, and nonclinical staff within participating facilities. Interview transcripts were analyzed using constant comparative analysis, with both directed and emergent coding, facilitated by NVivo 12 software. Findings From 04/13/2019 through 12/13/2019, 57 interviews were conducted with 9 administrators, 38 clinical providers, and 10 nonclinical staff across 6 urban/suburban, 2 border, and 2 rural facilities. Analysis identified two thematic categories: "influencer themes," which describe specific barriers and facilitators to AMS implementation, and "system themes," which characterize SNFs as complex adaptive systems shaped by interacting staff roles, care transition challenges, and differing perceptions of AMS practices within the same facility. Key facilitators included effective internal communication, ongoing AMS education, and clinician AMS champions. Primary barriers included poor interfacility communication during care transitions, limited access to diagnostic resources, enculturated prescribing norms, and tension between immediate infection control priorities and long-term AMS goals. Conclusions Findings suggest that AMS implementation in Arizona SNFs is best understood as a systems-level process emerging from interactions among staff roles, organizational workflows, and care transitions, rather than solely from individual prescribing decisions. Recognizing SNFs as complex adaptive systems highlights the importance of communication structures, local champions, and feedback mechanisms. It underscores the need for coordination strategies within and across SNFs to sustain AMS interventions.

ObjectivesTo explore stakeholder perspectives on care coordination barriers and facilitators in regionalized neuro-oncology delivery, using brain tumors as a model for examining complex care pathways serving mixed rural-urban populations. DesignReflexive thematic analysis of semi-structured interviews from stakeholders across the neuro-oncology care pathway was used to identify themes of care system strengths, systemic barriers to effective service delivery and priorities for system improvement. SettingRegionalized Canadian health system serving one of Ontarios largest catchment areas, characterized by predominantly rural populations and substantial geographic distances to tertiary care. ParticipantsThirty-six stakeholders purposively sampled to represent diverse roles across the care pathway, including family caregivers (n=6), healthcare providers from multiple specialties and care settings (n=28) and Indigenous community advisors (n=2). ResultsTwo main themes with subthemes emerged revealing a tension between localized excellence and systemic fragmentation. Theme 1 (Care System Strengths) included three subthemes: responsive palliative care integration, exceptional provider commitment, and effective intra-institutional communication. Theme 2 (Systemic Barriers to Care Continuity) included four subthemes: absent cross-institutional coordination infrastructure, insufficient pathway standardization, inadequate educational infrastructure for patients and providers and limited regional clinical trial access. Coordination mechanisms functioning effectively within the tertiary center consistently failed at interfaces with referring hospitals and community services, with participants describing patients becoming "lost in transitions." ConclusionsFindings reveal how regionalized cancer systems can achieve localized coordination while failing at system integration. The contrast between internal institutional coherence and external fragmentation suggests that effective care delivery requires deliberately extending coordination mechanisms across organizational boundaries through standardized pathways, shared information systems and defined cross-site accountability structures. Brain tumors, requiring rapid multidisciplinary coordination, expose these interface failures with clarity, offering transferable insights for improving integrated cancer care in regionalized health systems serving geographically dispersed populations. ARTICLE SUMMARYO_ST_ABSStrengths and limitations of this studyC_ST_ABSO_LIPurposive sampling captured diverse stakeholder perspectives across the entire care continuum, from tertiary providers to community services and family caregivers C_LIO_LIReflexive thematic analysis with independent coding by three researchers enhanced interpretive rigor and depth C_LIO_LIBrain tumors function as a model condition for examining care coordination due to their rapid progression and sensitivity to variability in care C_LIO_LISingle health system design limits direct generalizability but enables in-depth examination of coordination mechanisms in a regionalized context C_LIO_LIGeographic and organizational characteristics common to Canadian regionalized systems support transferability of findings C_LIO_LIIndigenous patient perspectives were represented through community advisors; direct patient voices from Indigenous communities would strengthen future work C_LI

