CMAJ Open

Importance: Abdominal pain causes roughly 10 million US emergency department (ED) visits annually, most resulting in discharge. Post-discharge courses vary, yet existing risk models predict only whether an ED revisit occurs, not what that revisit outcome will entail. Objective: To evaluate whether Curiosity, a generative medical event foundation model, can predict post-ED-discharge trajectories for adults with abdominal pain, differentiating the timing and severity of expected outcomes. Design: Retrospective cohort study; encounters January 1-December 31, 2022; 30-day follow-up; analysis conducted in 2026. Setting: Epic Cosmos research network (multicenter, population-based, de-identified electronic health record). Participants: Adults ([&ge;]18 years) discharged from the ED with abdominal pain, excluding training-set patients. Random sample of 3,000 drawn from 150,030 eligible patients (65.3% female; median age 47 years [IQR 36-60]). Exposure: ED discharge after evaluation for abdominal pain. Main Outcomes and Measures: Primary: Curiosity model vs. per-task, separately estimated XGBoost models on area under the receiver operating characteristic curve (AUROC) for ED revisit ending in admission (admit-revisit), ED revisit ending in discharge (DC-revisit), and any ED revisit at 72 hours, 7 days, and 30 days. Secondary: trajectory-level accuracy across 36 trajectory classes and edit distance vs XGBoost; calibration of simulated vs observed conditional path probabilities across 45 transitions. Results: Curiosity identified patients at high risk of revisit requiring admission more accurately than XGBoost and differentiated those likely to revisit without admission. Among 3,000 patients, Curiosity's 30-day admit-revisit AUROC was 0.83 (95% CI 0.79-0.87) vs 0.70 (95% CI 0.65-0.75) for XGBoost (DeLong P<.001), and admit-revisit AUC-PR was 0.37 (95% CI 0.29-0.46) against a 4.1% cohort base rate, vs XGBoost 0.13 (95% CI 0.09-0.19). Curiosity identified the most likely trajectory out of 36 possibilities for 45.9% of patients (XGBoost 41.0%; McNemar P<.001), with median edit distance 1.28 vs 1.40 (Wilcoxon P<.001). Median absolute calibration error across 45 transitions was 1.30 percentage points (95% CI 0.32-2.49). Conclusions and Relevance: A generative medical event foundation model produced calibrated trajectory-level predictions and discriminated admit-revisits more effectively than task-specific XGBoost baselines, separating patients that revisited and were admitted from those who revisited and were discharged.

Introduction The COVID-19 pandemic profoundly disrupted healthcare delivery worldwide, with cancer care among the most affected services. Prior studies documented delays in referrals, reduced specialist access, and increased provider burden. However, the extent to which these experiences were reflected at the system level remains unclear. Objective To document cancer care experiences and examine whether these experiences were reflected in population-level health system indicators across Ontario, Canada. Methods We used an exploratory sequential mixed-methods design. Qualitative data were collected through focus groups and semi-structured interviews with 32 participants, including patients with cancer (n=8), caregivers (n=5), healthcare providers (n=14), and decision-makers (n=5) across two hospital settings in Ontario, Canada. Emergent themes informed the development of quantitative indicators. We then conducted a retrospective population-based analysis of linked administrative health databases for cancer patients in Ontario (n=87,786) to assess the prevalence of identified themes. Results Four themes emerged: (I) delays in diagnosis and screening; (II) disrupted access to primary care; (III) barriers to specialist and mental health services; and (IV) fragmented care for patients with multimorbidity. Quantitative findings corroborated major themes. Screening rates declined for cervical (64.8% to 57.5%) and breast cancer (64.5% to 57.2%). While in-person primary care shifted almost entirely to virtual modalities (8.5% to 95.4%), overall visit volumes remained stable. Specialist care showed uneven patterns, with increased oncology visits but declines in cardiology and mental health services. Patients with multiple comorbidities experienced the largest reductions in non-oncology specialist care. Conclusion The pandemic disrupted key components of cancer care, particularly screening, access to certain specialist services, and care for patients with complex needs. Integrating qualitative and quantitative evidence highlights areas of system vulnerability and underscores the need for coordinated, resilient cancer care capable of maintaining essential services during future crises.

