Emergency Medicine Journal

Background Emergency Department (ED) overcrowding is associated with adverse outcomes including delays to treatment, patients leaving without being seen and increased mortality. Increasingly, evidence suggests that crowding may disproportionately affect those of lower socioeconomic status, worsening health inequalities. This study investigates the relationship between prolonged ED length of stay and vulnerable patient groups choosing not to wait to be seen. Methods A retrospective cohort study analysed monthly ED attendance data from a large district general hospital between January 2020 and July 2025. Patients who did not wait to be seen (DNW) were assessed for vulnerability based on age, presenting complaint and inferred frailty. Associations were explored using generalised estimation equation model with binary outcome. Results During the 66-month study period, 317,978 patients attended the ED, of whom 35,403 (11.1%) did not wait to be seen. Of these, just under one third met vulnerability criteria. As the percentage of patients staying in the ED for more than 8-hours increased, so did the proportion of patients leaving the department without being seen. With each 10% increase in LOS>8h percentage, the odds of any patient choosing not to wait increases by 48% (OR=1.48, 95% CI(1.47 to 1.49), p<0.001). Of the three vulnerability groups assessed, paediatric patients, those presenting with mental health complaints and frailty, frail patients were the most affected when lengths of stay in the ED increased and crowding became a problem (OR=1.73, 95% CI(1.72 to 1.74), p<0.001). Conclusion These findings demonstrate that while all patient groups are more likely to leave without being seen as crowding worsens, elderly and paediatric patient cohorts are significantly more likely to leave without being seen as crowding worsens. This raises genuine concerns about the accessibility of healthcare, in the presence of crowding, in these vulnerable groups.

Abstract Background The Simplified Geneva Score (SGS) incorporates two heart-rate thresholds. Multiple heart-rate measurements are commonly recorded during emergency department (ED) assessment. While the original SGS specifies use of mean heart rate, real-world implementation is unlikely to reflect this, and the effect of alternative heart-rate selection methods on score performance is unknown. Methods SCORES was a retrospective within-subject study of consecutive ED patients at a single urban hospital undergoing computed tomography pulmonary angiography (CTPA) for suspected pulmonary embolism (PE). Four SGS variants were calculated using the first, highest, mean, and lowest recorded pre-CTPA heart rate. Outcomes included score output, risk classification, diagnostic performance, reclassification flow, and downstream imaging utilisation. Precision estimates and comparisons were derived using empirical resampling techniques. Results 590 episodes were reviewed with 581 included for analysis. PE was confirmed in 50 cases (8.6%). Mean SGS differed significantly across all heart-rate implementations (p<0.0001). The proportion classified as high risk varied from 15% using the lowest heart rate to 40% using the highest heart rate. Reclassification across the SGS threshold was common, with 25% of episodes changing risk category unidirectionally between lowest and highest heart rate implementations. SGS(lowest HR) compared with SGS(mean HR) and SGS(highest HR) demonstrated higher specificity (87.6% vs 73.4% vs 62.1%), improved AUROC (0.744 vs 0.704 vs 0.659), and reduced downstream CTPA utilisation (818 vs 876 vs 920 scans per 100 PEs diagnosed), with no additional pulmonary emboli missed within the complete diagnostic pathway. Conclusions Heart-rate selection materially influences SGS output, risk classification, diagnostic performance, and imaging utilisation. In this retrospective cohort, use of the lowest recorded heart rate produced the most favourable overall balance of discrimination and diagnostic efficiency, supporting prospective validation.