ImportanceGuideline-concordant care for young children with attention-deficit/hyperactivity disorder (ADHD) includes recommending parent training in behavior management (PTBM) as first-line treatment. However, assessing guideline adherence through manual chart review is time-consuming and costly, limiting scalable and timely quality-of-care measurement. ObjectiveTo evaluate the accuracy and explainability of large language models (LLMs) in identifying PTBM recommendations in pediatric electronic health record (EHR) notes as a scalable alternative to manual chart review. Design, Setting, and ParticipantsThis retrospective cohort study was conducted in a community-based pediatric healthcare network in California consisting of 27 primary care clinics. The study cohort included children aged 4-6 years with [≥] 2 primary care visits between 2020-2024 and ICD-10 diagnoses of ADHD or ADHD symptoms (n=542 patients). Clinical notes from the first ADHD-related visit were included. A stratified subset of 122 notes, including all cases with model disagreement, was manually annotated to assess model performance in identifying PTBM recommendations and rank model explanations. ExposuresAssessment and plan sections of clinical notes were analyzed using three generative large language models (Claude-3.5, GPT-4o, and LLaMA-3.3-70B) to identify the presence of PTBM recommendations and generate explanatory rationales and documentation evidence. Main Outcomes and MeasuresModel performance in identifying PTBM recommendations (measured by sensitivity, positive predictive value (PPV), and F1-score) and qualitative explainability ratings of model-generated rationales (based on the QUEST framework). ResultsAll three models demonstrated high performance compared to expert chart review. Claude-3.5 showed balanced performance (sensitivity=0.89, PPV=0.95, and F1-score=0.92) and ranked highest in explainability. LLaMA3.3-70B achieved sensitivity=0.91, PPV=0.89, and F1-score=0.90, ranking second for explainability. GPT-4o had the highest PPV [0.97] but lowest sensitivity [0.82], with an F1-score of 0.89 and the lowest explainability ranking. Based on classifications from the best-performing model, Claude-3.5, 26.4% (143/542) of patients had documented PTBM recommendations at their first ADHD-related visit. Conclusions and RelevanceLLMs can accurately extract guideline-concordant clinician recommendations for non-pharmacological ADHD treatment from unstructured clinical notes while providing clear explanations and supporting evidence. Evaluating model explainability as part of LLM implementation for medical chart review tasks can promote transparent and scalable solutions for quality-of-care measurement.

Background Persons experiencing homelessness (PEH) have a 2-3-fold greater risk for cardiovascular disease (CVD) mortality compared with domiciled counterparts. Evidence has repeatedly shown elevated chronic disease burden, reduced access to many types of care, and lower utilization of medication to control CVD risk factors in clinical settings dedicated to providing health care to PEH. There are federally funded health clinics targeting barriers to access for patient populations experiencing homelessness in place. These clinics are frequently overwhelmed and limited by their scope to primary care despite well documented burdens of co- and tri-morbid conditions. There is scarce evidence on differences between access, quality, and experiences of care delivered relative to other safety-net models. Method The 2022 Health Center Patient Survey (HCPS) was collected on behalf of the Health Resources and Services Administration (HRSA). The HCPS is a nationally representative, three-staged, sample-based survey collected via 1:1 interview with clinic patients. The survey assessed sociodemographics, health conditions and behaviors, access to and utilization of care, and patients? experiences with comprehensive services they received at HRSA-funded Federally Qualified Health Centers (FQHCs), including community health centers (CHC), healthcare for the homeless (HCH) clinics, and public housing primary care (PHPC) clinics. One hundred and three unique awardees and 318 health center sites were recruited, and 4,414 patient interviews were completed. Investigators analyzed patient characteristics and multiple survey items related to AHA?s Essential 8 metrics for differences between HCH and CHC patient responses. Results HCH clinics had fewer elderly patients (~7%) than CHCs (~17%). Reported 7-day physical activity measures, average sleep below 7 hours per day, and Lifetime smoking (>100 cigarettes; OR=4.2, p<0.001) were all greatest among HCH patients. Fewer HCH patients reported ever having or recent lipid tests (both p<0.001). HCH patients were more likely to report hypertension (p=0.003) but less likely to report receiving nutrition advice (all p<0.05). HCH patients were less likely to be taking medication even if it was prescribed (p<0.001). Adjustments for differences in age or CVD history were able to explain some observed differences but increased the magnitude of other disparities. Conclusions CVD burden differs across the various HRSA funding mechanisms for clinics, as do demographics and multiple metrics of health behaviors and biomarkers of cardiovascular health. Greater disease burden in HCH patients is likely compounded by increased risk factors and underperformance in providing health education interventions.