BackgroundHospital visiting-hour policies vary widely across the United States, yet the structural factors shaping this variation remain poorly characterized. ObjectiveThis study investigates how hospital-level financial characteristics, payer mix, and rurality relate to the restrictiveness of inpatient visiting-hour policies, and assesses whether these relationships differ across states with diverse Medicaid expansion statuses. DesignCross-sectional observational analysis of hospital visitor policies in four states (Massachusetts, Wisconsin, Tennessee, and South Carolina) selected based on Medicaid expansion status, population size, and hospital density. ParticipantsA total of 318 acute-care hospitals were included using publicly available data from the Centers for Medicare & Medicaid Services and the National Academy for State Health Policy. Main MeasuresThe primary outcome was total daily visiting hours in general inpatient wards. Predictors included volume/capacity, patient mix, financial performance/efficiency, geography and organizational structure. Key ResultsHospital-level characteristics including higher Medicaid payer mix, stronger financial margins, greater inpatient occupancy, and larger size were associated with shorter visiting hours in unadjusted analyses. Commercial payer mix and rurality predicted longer hours. Mean visiting duration was 14.1 hours/day (SD = 5.07; range 0-24), with Massachusetts having the shortest on average across states (10.5 hours/day) and Wisconsin the longest (16.3 hours/day). Medicaid payer mix was the only predictor associated with visiting-hour restrictiveness after multiple-testing correction. Each 10-percentage-point increase in Medicaid payer mix was associated with an approximately 11.3% decrease (p = 0.002) in visiting hours. Within-state variation exceeded the differences between-states. ConclusionsVisitation hours vary considerably, with correlations around rurality of the community served, size of the hospital, and the number of patients on Medicaid. Medicaid payer mix emerged as the most consistent predictor of restrictiveness after adjustment. Hospitals can use these findings to evaluate visitation practices to balance patient-centered care with operational demands.

PurposeHigh clinical workload is associated with worse patient and hospital outcomes and is a well-established driver of clinician burnout. Trainees may be particularly exposed, shouldering both clinical and educational responsibilities. Evidence-based work design offers a data-driven approach to healthcare work but relies on robust workload measurements. Trainee workload remains poorly characterized, as commonly used metrics (e.g., duty hours, patient census) overlook cognitive and contextual dimensions. This pilot evaluated the feasibility of combining survey-based and electronic health record (EHR) data to characterize internal medicine (IM) trainees workload. MethodsA pilot study was conducted including IM and Medicine-Pediatrics residents (postgraduate years 1-4) between March 31 and June 22, 2025. Participants completed daily surveys during a seven-day inpatient schedule assessing workload and work experience domains, including environment, professional fulfillment, psychological safety, autonomy, and rounding experience, using validated instruments where available. Concurrently, EHR data captured chart review, documentation, orders, and secure messaging activity. Associations between survey and EHR data were assessed. ResultsAmong 37 eligible residents, 28 (76%) participated in the pilot capturing 166 shifts. Trainees spent 4.4 {+/-} 1.6 (mean {+/-} SD) minutes completing daily surveys and 8.6 {+/-} 2.3 minutes completing the final survey. Trainees reported working 11.6 {+/-} 1.0 hours/day and a median census of 9.0 (IQR 6.0-11.0). NASA-TLX score was 50.8 {+/-} 12.6. Positive shift ratings were associated with lower NASA-TLX scores and perceived rounding length. First-to-last EHR login duration was 15 {+/-} 2 hours/day, and EHR data showed 204 {+/-} 46 active minutes/day. Login duration correlated with self-reported hours (r=0.43, p<0.0001), and notes signed correlated with self-reported team (r=0.19, p=0.013) and personal census (r=0.34, p<0.0001). ConclusionsIntegrating survey-based and EHR-derived workload measures provides multidimensional insight into trainee work. This novel approach supports scalable measurement and evidence-based work design interventions to improve trainee well-being, education, and clinical efficiency.