Objective: Rural hospitals in the United States have fewer specialists, fewer critical care resources, and longer transport distances than urban hospitals, yet national studies disagree on whether rural patients are more likely to die after emergency care. The goal of this study was to determine whether short-term mortality differs between rural and urban patients receiving emergency or trauma care and to estimate the size of the difference. Methods: We conducted a systematic review and meta-analysis reported in line with PRISMA 2020. PubMed was searched on October 11, 2026, for studies published from 2010 onward, supplemented by web searching. Eligible studies were US observational studies that compared rural with urban patients or hospitals and reported mortality after emergency or trauma care. Odds ratios (ORs) were pooled with a DerSimonian-Laird random-effects model, with Hartung-Knapp confidence intervals (CIs) as a sensitivity analysis. Injury and trauma studies formed the primary analysis. Certainty of evidence was rated with GRADE. Results: Of 161 records identified, 14 studies were included and 12 were pooled. Across seven injury and trauma studies, rural setting was associated with higher odds of death (OR, 1.27; 95% CI, 1.07-1.52), with substantial heterogeneity (I^2 = 84%; 95% prediction interval, 0.74-2.19). Across all 12 studies, the pooled OR was 1.23 (95% CI, 1.09-1.40; I^2 = 96%). With Hartung-Knapp intervals, the overall estimate remained significant (95% CI, 1.05-1.45) and the trauma estimate was borderline (95% CI, 0.99-1.64). Study estimates ranged from 0.65 to 1.97. Three pediatric trauma studies gave a consistent estimate (OR, 1.32; 95% CI, 1.18-1.47; I^2 = 0%). Subgroup analyses by rurality definition, age group, and geographic scope did not explain the heterogeneity. Certainty of evidence was very low. Conclusions: In pooled US observational data, rural patients receiving emergency and trauma care had about 23% higher odds of short-term death than urban patients, most consistently among injured children. The evidence is inconsistent, rests on a single-database search with several reports assessed from abstracts only, and should be treated as preliminary. Why rural patients die more often, and at what point in the pathway from injury to definitive care, remains unanswered.

Background: The practice of hospitalizing emergency department (ED) patients with chest pain after ruling out acute myocardial infarction (AMI) varies widely. The long-term outcome and cost implications of this variation are uncertain. Methods: Analyzing 2007-2021 claims from a national commercial insurer, we identified 223,569 adults (18 years or older) ED visits with a principal diagnosis of chest pain without major secondary cardiopulmonary diagnoses, seen by 19,001 clinicians in 2,273 EDs. Within each ED, clinicians with the highest and lowest terciles of risk-adjusted hospitalization rates were classified as high- versus low-admitting. Primary outcomes were 30- and 180-day subsequent AMI hospitalizations. Secondary outcomes included 7-day cardiac testing, 30-day coronary intervention, and 30-day total and out-of-pocket costs. We estimated adjusted rate ratios (aRRs) and relative cost differences using generalized estimating equations, adjusting for patient and visit characteristics. Results: Patients had a mean age of 48.6 years, and 53.9% were female; measured characteristics were similar between groups. Compared with low-admitting clinicians, visits to high-admitting clinicians had higher 7-day cardiac testing (13.1% vs 10.8%; aRR 1.22, 95% CI 1.17-1.27) and 30-day coronary interventions (4.3% vs 3.8%; aRR 1.16, 95% CI 1.09-1.23). AMI hospitalization did not differ at 30 days (0.60% vs 0.60%; aRR 1.00, 95% CI 0.84-1.15) or 180 days (0.85% vs 0.87%; aRR 0.97, 95% CI 0.85-1.10). Thirty-day total costs were higher after visits to high-admitting clinicians (relative change 11.3%, 95% CI 8.8-13.8), and out-of-pocket costs $1,000 or more were more common (32.0% vs 29.6%; relative change 8.2%, 95% CI 6.1-10.3). Conclusions: Higher clinician hospitalizing tendency for ED chest pain is associated with greater downstream testing, more coronary interventions, and higher costs without lower short- or intermediate-term AMI risk. Reducing marginal admissions among high-admitting clinicians may decrease spending and practice variation with minimal impact on AMI outcomes.

Background Traumatic brain injury (TBI) is a major cause of morbidity and mortality in low- and middle-income countries (LMICs), where the burden of injury is disproportionately high, and healthcare resources are often limited. Clinical Practice Guidelines (CPGs) improve the consistency and quality of TBI care by shifting medical decision-making from individual judgement to evidence-based recommendations. The strong link between improved TBI outcomes and CPG adoption has largely been established in high-income country (HIC) hospital settings; evidence on implementing TBI CPGs in LMIC hospital settings is limited. Objective This study explored healthcare providers experiences to identify barriers and facilitators to implementing a TBI CPG in the Emergency Department (EMD) at Kilimanjaro Christian Medical Center in Northern Tanzania, guided by the Consolidated Framework for Implementation Research 2.0 (CFIR). Methods We conducted semi-structured in-depth interviews (IDIs) with doctors and nurses who regularly managed TBI patients at the EMD. We purposively sampled participants between June and August 2023. The research team analysed data using thematic content analysis guided by CFIR domains, developing codes and memos iteratively. Results 7 nurses and 8 doctors with an average of 5 years of experience caring for patients in the EMD completed in-person, in-depth interviews. Most providers reported frequent exposure to TBI cases and regular use of the CPG in routine clinical practice. However, not all participants reported consistent CPG use, highlighting variation in implementation among providers. Key facilitators included strong interprofessional teamwork, readily accessible guideline materials in clinical areas, availability of diagnostic imaging, and perceived improvements in patient outcomes. Barriers to consistent adherence included ICU bed shortages, limited operating theatre capacity, equipment and medication stock-outs, high patient volumes, staffing pressures, and a lack of structured refresher training or formal audit and feedback mechanisms. Variability in procedural confidence, particularly among less experienced providers, also affected implementation consistency. Conclusion Our findings show that a TBI CPG is well accepted and largely integrated into routine care among experienced providers. However, sustainability depends on strengthening training, mentorship, audit, and feedback systems for CPG use and on addressing system-level resource constraints. Institutional investment in capacity building is essential to ensure consistent and sustained CPG uptake in our setting.