BackgroundEvaluations of complex care programs for high-need patients have yielded mixed results, and identifying patient subgroups may reveal differential intervention effects. This study aimed to use latent class analysis (LCA) to identify high-need patient subgroups within a randomized trial of the Camden Coalitions Core Model and to examine differences in healthcare utilization and care team engagement. Methods & FindingsWe conducted a post-hoc exploratory analysis of a randomized controlled trial (ClinicalTrials.gov: NCT02090426) involving 780 adults aged 18 [-] 80 years in Camden, New Jersey, who had multiple chronic conditions and frequent hospitalizations. Participants were assigned to receive multidisciplinary care management delivered by nurses, social workers, and community health workers for 3 [-] 4 months following hospital discharge, or to usual care. LCA incorporated medical, behavioral, and social risk factors, as well as prior hospital utilization, to identify patient subgroups. Outcomes included inpatient readmissions and emergency department visits over two consecutive 6-month post-discharge periods, along with service hours delivered to intervention patients. Four patient classes emerged: (1) Behavioral Health & Housing Instability, (2) Multi-system Medical Complexity, (3) Pulmonary Health & Substance Use, and (4) Lower Overall Complexity. In the second 6-month follow-up period, intervention patients had lower readmission rates compared with controls (-6.4 percentage points; 90% CI, -12.2 to -0.5). Subgroup differences included reduced readmissions in Class 4 and fewer emergency department visits in Class 1. Service intensity varied across classes, with Class 1 receiving the highest number of staff hours and Class 2 the lowest. ConclusionPatient segmentation revealed meaningful variation in healthcare utilization outcomes and care team engagement across high-need subgroups, suggesting that tailoring complex care interventions to specific patient profiles may improve program effectiveness and equity.

Objective Cerebral palsy (CP) affects approximately 1 million Americans and 18 million individuals worldwide, yet contemporary US epidemiologic data remains limited. We aimed to use Cerebral Palsy Research Network (CPRN) clinical registry to describe demographics and clinical characteristics of individuals with CP across the US and determine associations with gross motor function and genetic etiology. Methods Registry subjects were included if they had clinician-confirmed CP and prospectively entered data for Gross Motor Function Classification System (GMFCS) Level, gestational age, genetic etiology, CP distribution, and tone/movement types. Logistic regression was used to determine which of these variables plus race, sex, ethnicity, and age were associated with GMFCS level and genetic etiology. Results A total of 9,756 children and adults with CP from 22 CPRN sites met inclusion criteria. Participants were predominantly White (73.0%), male (57.3%), non-Hispanic (87.8%), and younger than 18 years (73.7%). Most were classified as GMFCS levels I-III (55.6%), born preterm (52.8%), had spasticity (83.8%), and had quadriplegia (41.9%); 12.2% were identified as having a genetic etiology. Tone/movement types, CP distribution, and gestational age were significantly associated with both GMFCS level and genetic etiology (p<0.001). Compared to White individuals, Black individuals were more likely to have greater gross motor impairment (p<0.001). Conclusion In this large US cohort, clinical and demographic factors, including race, were associated with gross motor function and genetic etiology in CP. These findings highlight persistent disparities and demonstrate the value of a national clinical registry for informing prognostication, quality improvement efforts, and targeted genetic testing strategies.

ObjectiveTo evaluate whether new tuberculosis (TB) tools in the electronic health record (EHR) can support latent TB infection (LTBI) screening, testing and treatment among children and adolescents in a primary care setting. Study DesignThis retrospective cohort study included children and adolescents between the ages 1-25 years who had a well-child or well-adolescent visit at a Federally Qualified Health Center in Oakland, California, from December 2021 to December 2022. Four new EHR tools were introduced for the completion and documentation of TB risk factor screening, testing and treatment. Data were extracted from the EHR to identify gaps in these steps, and logistic regression was used to examine factors associated with completion of TB infection screening and testing. Acceptability was evaluated using provider satisfaction surveys before and after the implementation of TB EHR tools. ResultsOf 5,879 individuals (median age of 9 years at first visit, interquartile range (IQR) 4-13 years), 94% completed TB risk factor screening. Among those with a new risk factor, 59% had a TB infection test ordered and 96% completed testing. Ten participants (3%) tested positive, all initiated LTBI treatment, and most (n=7, 70%) completed treatment. Overall, 5,162 (88%) individuals completed their LTBI care cascade. Younger children ages 1-4 years were more likely to be screened for TB risk factors, but were less likely to be tested. Provider satisfaction increased from 40% to 71% for risk factor screening, and 36% to 77% for test ordering. ConclusionEHR tools supported completion of the pediatric LTBI care cascade, while also increasing provider satisfaction. EHR-based solutions show promise as part of multi-component strategies to address gaps in LTBI care for children and adolescents.