Background: Health-related social needs (HRSNs), particularly housing instability, are significant drivers of poor health outcomes among Medicaid populations. New York State's Social Care Networks (SCNs) aim to systematically connect members to housing services through coordinated referral systems. However, limited systematic analysis of referral patterns hinders quality improvement efforts. We analyzed housing referral outcomes and workflows to identify barriers to successful service connections. Methods: We conducted a mixed-methods quality improvement study at Public Health Solutions' WholeYouNYC SCN Coordination Center. Quantitative analysis examined 4,258 housing referrals submitted between June 2025 and January 2026, extracted from the Unite Us platform via Power BI dashboard. We calculated acceptance rates, analyzed time metrics, and examined outcomes by receiving organization. Qualitative data were collected through structured consultations with 7 staff members (5 navigators, 2 supervisors) and review of internal workflow documentation. Process mapping identified workflow bottlenecks. Results: Of 4,258 housing referrals, only 45% (n=1,936) were accepted by receiving organizations, while 19% (n=815) were rejected and 32% (n=1,382) remained awaiting response with no recorded action. Average time to acceptance was 8 days for accepted referrals. Acceptance rates were consistent across top receiving organizations (44-46%), suggesting systemic rather than partner-specific barriers. Analysis of unresolved referrals revealed prolonged cases, with the longest pending 271 days. Three critical workflow bottlenecks were identified: CBO response delays, missing housing documentation, and challenges with client engagement. Conclusions: Low housing connection rates (45%) and prolonged unresolved referrals (up to 271 days) indicate systemic barriers requiring interventions at multiple levels. Recommendations include establishing CBO response time benchmarks, implementing automated follow-up protocols, standardizing documentation requirements, and enhancing real-time data monitoring. These findings provide an evidence-based framework for quality improvement in social care coordination programs.

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.

BackgroundHigh workload among healthcare workers has increasingly been correlated with poor patient outcomes, inefficient operational and financial outcomes, and burnout. Despite growing literature exploring causes of attending physician workload, there is limited understanding of trainee-specific measures. ObjectiveWe aimed to characterize elements contributing to trainee workload and perceived challenges and satisfiers to the trainee workday as a foundation for better understanding and measuring trainee work experience. MethodsInternal Medicine and Medicine-Pediatrics residents at an academic medical center were invited to participate in focus groups discussing contributors to inpatient workload and work experience between March and April 2024. A qualitative content analysis identified key metrics of trainee workload and work experience, which were then consolidated into overarching domains. A structured, multi-round rating process ranked the perceived relevance of each metric. ResultsTwenty residents participated across six focus groups. Analysis of focus groups yielded 297 workload metrics across 28 unique domains. Seventeen domains had metrics identified as highly relevant (median 6-7; IQR < 1) including autonomy, communication, disruptions, task switching, documentation, emotional burden, patient factors, professional fulfillment, rounding, teaming, and work-life balance. ConclusionsResident physicians highlighted complex interactions between clinical factors, work design, and psychosocial dynamics that contribute to their sense of workload. This creates opportunities to develop unique measures of workload to understand the trainee experience better. Further studies are needed to capture the generalizability of these findings and the relationship between these workload domains and patient, organizational, and trainee outcomes with the aim of implementing evidence-based work design.

Background Advice and Guidance (A&G) enables primary care clinicians to seek specialist input, supporting decision making and avoiding unnecessary referrals. The use of A&G has significantly expanded, accelerated by COVID19 and contractual changes. While A&G is intended to streamline elective care, concerns persist regarding workload shift, variable responsiveness, and system usability. Despite growing policy emphasis, little is known about why clinicians choose to use A&G. Aim Explore the current use of A&G within primary care, focusing on decision making processes which underpin PCCs' decision to use A&G. Design and Setting Qualitative study set in English Primary Care Method Twenty semi structured video interviews were conducted with primary care clinicians purposively sampled for maximum variation. Topic guides were developed with PPIE input and refined iteratively. Data were analysed using reflexive thematic analysis within an interpretive description framework, with themes developed collaboratively and refined through discussion with researchers and PPIE contributors. Ethical approval was obtained (REC 333799). Results Four overarching themes encapsulate clinicians' decisions to use A&G: clinical presentation (acuity and complexity), navigating healthcare pathways, previous experiences of A&G, and using A&G to validate clinical decision making. Barriers included delayed responses and uncertainty about inequitable workload distribution. These factors shape how effectively A&G could be integrated into routine practice. Conclusion Primary care clinicians use A&G to support patient care and aid decision-making, but its effectiveness depends on timely, clinically helpful responses. Ensuring responses remain appropriate to primary care remit and capacity will be essential if A&G becomes the main route into elective care.

