Journal of General Internal Medicine

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.

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 Therapeutic empathy improves patient and practitioner outcomes, yet existing measures are often lengthy, conceptually inconsistent, and cannot be easily compared across respondent groups. Brief, universal measures (usable by patients, practitioners, students, and observers) are lacking. We therefore developed a universal single-item scale and conducted psychometric testing of the patient-reported version. Methods Following best-practice, we used a three-phase approach: (1) item development; (2) pre-testing the scale by obtaining expert panel feedback (n=9) and conducting cognitive interviews with stakeholders (n=35); and (3) scale validation in an international patient sample (n=521) assessing convergent, discriminant, and known-groups validity. Validation involved assessing correlations with the Consultation and Relational Empathy (CARE) measure and clinical neutrality measure, and by assessing differences in scores by patient ethnicity. Results We developed two versions (pictorial and text-based) of each scale. Expert feedback and cognitive interviews confirmed content and face validity. Pictorial and text-based versions showed high convergent validity with the CARE measure (r=0.761 and r=0.838, both p<0.001), and discriminant validity with a clinical neutrality measure (r=0.131 and r=0.139, p=0.003 and p=0.001, respectively). Correlations with the CARE measure remained high (r>0.70) and statistically significant (p<0.001) across patient gender, ethnicity, and practitioner type. Ethnic minority patients rated practitioner empathy lower than White patients (pictorial p=0.057; text-based p=0.033), demonstrating known-groups validity. Patients rated doctors' empathy higher than other healthcare practitioners' (p=0.001 for both pictorial and text-based); there were no significant differences in empathy scores by patient gender. Conclusions We developed the first universal single-item therapeutic empathy measure and demonstrated validity for the patient-reported versions. The scale is brief, accessible, and applicable to clinical practice, education, and research. Further research should validate practitioner-, student-, and observer-reported versions, and assess predictive and cross-cultural validity. This robust tool can support patient-reported routine measurement of therapeutic empathy and contribute to improving patient and practitioner outcomes.

BackgroundAmid persistent structural barriers and recent national policy changes, early-career physician-scientists face mounting challenges that threaten the sustainability of the biomedical research pipeline in the United States. MethodsWe surveyed early career physician-scientists collecting demographic data, career development support, distribution of clinical and research responsibilities, funding, and perceived career challenges. The survey was distributed by email to the department chairs at 110 institutions in the United States. ResultsA total of 175 surveys were completed. About half 50.8% (n=89) of respondents received a career development award, with 28.9% of respondents reporting limited institutional/departmental support. The most reported challenges were balancing clinical, research, and educational responsibilities (72.5%, n=127); balancing work and family responsibilities (48%, n= 84); limited funding opportunities (48%, n=84); and under-compensation (34.3%, n=60). About 57.7% (n=101) of respondents had considered leaving academic medicine within the next two years, and 83.2% (n=139) indicated a >50% likelihood of doing so within five years. The most frequently cited reasons for attrition were funding challenges (72%, n=126), under-compensation (42.3%, n=74), feeling unhappy or stressed (40.6%, n=71), and burnout (37.7%, n=66). Furthermore, 43.9% (n=76) of respondents reported considering relocation outside the United States for better academic working conditions, and 10.4% (n=18) had already been contacted by institutions abroad. ConclusionEarly-career physician-scientists face substantial structural and financial challenges, with limited institutional support, high rates of burnout, and widespread intent to leave academia. These findings underscore an urgent need for sustained investment, targeted retention strategies, and policy reforms to stabilize and strengthen the physician-scientist workforce in the United States.

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 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.