ObjectiveThe objectives of this study were to: (1) quantify toddlers total physical activity (TPA) and guideline adherence using a machine learning method; and (2) explore socio-ecological predictors (e.g., sex, childcare) of TPA. MethodsToddlers (n=103, 21.4 {+/-} 6.9 months, 52% female) from the Hamilton, Canada region completed a gross motor assessment (Peabody Developmental Motor Scales 2nd ed; PDMS-2) and wore an ActiGraph wGT3X-BT accelerometer on the right hip for 4-8 days. Parents completed demographics and physical activity surveys. TPA was estimated using a validated machine learning model and reported using descriptive statistics. Multiple linear regression explored potential predictors of TPA: age, sex, household income, older sibling, BMI-for-age z-score, gross motor z-score, childcare arrangement, parent physical activity, and temperature, controlling for accelerometer wear time. ResultsToddlers had an average of 200.3 {+/-} 44.0 minutes of daily TPA. Most (72%) met the PA guideline of 180 min/day when averaged across days, while only 27% met the guideline on all days. The regression model was significant and explained 57% of the variation in TPA (F13,79 = 8.09, p < 0.0001). Controlling for wear time, the only significant positive predictors were age and PDMS-2 z-score. ConclusionAlmost three quarters of toddlers met the TPA guidelines. Older toddlers and toddlers with more advanced gross motor skills for their age participated in more daily TPA. Future research should continue to apply machine learning methods in more diverse samples and could build on modifiable predictors (e.g., motor skill) to design interventions to improve toddlers PA levels.

ObjectivesTo evaluate rates of treatment for depression and identify resident- and facility-level predictors of pharmacotherapy among long-term care (LTC) residents in the United States. DesignRetrospective, observational study. Setting and ParticipantsElectronic health record data from 1,675,873 LTC residents in the PointClickCare Life Sciences database (January-April 2025) were reviewed and 358,425 skilled nursing facility residents with a documented depression diagnosis were identified. MethodsResidents were classified as treated/untreated based on having a medication order for pharmacological depression treatment within medication classes recommended by the American Psychological Association. Descriptive analyses incorporated demographic and clinical characteristics, and multivariable logistic regression estimated odds of treatment. ResultsOverall, 81.7% of residents diagnosed with depression had [&ge;]1 pharmacological depression treatment order. Selective serotonin reuptake inhibitors (59.8%) and miscellaneous antidepressants (42.3%) were the most frequently used classes. Treatment rates were similar across depression diagnoses. Higher odds of receiving treatment were observed among residents also diagnosed with vascular dementia and those with hyperlipidemia medication orders. Lower odds were noted among residents who were Black or African American, had diabetes or hyperlipidemia diagnoses, or resided in facilities located in areas with poor socioeconomic status. Conclusions and ImplicationsMost residents with depression had at least one recommended pharmacologic therapy, although important disparities remain. Racial differences, comorbid conditions, and facility context continue to influence treatment access. These findings support the need for improved screening practices, greater attention to equity in prescribing, and strengthened clinical resources in socially vulnerable settings to enhance the quality of depression care in LTC facilities. Brief SummaryDepression is common in long-term care (LTC) and is associated with poor functional and clinical outcomes, however recent treatment patterns are not well understood. Using electronic health record data from 1,675,873 U.S. LTC residents between January and April 2025, 358,425 skilled nursing facility residents were identified with a documented depression diagnosis. The use of antidepressant medication was assessed based on medication order history and was aligned with American Psychological Association recommendations. Overall, 81.7% had at least one pharmacologic treatment order for depression; selective serotonin reuptake inhibitors (59.8%) and miscellaneous antidepressants (42.3%) were most frequently used. After adjusting for covariates, lower odds of treatment were observed among Black or African American residents and among residents in facilities located in more socioeconomically vulnerable areas. These findings highlight persistent inequities in depression pharmacotherapy in LTC and support efforts to strengthen depression assessment and ensure equitable access to evidence-informed treatment across facilities.