Objectives: Electronic Health Records (EHRs) impose a significant time burden on physicians, often requiring work to be completed outside of scheduled hours. While this burden is well-documented, how it evolves throughout emergency medicine (EM) residency remains poorly understood. This study aimed to quantify EHR usage patterns, analyze the composition of after-shift work, and characterize the development of EHR efficiency across EM training. Methods: We conducted a retrospective cohort study of EM residents (postgraduate year [PGY] 1-4) using 5.5 years of EHR audit log data (2020-2025) at a single academic institution. We analyzed EHR time per new patient encounter, stratified by postgraduate year, and categorized activities into domains such as documentation, chart review, and orders. EHR work was measured both during and after scheduled shifts. Results: The analysis included 144 unique residents and 167,010 new patient encounters across 15,386 shifts. Encounter-attributed EHR time per encounter decreased by 52% from PGY-1 to PGY-4 (median 19.9 to 9.6 minutes, p<0.001), despite an 86% increase in patient volume per shift (median 7 to 13 encounters). This efficiency gain was driven primarily by a 69% reduction in documentation time (9.3 to 2.9 minutes), accompanied by shorter notes. After-shift work (EHR activity after the 9-hour clinical shift) was present in 89.9-94.4% of encounters. At the shift level, combined after-shift EHR time (encounter-attributed plus tracking board) was a median of 64.2 minutes per shift for PGY-1 and 104.2 minutes for PGY-4. Shift-level tracking board activity dominated the after-shift burden and increased with training (median 40.2 to 79.0 minutes per shift from PGY-1 to PGY-4). Conclusions: EM residents achieve substantial gains in on-shift EHR efficiency, with the largest reductions observed in documentation time, accompanied by shorter notes and faster input speed. However, a persistent after-hours workload, dominated by administrative and patient flow tasks, suggests that (at least at this single institution) system-level factors--not just individual skill--may contribute to this pattern. Monitoring these objective EHR metrics may help programs identify struggling learners and evaluate the impact of interventions aimed at improving resident well-being and workflow efficiency.

Background: Stroke is a time-sensitive neurological emergency in which early EMS activation and presentation to definitive care are cornerstones of effective therapy. Large language models (LLMs) are increasingly consulted by the public for medical advice, but the veracity of the guidance provided by commercially available models responding to potential stroke symptoms is not well understood. Methods: We performed a cross-model benchmarking study comparing the triage choices of three frontier LLMs (Claude Sonnet 4.6, GPT-4o, and Llama 3.3-70b-versatile) on first-person vignettes describing a unilateral arm symptom on waking, across 10 symptom descriptors, and two clinical phases (before and after a partially reassuring self-examination), with or without a clinical distractor (n=50 per condition). Results: Claude sought emergency care most often, Llama least, and GPT-4o in between, diverging most sharply in the post-examination phase where Claude called 911 in 100% of runs, Llama called for non-emergency help in 100%, and GPT-4o was symptom-dependent. A distractor shifted behavior away from emergency care in almost all conditions: calling 911 fell from 37.9% to 14.6% and waiting rose from 0% to 45.9% in the post-examination vignette. Responses were also sensitive to symptom word: weak, limp, heavy, and clumsy generated higher alarm, whereas numb, tingly, odd, strange, and weird generated less urgent responses. Conclusions: The increasing use of LLMs for medical advice has significant public health implications. Commercially available LLMs show significant model-to-model variability and framing sensitivity when confronted with potential stroke symptoms, including under-recognition of canonical CDC warning descriptors, underscoring the need for systematic benchmarking as these tools become de facto first points of contact for patients experiencing neurological emergencies.

BackgroundIndividuals experiencing or at risk of homelessness face substantial barriers to preventive eye care that are poorly addressed by standard service models. Interdisciplinary optometry-social work collaboration offers a rights-based approach to improving engagement and continuity of care. MethodsA convergent mixed-methods study was conducted between February and August 2024 at a multidisciplinary community centre. Clients experiencing or at risk of homelessness received integrated optometry and social work assessment and were prioritised as high, medium, or low based on combined clinical and social risk. Social work follow-up was guided by the Triple Mandate and W-Questions framework. Quantitative data were summarised using mean (SD), median [IQR], or n (%). Qualitative case notes were analysed using content analysis with inductive coding and secondary review for consistency. ResultsA total of 165 clients had priority categories coded (high: 68; medium: 47; low: 154). Demographic data were available for 132 clients (60% male; mean age 49.5 years [SD 16]); 27% had not completed high school, 89% reported weekly income below AUD 1000, and 28% had vision impairment. Two hundred forty-five case-note entries were consolidated into 146 unique records. SMS (46%) and phone calls (38%) were the most documented contact methods, although only 21% of calls were answered; missed calls (13%) and disconnected numbers (7%) were common. Multi-modal contact was more frequently documented for higher-priority clients. Appointment assistance was the most recorded facilitator (71%), while rights-based supports, including interpreter and transport assistance, were infrequently documented ([&le;]5%). Qualitative analysis identified unstable communication, reliance on informal supports, and service fragmentation as key influences on recall outcomes. ConclusionThis study supports an interdisciplinary, rights-based optometry-social work model to address barriers to preventive eye care among people experiencing or at risk of homelessness. Embedding structured handovers and tiered recall processes within community-based services may strengthen continuity and accountability for high-priority clients. Future implementation should evaluate outcomes related to equity of reach, service integration, and sustained engagement in 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.