Background: During emergency tracheal intubation, clinicians use waveform capnography or colorimetric carbon dioxide (CO2) detection to confirm exhaled CO2 from the endotracheal tube to determine its location. Both devices reliably yield a negative result when the tube is in the esophagus. However, in most emergency intubations the tube is correctly located in the trachea, and in this circumstance the relative diagnostic accuracy of the two devices is uncertain. If one device more often fails to detect exhaled CO2 despite correct tube placement (a false negative result), this could lead to unnecessary tube removal, repeated intubation attempts, and an increased risk of serious complications. Research Question: Among critically ill adults undergoing emergency tracheal intubation, is waveform capnography or colorimetric CO2 detection more sensitive for identifying an endotracheal tube correctly placed in the trachea? Study Design and Methods: The WAVE study is a pragmatic, prospective, paired diagnostic accuracy study conducted in emergency departments and intensive care units in the United States. The study compares waveform capnography and colorimetric CO2 detection (index tests) against a non-CO2-based reference standard for endotracheal tube location among 2,092 critically ill adults undergoing tracheal intubation. The primary outcome is the sensitivity of each index test for identifying a tube located in the trachea. The secondary outcome is specificity for identifying a tube located outside the trachea. Results: Enrollment began on April 21, 2025 and is ongoing, with completion anticipated in 2026. Interpretation: The WAVE study will provide prospective evidence comparing waveform capnography with colorimetric CO2 detection for determination of endotracheal tube location in critically ill adults. These findings may inform clinical practice and guideline recommendations regarding the optimal test of exhaled CO2 for use in the ED and ICU. Prespecifying the protocol and statistical analysis plan before completion of enrollment enhances the rigor, reproducibility, and transparency of the study.

Background Interruptions are common in hospital outpatient departments (OPDs) and are linked to error. Many are clinically necessary, so the aim is to regulate them. Most interventions target the individual clinician. Little is known about how doctors manage interruptions in practice or what makes their strategies work. Methods Qualitative study using focus groups and interviews with 42 doctors working in UK National Health Service OPDs (19 sessions, September to November 2025). Participants were purposively sampled by clinical experience and self-reported patient complexity. Data were analysed using Framework Analysis, informed by Cognitive Load Theory and Social Identity Theory. Results We developed four themes. (1) Pre-emptive protection of attention. Doctors withdrew, signalled unavailability and set thresholds for interrupting. These depended on colleagues learning and sharing the norm, which staff rotation eroded. (2) Sharing the burden through team coordination. Designated roles and time-limited rotas moved interruptions to colleagues with capacity set aside for them and made interrupting legitimate. (3) Real-time cognitive repair. Doctors asked interrupters to wait while they secured the task, and rebuilt the consultation on return, sometimes with the patient's help. Asking a colleague to wait depended on seniority or team membership; some trainees described difficulty doing this with senior colleagues. (4) Organisational recognition as the precondition for sustainable regulation. Where supervision was built into job plans, the interruptions it generated were expected and doctors held capacity in reserve for them. Advice, vetting and telephone queries were rarely counted as work. Conclusions Each strategy served a cognitive purpose, and social and organisational conditions decided whether it was perceived to work. Interventions could make norms shared and roles explicit, count interruption work in job plans and keep routes open for junior staff to raise concerns. Evaluations should ask who is able to use them.