Background and ObjectivesRecurrent acute otitis media (rAOM; defined as [&ge;]3 AOM episodes in 6 months or [&ge;]4 episodes in 12 months) affects 10-15% of children in the United States and is a leading cause of healthcare utilization and antibiotic prescriptions. Prospective identification of children at risk of rAOM could help target interventions and identify new risk factors to guide preventive approaches. We therefore sought to develop predictive models to identify children at risk of rAOM using electronic health records (EHR) data. MethodsWe extracted retrospective EHR data for children who were born at Duke University Health System (DUHS) hospitals between January 1, 2014, and June 30, 2022, and who had at least one AOM episode during the study period. We used LASSO to build predictive models for development of rAOM at each episode and identified factors associated with rAOM. ResultsWe identified 6,566 children who met the study criteria, including 1,634 (24.8%) who met criteria for rAOM. A model using only data available at the first AOM episode had an area under the curve (AUC) of 0.75 (0.73, 0.77) and an Area Under the Precision Recall Curve (AUPRC) of 0.41 (95% CI 0.37, 0.46), indicating moderate discriminative ability. At the time of the first AOM episode, features associated with subsequent rAOM development included age, number of prior antibiotic prescriptions, and diagnosis of gastroesophageal reflux disease (GERD). Further, children who developed rAOM were more likely to experience treatment failure than children who did not meet rAOM criteria across all episodes. ConclusionsOur findings indicate that clinical exposures and patient characteristics documented in the EHR distinguish children who are at risk of developing rAOM. Such models could be deployed within EHR systems to identify children who would benefit from early evaluation by an otolaryngologist and audiologist.

Importance Missed outpatient appointments, including no-shows and cancellations, may disrupt continuity of care and be associated with worse outcomes, but long-term system-wide patterns and clinical implications are not well characterized. Objective To characterize variation in missed appointment rates in the Veterans Health Administration (VHA) over time and by geographic location, visit modality, and preexisting conditions, and to evaluate associations between missed appointment rates and adverse outcomes among veterans with posttraumatic stress disorder (PTSD) or traumatic brain injury (TBI). Design Cohort study using VHA Corporate Data Warehouse outpatient appointment data from January 1, 2000, through December 31, 2024. Setting National integrated health care system of the VHA. Participants System analysis includes all scheduled outpatient appointments with a valid status, and outcome analysis includes veterans with PTSD (n = 1 429 890) or TBI (n = 554 553), diagnosed before 2023. Exposures For system -level analyses, missed appointment rates were calculated. In outcome analyses, 2023 missed appointment rates were categorized into tertiles within the cohort and appointment type. Main Outcomes and Measures One year risks of all-cause hospitalization, all-cause mortality, and hospitalization or death beginning January 1, 2024. Results Among 2,162,520,880 outpatient appointments from 2000 to 2024, 6.5% were no-shows and 25.4% were canceled. Across facilities, no-show rates ranged from 3.5% to 14.1%, patient-initiated cancellation rates from 9.7% to 26.0%, and clinic-initiated cancellation rates from 8.5% to 17.9%. In 2023, veterans with amputation, Parkinson disease, PTSD, or TBI had higher missed appointment rates than veterans without these conditions. Among veterans with PTSD, the highest no-show tertile, compared with none, was associated with higher mortality (HR, 1.91; 95% CI, 1.84-1.98) and hospitalization or death (HR, 1.07; 95% CI, 1.05-1.08). Among veterans with TBI, the highest no-show tertile was associated with hospitalization or death (HR, 1.65; 95% CI, 1.61-1.69). Conclusions and Relevance Missed outpatient appointments were common in the VHA and varied substantially across facilities and over time. Among veterans with PTSD or TBI, higher missed appointment rates, particularly no-shows, were associated with increased risks of hospitalization and mortality, suggesting that these patterns may help identify high-risk veterans for targeted outreach.