ObjectiveTo quantify waiting time in days for scheduled outpatient specialist consultations and to compare waiting time between standardized and non-standardized access pathways in Ecuadorian public hospitals. MethodsWe analyzed hospital-based survey data from Ecuadorian public hospitals, restricted to adults attending a scheduled outpatient specialist consultation (n = 4,436). Emergency care, unscheduled urgent visits, procedures, and follow-up visits were excluded by design. Access pathway was classified from participants self-report as standardized (institutional or system-based) or non-standardized (informal or non-system-based). Waiting time, defined as the number of days between obtaining the appointment and attending the consultation, was compared using the Mann-Whitney U test. Sociodemographic correlates of non-standardized access were examined using adjusted logistic regression, and adjusted median differences were estimated using quantile regression ({tau} = 0.50). Analyses were stratified into direct-access specialties and referral-required specialties. ResultsNon-standardized access was associated with shorter waiting times than standardized access. In adjusted median regression, non-standardized access was associated with a 3.2-day shorter median waiting time (95% CI -4.6 to -1.8). The difference was larger in direct-access specialties (-15.0 days, 95% CI -15.0 to -6.0) than in referral-required specialties (-5.0 days, 95% CI -5.0 to 0.0). ConclusionAmong patients who attended a scheduled outpatient specialist consultation in Ecuadorian public hospitals, non-standardized access was associated with shorter waiting times, particularly in direct-access specialties. These findings suggest that, within routine outpatient care, parallel access pathways may shape timeliness and warrant greater transparency in appointment allocation and referral coordination.

Background Pediatric immunizations for Respiratory Syncytial Virus (RSV), including monoclonal antibodies for infants and vaccines for pregnant people, have become broadly available and can prevent severe RSV outcomes in infants. However, quantifying the impact of RSV immunization in prevention of severe pediatric illness at the population-level is limited by lack of RSV case surveillance data. The Massachusetts Department of Public Health (DPH) conducted a modeling analysis using routine public health surveillance data to estimate the state-level impact of new RSV immunization products on Emergency Department (ED) visits and hospitalizations in Massachusetts for highest risk pediatric groups. Methods A scenario projection tool, called R.Scenario.Vax, was utilized to simulate RSV-associated ED hospital encounters by age group in the context of newly available immunizations. ED visit and hospitalization data from the National Syndromic Surveillance Program (NSSP) during the time period 10/08/2017--10/19/2024 were analyzed, scaled to account for changes in RSV testing practices over time and missing encounter volume in historic data, and utilized to inform model fit of a "typical" RSV season. RSV immunization data from the Massachusetts Immunization Information System (MIIS) for the 2023--2024 and 2024--2025 RSV seasons informed high and moderate pediatric RSV immunization coverage scenarios and their impact was compared to a counterfactual reference scenario of no new immunizations. Median projections were quantitatively and qualitatively compared to observed 2024--2025 season data. Percent reduction in hospital encounters and encounters averted per 10,000 population were calculated for each scenario as compared to the reference. Results Projections for the youngest at-risk age groups showed significantly lower RSV-associated ED visits and hospitalizations during the 2024--2025 season for both high and moderate immunization coverage scenarios. Median projections for infants under 6 months old in the highest coverage scenario, wherein nearly all infants were immunized, showed 72.6% lower ED visits and 73.4% lower hospitalizations when compared to the reference scenario, equating to 262 ED visits and 85 hospitalizations averted per 10,000 population. Conclusions Our results support the use of modeling methods for public health insights and suggest that RSV immunizations for infant populations result in significantly lower RSV-related ED encounters in Massachusetts.