ObjectivesAlarm fatigue is a patient safety concern in ICUs, yet no validated instrument exists to assess alarm fatigue among healthcare professionals in non-Western settings. This study aimed to cross-culturally adapt the Charite Alarm Fatigue Questionnaire (CAFQa) into Japanese and evaluate its reliability and validity among ICU nurses and physicians. MethodsThe Japanese CAFQa was cross-culturally adapted following the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) guidelines, including forward translation, back-translation, expert panel review, and cognitive interviews. A multicenter cross-sectional validation study was performed across eight ICUs at five hospitals in Japan. A total of 129 participants (103 nurses and 26 physicians) completed the Japanese CAFQa, the NIOSH Brief Job Stress Questionnaire, and the Insomnia Severity Index (ISI). Structural validity, internal consistency, test-retest reliability (n = 102), convergent validity, and known-groups validity were assessed. ResultsCFA confirmed the two-factor structure with acceptable fit (CFI = 0.922, RMSEA = 0.041, SRMR = 0.076), with standardized factor loadings ranging from 0.33 to 0.82. The two factors were not correlated (r = 0.05). Cronbachs alpha was 0.688 for the overall scale, 0.805 for Alarm Stress, and 0.649 for Alarm Coping. Test-retest ICCs ranged from 0.616 to 0.753. The CAFQa total score correlated with the NIOSH total (r = 0.261) and the ISI total (r = 0.338). Healthcare professionals with [&ge;]4 years of ICU experience had higher Alarm Coping scores than those with 1-3 years (median 7.0 vs 6.5), and physicians scored higher on Alarm Coping than nurses (median 8.0 vs 7.0). ConclusionsThe Japanese CAFQa demonstrated acceptable structural validity, reliability, and convergent and known-groups validity, providing the first validated tool for quantitatively measuring alarm fatigue in Japan. Implications for Clinical PracticeThe Japanese CAFQa enables ICU managers to quantify alarm fatigue at individual and unit levels, identify high-risk staff, and evaluate the effectiveness of alarm management interventions.

Background and ObjectivesPatient portals have become essential infrastructure for healthcare delivery following the 21st Century Cures Act, yet adoption remains inequitable. Understanding demographic and geographic determinants of portal activation is critical for addressing digital health disparities, particularly among neurology patients who face unique access barriers. We examined the demographic, geographic, and neighborhood-level factors associated with patient portal activation among neurology patients at multiple geographic scales in the Washington, DC metropolitan area. MethodsWe conducted a retrospective cohort study of 72,417 adult neurology patients seen at two academic medical centers sharing an electronic health record in Washington, DC (February 2021-February 2026). We examined portal activation using multivariable logistic regression and geographic analysis at four nested scales: the metropolitan catchment area, DCs eight wards, individual census tracts (via geocoded patient addresses), and individual DC residents. ResultsPortal activation was 64.7% overall. Activation varied by race/ethnicity (Non-Hispanic White 76.1%, Non-Hispanic Black 57.0%, Non-Hispanic Asian 57.6%, Hispanic 55.0%) and geography (DC Ward 2: 82.0% vs. Ward 7: 48.0%). Ward-level educational attainment (r = 0.948), broadband access (r = 0.889), and income (r = 0.811) were strongly correlated with activation. Within individual wards, Non-Hispanic White patients activated at 84-91% while Non-Hispanic Black patients activated at 48-64%, demonstrating that neighborhood resources alone do not explain disparities. DiscussionPatient portal activation is shaped by demographic, socioeconomic, and geographic factors operating at multiple levels. Persistent within-ward racial disparities indicate that geographically targeted interventions must be paired with culturally tailored approaches to achieve digital health equity.

BackgroundMobile health programs (MHPs) provide essential preventive services to uninsured and underserved communities. Following the 2024 regulatory approval of human papillomavirus (HPV) self-collection for cervical cancer screening, MHPs represent an access point for healthcare-based self-collection. However, little is known about patient perceptions of this approach in MHP and other healthcare settings. MethodsFrom May - August 2025, we surveyed individuals aged 25-65 years with a cervix who attended MHPs in Southern Arizona. The survey assessed interest in HPV self-collection, preferred locations, instructional preferences, and facilitators to attend follow-up after a positive result. Descriptive statistics summarized demographic characteristics and survey responses. ResultsFifteen female participants completed the survey (mean age 36 years). Ten (67%) identified as Hispanic or Latino, nine (60%) preferred Spanish, and 14 (93%) were uninsured. Interest in HPV self-collection was high, with ten (67%) very or extremely interested. Among those interested, nine (69%) preferred home-based self-collection, and four (31%) preferred clinic or MHP-based self-collection. Most common concerns regarding self-collection on the MHP were ensuring privacy (n=7; 47%) and knowing how to perform the test correctly (n=5; 33%). Most participants (n=11; 73%) reported being very or extremely confident they would attend follow-up after a positive result; language-concordant support, reminder calls, and scheduling assistance were the most endorsed facilitators. ConclusionHPV self-collection was highly acceptable among MHP attendees, although home-based self-collection was most preferred. Addressing privacy concerns, providing multiple modes of instruction, and offering navigation support may improve implementation success and ensure timely follow-up care in MHP settings.