BACKGROUND Point-of-care (POC) high-sensitivity cardiac troponin (hs-cTn) testing has the potential to expedite decision-making and reduce emergency department (ED) length of stay for patients presenting with possible myocardial infarction (MI) by ensuring that results are consistently available when looked for by clinicians. We assessed the real-life effectiveness and safety of implementing POC hs-cTn testing in the ED. METHODS We conducted a pragmatic, stepped-wedge cluster randomized trial. The control arm was usual care with an accelerated diagnostic pathway utilizing a single-sample rule-out step with a central laboratory hs-cTn assay. The intervention arm used the same pathway with a POC hs-cTnI. The primary effectiveness outcome was ED length of stay assessed using a generalized linear mixed model, and the safety outcome was 30-day MI or cardiac death. RESULTS Six sites participated with 59,980 ED presentations (44,747 individuals, 61{+/-}19 years, 49.5% female) from February 2023 to January 2025, in which 31,392 presentations were during the intervention arm. After adjustment for co-variates associated with length of stay, the intervention reduced length of stay by 13% (95% confidence intervals [CI], 9 to 16%. P<0.001), corresponding to a reduction of 47 minutes (95%CI, 33 to 61 minutes) from a mean length of stay in the control arm of 376 minutes. The 30-day MI or cardiac death rate was similar in the control and intervention arms (0.39% and 0.39% respectively, P=0.54). CONCLUSIONS Implementation of whole-blood hs-cTnI testing at the POC into an accelerated diagnostic pathway was safe and reduced length of stay in the ED compared with laboratory testing.

BackgroundSocial determinants of health (SDOH) affect access to transcatheter aortic valve replacement (TAVR), yet their impact on post-procedural mortality remains incompletely defined. We investigated the association between neighborhood-level social deprivation and post-TAVR mortality, readmission, cardiovascular events, and procedural outcomes. MethodsWe performed a retrospective cohort study of 727 consecutive TAVR patients (2023-2024) with 1-year follow-up data at a central New Jersey tertiary care academic medical center, stratified into quartiles based on the composite Social Deprivation Index (SDI) and its seven constituent domains (Q1 = least deprived; Q4 = most deprived). Kaplan-Meier survival analysis with log-rank testing and Cox proportional hazards regression adjusted for STS-PROM score were used to evaluate mortality across quartiles. ResultsThe cohort (mean age 80.4 years; 46% female; 87% White; mean STS-PROM 5.5%) was skewed toward lower-deprivation neighborhoods (85% in Q1-Q2). Survival differed significantly across SDI quartiles at 30 days (log-rank p=0.037) and 90 days (p=0.049), but not at 1 year (p=0.164). In Cox regression, composite SDI was not a significant predictor of one-year mortality. Domain-specific analysis identified single-parent household density as the only significant mortality predictor, with patients in Q4 having higher 1-year mortality than those in Q1 (aHR 2.65, 95% CI 1.15-6.14, p=0.023). Procedural events, overall 30-day readmissions, and 30-day composite cardiovascular events did not differ significantly across SDI quartiles (all p>0.05). ConclusionNeighborhood-level social deprivation was not independently associated with post-TAVR all-cause mortality, though underrepresentation of patients from highly deprived neighborhoods highlights ongoing access disparities. Single-parent household density, a marker of social fragmentation, demonstrated a hypothesis-generating association with increased mortality risk, suggesting a potential role for neighborhood social fragmentation in post-TAVR outcomes that warrants prospective validation. These findings support equitable TAVR access while highlighting social support as an area for future investigation.

Importance: Health systems are increasingly adopting race-neutral cardiovascular risk prediction tools, yet no study has examined how these choices redistribute preventive treatment at the point of clinical decision-making, particularly for Black individuals who already bear a disproportionate cardiovascular burden. Objective: To evaluate how including race, substituting social determinants of health (SDoH), or excluding both reshapes cardiovascular risk classification, calibration, fairness, and clinical decisions. Design: Retrospective cohort study with repeated cross-validation and integrated decision-focused evaluation, using CARDIA study data with baseline measures from 2010 and cardiovascular outcomes through 2021. Setting: Community-based longitudinal cohort recruited across multiple U.S. cities. Participants: 3,241 Black and White adults without known cardiovascular disease at baseline. Main Outcomes and Measures: Three models predicting 10-year incident cardiovascular disease were compared on predictive performance, calibration, fairness metrics, and realized clinical utility at the ACC/AHA 7.5% preventive treatment threshold. Results: Among 3,241 participants (46% Black, mean age 50 years, 6.9% CVD incidence), overall performance was similar across models (AUC 0.762 to 0.768). Predictor choice substantially reshaped clinical decisions at the guideline threshold. The SDoH-based model improved parity metrics but produced systematic underprediction and concentrated new overtreatment among Black participants. The clinical-only model further improved parity metrics but generated new undertreatment, with four cases of untreated CVD and none avoided. No single evaluative dimension captured the full equity consequences. Conclusions and Relevance: Parity metrics improved under both race-neutral models, yet both produced clinical harms concentrated among Black participants not apparent in population-average metrics. The case for race removal has rested on conceptual grounds, but comprehensive empirical evaluation is necessary before health systems can be confident their model choices truly serve those most at risk.