BackgroundAlthough virtual consultations are increasingly used in healthcare, mode affects attendance patterns remains limited, particularly across demographic groups. Within NHS secondary care, telephone consultations have been the most widely adopted form of telephone care; however, few studies have examined non-attendance (commonly termed Did Not Attend [DNA]) patterns specifically for telephone consultations and fewer still have explored how patient characteristics influence attendance differently across consultation modes. Understanding these patterns is essential for equitable service planning. ObjectiveTo compare non-attendance rates between telephone and in-person secondary care consultations among adults with type 2 diabetes (T2D), and to identify patient characteristics associated with non-attendance under each mode. MethodsData from 853,693 secondary care consultations (January 2020-August 2024) for 45,618 patients with T2D in Northwest London were analysed. Telephone consultations in this dataset consisted exclusively of telephone consultations; we therefore refer to them as telephone consultations throughout. Patient-level consultations were aggregated into patient-mode strata for regression modelling. Zero-inflated Negative Binomial regression assessed factors associated with missed consultation rates by mode (in-person or telephone). Propensity-score balance diagnostics (inverse probability of treatment weighting) were conducted to assess measured confounding by mode assignment. Specialty-stratified non-attendance rates were examined across 34 major specialties. ResultsIn-person consultations had higher unadjusted non-attendance rates than telephone consultations (9.1% vs 7.2%, p<0.001). This pattern was consistent for both first consultations (9.3% vs 6.2%, p<0.001) and follow-up consultations (9.0% vs 7.50%, p<0.001). For in-person consultations, higher non-attendance was associated with younger age (18-39: 12.2%, 40-59: 11.1% vs 60-79: 9.9%, p<0.001), Black or Black British ethnicity (18.9% vs White: 7.6%, p<0.001), and greater deprivation (most deprived IMD1: 10.3% vs least deprived IMD5: 7.0%, p<0.001). For telephone consultations, higher non-attendance was associated with male gender (7.3% vs female: 7.0%, p<0.01), younger age (18-39: 11.3%, 40-59: 9.5% vs 60-79: 6.1%, 80+: 5.6%, p<0.001), and greater socioeconomic deprivation (most deprived: 8.3% vs least deprived: 4.7%, p<0.001). Interaction analyses revealed that demographic disparities were amplified for telephone relative to in-person consultations. Specialty-stratified analysis showed that in-person non-attendance exceeded telephone non-attendance in the majority of high-volume specialties. ConclusionsIn-person consultations had higher non-attendance rates than telephone consultations. Various demographic factors influenced non-attendance rates, with younger age and greater socioeconomic deprivation consistently associated with non-attendance for both in-person and telephone consultations. These findings suggest that a personalised, equity-informed approach to consultation mode selection is needed. Findings apply to telephone consultations and may not generalise to video-based modalities.

Background Rural populations face disproportionate barriers to healthcare access, often due to geographic isolation and limited provider availability. Prior studies have shown that increased travel time negatively affects appointment adherence. Telemedicine has emerged as a potential solution, but understanding its utilization in rural populations remains ongoing. Methods This retrospective cross-sectional observational study analyzed all scheduled appointments (n=5,548) from a single rural family medicine clinic in the Pacific Northwest United States during 2024. One-way travel times were calculated using the Google Maps Distance Matrix API and categorized into Short (<15 minutes), Medium (15-30 minutes), and Long (>30 minutes) commute groups. Proportions for utilization and cancellations of both telemedicine and in-person appointments were assessed across commute groups using chi-square tests (p < 0.05 considered significant). Results Overall, the proportion of cancellations were significantly higher among patients with Long commutes (36.2%) compared to Medium (31.0%) and Short (32.2%) commute groups (p < 0.001). Telemedicine utilization increased proportionately with commute time (7.7% for Long commute patients vs. 1.5% for Short; p < 0.001). However, telemedicine cancellation proportions did not significantly differ across groups (21.2% for Long, 13.3% for Medium, 17.0% for Short; p = 0.122), suggesting comparable telemedicine adherence regardless of distance. The proportions for in-person appointment utilization and cancellation were both greatest for the Short commute group. Conclusion Longer travel times are associated with increased appointment cancellations for rural patients, reinforcing travel burden as a key barrier to care. Telemedicine use increases with commute distance and demonstrates consistent adherence across groups, indicating its value as a tool to address rural healthcare gaps. These findings support the continued expansion of telehealth infrastructure to improve care for geographically isolated populations.