Medical discrimination may alter how patients relate to health information sources following adverse care encounters. We examined whether discrimination experience is associated with selective erosion of institutional health trust and with compensatory digital health engagement, using nationally representative data from the Health Information National Trends Survey (HINTS) 6 (2022; n=6,252) and HINTS 7 (2024; n=7,278). Survey-weighted modified Poisson regression estimated prevalence ratios (PRs) for binary high-trust outcomes, and survey-weighted ordinary least squares estimated coefficients for continuous outcomes; jackknife replicate weights (50 replicates) provided variance estimates. Discrimination was associated with substantially lower probability of high trust in the healthcare system (PR=0.39; 95% CI 0.30-0.52) and physicians (PR=0.85; 95% CI 0.77-0.94), with no significant association for trust in scientists, government, family, or religious organisations. The clinical-institutional pattern replicated in HINTS 6, which additionally showed reduced trust in scientists for race/ethnicity-based discrimination. Contrary to a disengagement hypothesis, discrimination-exposed adults showed higher probability of online health information seeking (PR=1.06), health app use (PR=1.11), and online provider messaging (PR=1.13); these associations persisted after adjustment for trust in physicians. Discrimination was independently associated with lower health self-efficacy (b=-0.271). Medical discrimination selectively erodes trust in clinical institutions while leaving broader epistemic trust largely intact. Despite this, discrimination-exposed patients engage more actively with digital health channels, consistent with compensatory reorientation toward non-clinical information sources. These findings describe engaged but institutionally alienated patients, with implications for restoring clinical trust and for equity-centred digital health design.

IntroductionDuring the COVID-19 pandemic, incarcerated populations faced heightened risk of exposure due to healthcare barriers, restrictive environments, and pre-existing health conditions. Consequently, Correctional Officers (COs) faced increased risk of COVID-19 exposure. Given the health benefits of COVID-19 vaccination and the rise in vaccine hesitancy, this study examined the relationship between COs health beliefs and COVID-19 vaccine uptake. MethodsA health beliefs survey was administered to Massachusetts-based COs (n=118). Chi-squared Automatic Interaction Detection modeling and logistic regression was utilized to analyze the survey data. ResultsCOs with higher trust in vaccines and a prior positive COVID-19 test were most likely to get vaccinated voluntarily. Those with low trust in vaccines and no previous positive COVID-19 test were least likely to receive the vaccine. ConclusionDespite the severe impact of COVID-19 in correctional settings, and the evidence of vaccine efficacy against hospitalization and death, vaccine uptake among COs remains low.

BackgroundPersons 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. MethodThe 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 AHAs Essential 8 metrics for differences between HCH and CHC patient responses. ResultsHCH 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. ConclusionsCVD 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. Clinical PerspectiveO_ST_ABSWhat Is New?C_ST_ABSO_LIPatients accessing Health Care for the Homeless clinics demonstrate unique cardiovascular risk profiles characterized by higher rates of inadequate sleep, smoking history, and pre-diabetes compared to Community Health Center patients, even after adjusting for sociodemographic factors. C_LI What Are the Clinical Implications?O_LITraditional cardiovascular disease risk assessment tools and prevention strategies may need to be recalibrated for homeless populations, as standard clinical metrics and screening approaches may not fully capture the complex interplay of behavioral, environmental, and social exposures affecting this vulnerable group. C_LI Research PerspectiveO_ST_ABSWhat New Question Does This Study Raise?C_ST_ABSO_LIHow do structural inequities and comorbid conditions resulting in and from homelessness impact health in ways that may not be captured by conventional risk assessment tools? C_LI What Question Should be Addressed Next?O_LIWhat modifications to evidence-based cardiovascular interventions are needed to effectively serve people experiencing homelessness, and how can these interventions be integrated into Health Care for the Homeless clinics and other FQHCs? C_LI

IntroductionIntraoperative glycemic dysregulation, including unrecognized hypoglycemia and stress-induced hyperglycemia, is common during elective surgery. Conventional point-of-care (POC) monitoring provides only intermittent measurements, limiting the anesthesiologists ability to detect rapid glucose fluctuations. Continuous glucose monitoring (CGM) enables real-time, trend-based assessment, potentially shifting intraoperative glycemic management from reactive to proactive. ObjectiveTo meta-analyze the analytical accuracy, intraoperative glycemic efficacy, and feasibility of subcutaneous CGM in adults undergoing elective surgery, informing anesthesiology practice. MethodsThis systematic review and meta-analysis followed the PRISMA 2020 statement. Searches were conducted in PubMed, Embase, and Cochrane Central Register of Controlled Trials from January 2010 to May 2025. Eligible studies included randomized controlled trials and prospective cohorts of adults undergoing elective surgery under general or neuraxial anesthesia using subcutaneous CGM. Primary outcomes were pooled mean absolute relative difference (MARD) and time in range (TIR, 70-180 mg/dL). Random-effects models were applied. ResultsTen studies (3 RCTs, 7 cohorts; N=557) were included. Pooled MARD was 14.1% (95% CI 11.3-16.9%; I{superscript 2}=78%), lower in non-cardiac surgery (12.7%) than cardiac procedures with hypothermia (19.2%; p=0.03). CGM improved TIR by +14.9 percentage points (95% CI 7.2-22.6; p<0.001). Clinically significant hypoglycemia was detected in 43% of patients, all missed by POC. Sensor availability exceeded 96%, with no serious device-related events. ConclusionSubcutaneous CGM provides acceptable intraoperative accuracy and improves glycemic control, supporting its integration into anesthetic management.