Background: Current risk assessment tools for guiding direct oral anticoagulant (DOAC) therapy for patients with atrial fibrillation (AF) based on clinical risk factors demonstrate modest predictive performance limiting clinical impact. Additionally, while guidelines recommend periodic reassessment of risk over time, there remains an absence of modelling solutions for capturing evolving risk in AF patients. Methods: Using UK electronic health records, we developed and validated the Transformer-based Risk assessment survival model (TRisk), an artificial intelligence model that predicts 12-month thromboembolic and bleeding events in AF patients by leveraging temporal patient journeys up to baseline. A cohort of 411,850 prevalent non-valvular AF patients aged [&ge;]18 years between 2010 and 2020 was identified from 1,442 English general practices. Practices were randomly allocated to derivation (n=1,079) and external validation (n=363) cohorts. TRisk was compared with CHA2DS2-VASc and CHA2DS2-VA for thromboembolic event prediction, and HAS-BLED and ORBIT for bleeding prediction, with subgroup analyses by sex, age, and baseline characteristics. A second validation of TRisk was also performed on 16,218 US AF patients between 2010 and 2023. A decision model compared outcomes and healthcare costs for TRisk versus standard care. Findings: TRisk achieved higher discrimination for thromboembolic event prediction (C-index: 0.82; 95% confidence interval [CI]: [0.81, 0.83]) as compared to CHA2DS2-VASc (0.71 [0.70, 0.73]) in UK validation. Application of TRisk to US data yielded similar C-index: 0.82 (0.80, 0.84). For bleeding prediction, TRisk (C-index: 0.70 [0.69-0.71]) outperformed both HAS-BLED (0.63; [0.61, 0.64]) and ORBIT (0.64; [0.63, 0.65]), with comparable US results (0.71; [0.69, 0.74]). The model remained well-calibrated across both populations and performed equitably across subgroups, including by race and during the COVID-19 pandemic. Impact analyses showed TRisk could reduce DOAC prescriptions by 8% in the UK and 7% in the US relative to guideline-recommended approaches, while preventing at least as many thromboembolic events. This refined approach would generate annual healthcare savings of GBP 5.5 million and USD 456.2 million in the UK and US respectively among patients initiating DOACs, rising to GBP 48.6 million and USD 1.8 billion when extended to all AF patients on DOACs. Interpretation: TRisk enabled more precise prediction for both thromboembolic and bleeding events across AF populations in UK and US compared to established clinical scoring systems. Incorporating TRisk into routine AF care would result in substantial cost savings without compromising the identification of true high-risk patients. Funding: None

Objectives: For suspected hip fractures, prehospital protocols directing patients to an orthopaedic centre rather than the nearest emergency department (ED) could reduce time-to-surgery but may impact EMS travel burden. This study evaluates the impact of transfer protocols by quantifying transport to hospitals from long term care (LTC) facilities across Ontario. Methods: A retrospective cross-sectional analysis of all Ontario LTC facilities and hospitals was performed. Two protocols were modeled: standard transfer to the nearest ED with subsequent transfer if required, and selective transfer based on Collingwood Hip Fracture Rule prehospital screening1 directly to the nearest orthopaedic services (orthoED). Median one-way travel distances were calculated from Google Maps. Results: In Ontario, 15.4% of LTC residents require hospital destination decisions because their nearest ED lacks orthopaedic services; for these facilities, median distances were 2.7km to the ED and 36.0km to the orthoED. Among the 52 LTC facilities where selective transfer was distance-optimal, it substantially reduced travel for patients with hip fracture (31.1km vs 49.6km; P<.01) while only modestly increasing travel for patients without hip fracture. Where standard transfer was distance-optimal, little travel difference was noted for patients with hip fracture, however false positive screened patients traveled significantly further to an orthoED. Greatest negative consequences of selective transfer lie in the 1.3% of residents living farthest (>100km) from an orthoED. Conclusions: EMS direct transportation to hospitals with orthopaedics may improve hip fracture care but can increase EMS burden due to patients identified falsely as having a hip fracture, particularly in remote communities.