BackgroundRare diseases (RD; enfermedades poco frecuentes, EPoF, in Chilean policy terminology) collectively affect 3.5-5.9% of the population and are associated with long diagnostic trajectories. Chile lacks a reproducible national operational definition for identifying RD in administrative hospital data. MethodsWe conducted a retrospective observational analysis of Chilean GRD-IR events (IR-29301 version) for 2019-2024 released through FONASA Datos Abiertos, covering hospital discharges and major ambulatory surgery reported by 72 public establishments for FONASA-covered persons. The canonical analytical cohort contained 5,779,482 DRG events in 4,027,921 linked patients. We constructed a Chilean Orphanet-ICD-10 homologation and audited it through an agentic human-in-the-loop pipeline, yielding a conservative RD operational catalogue (434 final ICD-10 codes in the KEEP + MAP_TO_SPECIFIC_ORPHA scenario). RD-coded DRG events were labeled as observed inpatient odysseys when at least one prior DRG event existed for the same patient. We quantified prior events, DRG-observed inpatient trajectory time, nonspecific prior diagnoses, DRG weight, and bridge-code associations. Bridge-code enrichment was estimated using patient-level Fisher exact tests with Benjamini-Hochberg false-discovery correction; event-level estimates were retained as sensitivity analyses. ResultsThe audited conservative catalogue identified 55,284 primary-diagnosis RD-coded DRG events in 45,784 patients and 374,866 RD-coded events in any diagnostic field. We characterized 63,685 observed inpatient odyssey cases in 25,648 unique patients across 371 audited RD ICD-10 codes. Median DRG-observed inpatient trajectory time to RD-coded diagnosis was 241 days, and mean prior events per odyssey was 8.1. Bridge-code analysis identified 616 associations with support [&ge;] 10 patients and 390 with q < 0.05; 350 significant associations were no-same-code administrative trajectory signals. These signals varied in interpretation, including clinically plausible precursors, diagnostic refinement, and care-process bridges. The Odyssey Index reordered conditions relative to raw prior-event counts, separating high-volume entities from stronger trajectory signatures. ConclusionsTo our knowledge, we provide the first nationwide audited and reproducible characterization of inpatient RD diagnostic odysseys in Latin America using administrative hospital data. The framework supports trajectory surveillance, registry design, quality-control analyses, and prioritization of candidate signals for prospective clinical validation under Chiles Law 21,743. Bridge-code associations should be interpreted as statistically enriched administrative signals, not as validated causal or clinical pathways. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/26352213v1_ufig1.gif" ALT="Figure 1"> View larger version (57K): org.highwire.dtl.DTLVardef@42593borg.highwire.dtl.DTLVardef@1f0690aorg.highwire.dtl.DTLVardef@803365org.highwire.dtl.DTLVardef@ae41af_HPS_FORMAT_FIGEXP M_FIG Graphical abstract. Updated canonical FONASA DRG/GRD-IR 2019-2024 cohort, audited RD catalogue, odyssey cohort, and bridge-code signal summary. C_FIG

Background and ObjectivesSkilled nursing facility (SNF) hospitalization rates vary substantially across facilities serving comparable patient populations, yet the organizational factors underlying high performance remain poorly characterized. This study examines whether faith or mission-driven organizational identity is associated with lower-than-expected hospitalization rates in a national sample of Medicare-certified SNFs. DesignCross-sectional analysis of a stratified random sample of 618 Medicare-certified SNFs, drawn from a national cohort of 13,419 facilities with claims-based quality data. Facilities were classified by organizational identity (faith-affiliated, purpose-driven, or secular) using publicly available records. Performance was measured using CMS claims-based hospitalization and emergency department transfer rates adjusted for expected rates given patient case mix. Setting and ParticipantsMedicare-certified skilled nursing facilities in the United States, February 2026 CMS release. MethodsWe computed a composite performance gap as the mean of four z-scored observed-minus-expected measures (short-stay and long-stay hospitalization and ED transfer rates). We tested the association between faith affiliation and performance using Fishers exact test, logistic regression, OLS regression, propensity score matching, and causal mediation analysis. ResultsFaith-affiliated or purpose-driven facilities constituted 14.7% of significant overperformers (95% CI: 7.0-23.5%) and 0% of significant underperformers (95% CI: 0.0-4.4%), a monotonic gradient confirmed across all five performance zones. After propensity score matching on facility size, ownership type, and urbanicity (n=49 matched pairs), faith-affiliated facilities achieved 18.2% short-stay rehospitalization compared to 21.7% for matched secular facilities (3.5 percentage points fewer, p=0.019), and 1.30 long-stay hospitalizations per 1,000 resident-days compared to 1.71 (0.41 fewer per 1,000 days, p=0.019). Faith affiliation was associated with 61% more RN staffing hours per resident per day (0.96 vs. 0.60 hours, p<0.001), and formal mediation analysis confirmed that RN staffing hours substantially mediated the relationship between faith affiliation and hospitalization performance. Conclusions and ImplicationsFaith and mission-driven organizational identity is associated with superior hospitalization performance in a national SNF sample, mediated by elevated RN staffing intensity. These findings suggest that organizational culture and values are modifiable upstream determinants of nursing home quality, with implications for quality improvement, workforce policy, and value-based payment design.