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.

Background Home Test-to-Treat (HTTT) programs deliver timely antiviral treatment for acute respiratory infections, including COVID-19 and influenza, through at-home testing and telehealth. Because access is often measured by visit occurrence, variation in how and when care is delivered may be overlooked. We hypothesized that telehealth access follows distinct process-based patterns. Methods We analyzed de-identified encounters from the national HTTT program (September 2023-July 2024); 6,213 of 8,160 eligible individuals remained after exclusions for missing data. Phenotypes were derived by k-means clustering of standardized variables capturing encounter timing, modality preference, process duration, and sociodemographic and digital access attributes. Ten-day surveys assessed symptom duration and healthcare utilization. Results Three phenotypes emerged: Delayed/Disrupted Access (n = 1,537; 24.7%), Digitally Engaged but Socioeconomically Vulnerable (n = 1,460; 23.5%), and Mainstream Access and Efficient Utilization (n = 3,216; 51.8%). Mean process duration differed (15.93 [SD 3.84] vs 3.69 [3.31] vs 2.87 [2.41] hours; p < 0.001). Synchronous preference was lowest in the Digitally Engaged group (22.9%); antiviral prescribing was high (88.6%-91.9%). Among 10-day respondents (n = 1,023), symptom duration did not differ. Emergency department visits were most frequent in the Digitally Engaged group (2.3% vs 0.0% and 0.5%; p = 0.02) and urgent care in the Delayed/Disrupted group (5.8% vs 4.1% vs 2.0%; p = 0.02). Conclusions Telehealth use in a national HTTT program formed distinct phenotypes defined by timing, modality, and care-process efficiency. Evaluating equity requires attention to how and when care is delivered, not simply whether it occurred.

ImportancePhysicians routinely prognosticate to guide care delivery and shared decision making, particularly when caring for patients with critical illnesses. Yet, these physician estimates are prone to inaccuracy and uncertainty. Artificial intelligence, including large language models (LLMs), show promise in supporting or improving this prognostication. However, the performance of contemporary LLMs in prognosticating for the heterogeneous population of critically ill patients remains poorly understood. ObjectiveTo characterize and compare the performance of LLMs and physicians when predicting 6-month mortality for hospitalized adults who survived critical illness. DesignEmbedded mixed methods study with elicitation and comparison of prognostic estimates and reasoning from LLMs and practicing physicians. SettingThe publicly available, deidentified Medical Information Mart for Intensive Care (MIMIC)-IV v2.2 dataset. ParticipantsWe randomly selected 100 hospitalizations of adult survivors of critical illness. Four contemporary LLMs (Open AI GPT-4o, o3- and o4-mini, and DeepSeek-R1) and 7 physicians provided independent prognostic estimates for each case (1,100 total estimates; 400 LLM and 700 physician). Main outcomes and measuresFor each case, LLMs and physicians used the hospital discharge summary and demographics to predict 6-month mortality (yes/no) and provide their reasoning (free text). We assessed prognostic performance using accuracy, sensitivity, and specificity, and used inductive, qualitative content analysis to characterize reasonings. ResultsMean physician accuracy for predicting mortality was 70.1% (95% CI 63.7-76.4%), with sensitivity of 59.7% (95% CI 50.6-68.8%) and specificity of 80.6% (95% CI 71.7-88.2%). The top-performing LLM (OpenAI o4-mini) accuracy was 78.0% (95% CI 70.0-86.0%), with sensitivity of 80.0% (95% CI 67.4-90.2%) and specificity of 76.0% (95% CI 63.3-88.0%). The difference between mean physician and top-performing LLM accuracy was not statistically significant (p = 0.5). Qualitative analysis revealed similar patterns in LLM and physician expressed reasoning, except that physicians regularly and explicitly reported uncertainty while LLMs did not. Conclusion and RelevanceIn this study, LLMs and physicians achieved comparable, moderate performance in predicting 6-month mortality after critical illness, with similar patterns in expressed reasoning. Our findings suggest LLMs could be used to support prognostication in clinical practice but also raise safety concerns due to the lack of LLM uncertainty expression. KEY POINTSO_ST_ABSQuestionC_ST_ABSHow does large language model (LLM) prognostic accuracy and reasoning compare to physicians when predicting 6-month mortality for adult survivors of critical illness? FindingsIn this embedded mixed methods study, physicians and large language models had comparable, moderate prognostic accuracy with similar expressed reasoning patterns except that LLMs did not explicitly express uncertainty. MeaningLarge language models may be able to support physician prognostication, although the inability of LLMs to express uncertainty poses an important safety consideration.