BackgroundThe instability plasticity framework proposes that multimorbidity trajectories periodically enter instability phases that are vulnerable to escalation but also potentially modifiable through relational intervention. Whether such phases commonly resolve without acute care, or predominantly progress to hospitalisation, has not been quantified at scale. ObjectiveTo quantify instability window outcomes across a full longitudinal monitoring cohort; to test whether the characteristics distinguishing admitted from resolved windows reflect within-patient trajectory dynamics or between patient severity; and to characterise which patient-reported and operator-rated signals reliably precede admission, using both a curated pilot sub-cohort and the full monitoring cohort with an explicit cross-cohort comparison. MethodsTwo complementary analyses were conducted on data from the MonashWatch Patient Journey Record (PaJR) relational telehealth system. Instability windows were identified algorithmically ([&ge;]2 consecutive calls with Total_Alerts [&ge;]3) across the full longitudinal dataset (16,383 calls, 244 patients, 2.5 years) and classified by linkage to ED and hospital admission data. Window characteristics were compared at window, patient, and paired within-patient levels. Pre-admission signal cascades were analysed in two configurations: a curated pilot sub-cohort (64 patients, 280 calls, {+/-}10-day window, 103 admissions, December 2016-September 2017) and the full monitoring cohort (175 patients, 1,180 pre-admission calls, {+/-}14-day window, December 2016-July 2019). A three-way cross-cohort comparison decomposed differences between the two configurations into pipeline and population effects. Results621 instability windows were identified across 157 patients (64% of the monitored cohort). 67.3% resolved without hospital admission or ED attendance, a rate stable across alert thresholds 1-5. In paired within-patient analysis (n = 70), duration in days (p = 0.002) and multi-domain breadth (p < 0.001) distinguished admitted from resolved windows; alert intensity did not. In the pilot sub-cohort, patient-reported illness prognosis (Q21) was the dominant pre-admission signal (GEE {beta} = +0.058, AUC = 0.647, p-BH = 0.018). This finding did not replicate in the full cohort: Q21 was non-significant (GEE {beta} =- 0.008, p = 0.154, AUC = 0.507). Cross-cohort analysis identified selective curation of the pilot sub-cohort as the primary explanation. In the full cohort, six signals escalated significantly before admission after Benjamini-Hochberg correction: total alerts, health impairment (Q26), red alerts, self-rated health (Q3), patient concerns (Q1), and operator concern (Q34). Health impairment achieved the highest individual AUC (0.605) and showed the longest pre-admission lead. No individual signal exceeded AUC 0.61. ConclusionsTwo thirds of instability phases resolve without hospitalisation, providing direct empirical support for trajectory plasticity as a clinically frequent phenomenon. Within the same patient, persistence -- in duration and in the consistency of high-severity multi-domain flagging across calls -- distinguishes trajectories that tip into admission from those that resolve. The Q21 signal reversal between cohorts illustrates how selective curation can produce compelling but non-replicable findings in monitoring research. In the full population, objective alert signals and operator judgement, rather than patient illness prognosis, carry the pre-admission signal.

Background: Palliative care services improve quality of life and health outcomes for individuals living with chronic and life-limiting illnesses. Although these services have expanded considerably in urban areas, their availability remains limited in many rural communities. This study aimed to identify key components of integrated palliative care services and examine how these elements are implemented within rural healthcare systems in southern Minnesota. Methods: A qualitative case study using deductive content analysis was conducted. Semi-structured interviews were carried out with healthcare professionals involved in palliative and hospice care serving rural communities in southern Minnesota. Results: Participants identified several essential components of integrated palliative care, including multidisciplinary care teams, continuity of care across healthcare settings, interprofessional collaboration, and early identification of patients who may benefit from palliative care. Existing services in southern Minnesota incorporate several integrated elements, such as coordinated care teams, individualized care plans, nurse-led case management, professional training, and the use of virtual visits for geographically distant patients. However, participants also identified important gaps, including limited availability of palliative care services in rural areas, fragmented continuity of care, challenges in early patient identification, funding and insurance barriers, and the absence of a unified palliative care network. Conclusions: While palliative care services in southern Minnesota demonstrate important strengths, further efforts are required to improve service integration, coordination, and access for rural populations. Strengthening integrated PCSs may help reduce disparities in access to care and improve service delivery for rural patients and their families. These findings may inform the development of integrated palliative care models in rural healthcare systems beyond the study setting.