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.

BackgroundComputed tomography (CT) is a cornerstone of timely diagnosis for stroke, trauma, and oncologic conditions, and delays in access are associated with worsened outcomes. Although Houston, Texas, is home to one of the worlds largest medical complexes, the geographic distribution of CT imaging infrastructure has not been systematically examined against neighborhood-level measures of socioeconomic vulnerability. MethodsWe conducted a cross-sectional geospatial analysis of CT imaging facilities across the Greater Houston metropolitan area. Facility locations -- including hospital-based scanners, independent imaging centers, and freestanding emergency facilities -- were compiled from publicly available imaging directories, Texas Department of State Health Services (DSHS) facility listings, Centers for Medicare & Medicaid Services (CMS) provider data, and CT location data contributed by MD Anderson Cancer Center. Census tract-level indicators (median household income, percent uninsured, poverty rate) were obtained from the U.S. Census Bureau American Community Survey. Facility locations were geocoded and overlaid on census-tract choropleths in ArcGIS Online and ArcGIS StoryMaps to identify tracts with elevated socioeconomic vulnerability and limited proximity to CT infrastructure. ResultsCT imaging facilities were markedly clustered in the central urban core and in higher-income corridors, with hospital-based and independent scanners concentrated in census tracts with lower poverty rates, higher median household income (>$119,300), and higher insurance coverage. Conversely, peripheral and southeastern tracts with elevated poverty (>24%), median household income below $37,800, and uninsured rates exceeding 16% contained comparatively sparse CT infrastructure, generating spatial "gaps" in advanced diagnostic capacity. The pattern persisted across facility type: freestanding emergency and independent imaging centers did not meaningfully compensate for the undersupply of hospital-based scanners in vulnerable communities. ConclusionsIn Houston, the spatial distribution of CT imaging resources mirrors rather than offsets underlying socioeconomic inequality. Neighborhoods with higher poverty and uninsured rates face compounded barriers of distance and coverage. Citywide spatial analysis renders these inequities visible in ways individual clinical encounters cannot, and supports equity-informed health-system planning, targeted investment in underserved catchments, and policies linking imaging-capacity expansion to measurable community need.

Objective To evaluate physician knowledge, attitudes, and practices regarding viral exanthem diagnosis and mandatory reporting requirements among practicing physicians in major metropolitan regions of Florida. Study Design An IRB-exempt cross-sectional survey was distributed via REDCap to licensed physicians and residents in family medicine, internal medicine, pediatrics, and infectious disease across Florida. The 19-question survey assessed demographic characteristics, knowledge of viral exanthem diagnosis (measles, rubella, roseola), reporting requirements, physician attitudes, and clinical practices. Knowledge scores were compared by specialty using ANOVA with Tukey post-hoc analysis. Multivariate analysis and linear regression assessed associations between physician confidence and knowledge scores. Results A total of 162 physicians responded, with 146 complete responses included in analysis. Participants included pediatrics (n=74), family medicine (n=48), and internal medicine (n=24). The overall mean knowledge score was 78.5% (SD 20.5). Pediatricians demonstrated the highest scores (82.7%) compared with internal medicine (76.4%) and family medicine (73.3%), with pediatricians scoring significantly higher than family physicians (p=0.04). Differences in vignette-based diagnostic knowledge and mandatory reporting knowledge were not statistically significant across specialties. Roseola was the most commonly diagnosed viral exanthem (66%), followed by measles (30%) and rubella (17%). Most physicians (91.4%) expressed interest in additional training. Conclusions Although overall physician knowledge of viral exanthem diagnosis and reporting was high, clinically meaningful gaps remain, particularly in differentiating similar rash presentations. Pediatricians demonstrated higher knowledge scores than family physicians. Enhanced physician education may improve diagnostic accuracy and public health reporting as vaccination rates decline and outbreaks of vaccine-preventable viral exanthems increase.

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.