Background While overall application volume by ethnic minority doctors into UK postgraduate medical training has expanded significantly, workforce diversity varies substantially across specialties. Previous reports have suggested that doctors from certain ethnic backgrounds and international medical graduates (IMGs) experience lower likelihoods of securing training posts, but granular longitudinal evaluations across all specialty training levels and recruitment stages remained limited. Therefore, this study evaluates longitudinal trends in self-reported ethnicity and country of qualification across postgraduate medical training in England between 2021 and 2025, maps specialty representation, and tests the hypothesis that recruitment success rates differ significantly between ethnic minority and White baseline applicants. Methods We conducted a retrospective national longitudinal analysis of mandatory NHS specialty recruitment data across England from 2021 to 2025, encompassing over 300,000 applications across ST1/CT1 (entry-level) and ST3+ (higher specialty) pathways. Applicants were categorised into census-aligned broad and granular ethnic groups, alongside IMG classifications. Unadjusted odds ratios (ORs) with 95% confidence intervals were calculated using White graduates and internal sub-ethnic baselines as comparators. Specialty-level ecological correlations (Spearman's rho) were performed to evaluate whether international workforce reliance moderated ethnic recruitment disparities. Results Between 2021 and 2025, applicant volume expanded rapidly, with ethnic minority applicants and IMGs comprising numerical majorities. However, Asian and Black applicants experienced substantially lower odds of receiving training offers compared to White baseline applicants across multiple high-volume specialties (e.g., emergency medicine and anaesthetics at ST1, where Black applicants faced multi-fold reductions in success odds: OR 0.03 to 0.05, p<0.001). Granular analyses revealed notable intra-ethnic heterogeneity, including lower success among Pakistani and Bangladeshi applicants compared to Indian applicants. Furthermore, UK graduates maintained higher offer success than IMGs across most specialties. Ecological correlations demonstrated that higher international applicant reliance was moderately and significantly associated with wider recruitment disparities at higher training levels for Black applicants {rho}=-0.43, p=0.019). Conclusions Centralised medical recruitment in England exhibits some persistent ethnic and international qualification disparities despite rising application diversity. The findings highlight the necessity of disaggregating ethnic groups and addressing subjective evaluation vulnerabilities within multi-stage recruitment processes to foster equity. Trial registration: Not applicable

Objectives To examine the prevalence of interruptions during UK secondary care outpatient consultations, their association with clinician mental workload, and the strategies clinicians use to manage them. Design Nationwide cross-sectional online survey. Setting Secondary care outpatient clinics, United Kingdom. Participants A sample of 465 UK secondary care doctors across a range of grades and specialties, recruited through a medical defence organisation's network. Primary and secondary outcome measures Mental workload (NASA Task Load Index, Raw-TLX); interruption frequency; perceived cognitive impact of interruptions; task resumption difficulty; perceived social effects on colleague interactions and team cohesion; and interruption-management strategy use. Results 61.1% of clinicians reported being interrupted often or very often, and mean overall workload was high (Raw-TLX 66.2, SD 11.5). Interruption frequency, patient complexity and perceived cognitive impact were each associated with higher workload across multiple NASA-TLX subscales. In fully adjusted models, interruption frequency retained independent associations with overall workload (beta = 1.79, p = 0.004) and temporal demand (beta = 0.94, p < 0.001). Task resumption difficulty was associated with worse perceived performance (beta = -0.59, p = 0.014), while perceiving interruptions as socially positive was associated with better performance (beta = 0.75, p = 0.003) and lower frustration (beta = -1.16, p < 0.001). Years of experience showed only small effects, and self-reported strategy use (38.7%) was not consistently associated with lower workload. Exploratory natural language processing of open-text descriptions identified three strategy clusters; cognitive offloading, anticipating and preparing, and blunt responses, with distinct emotional profiles. Conclusions Interruptions were commonly reported during UK secondary care outpatient consultations and were associated with measurable cognitive and temporal burden. The perceived social framing of interruptions was independently associated with frustration and self-rated performance. Binary strategy use showed no consistent workload association, indicating a need for system-level approaches addressing interruption frequency, task resumption support and the social context of interruptions rather than reliance on individual coping.

Background Best practice alerts (BPAs) used in emergency departments (EDs) to identify patients with possible sepsis lack specificity and some ED patients with sepsis BPAs are discharged. We evaluated factors associated with ED discharge following a sepsis BPA and with subsequent 7-day return admissions with sepsis or infection. Methods We conducted a retrospective cohort study using an electronic health record data query from five hospitals (2022-2024). Using logistic regression, we evaluated the association of participant demographics and clinical characteristics with ED discharge and factors at the initial ED visit associated with return admission. Results Among 62,068 ED visits with a sepsis BPA, one third resulted in ED discharge. Discharged participants were younger and more often female, Black, multiracial, and/or Hispanic; they more often had an initially normal pulse, respiratory rate, blood pressure, and lactic acid than admitted participants. Discharged participants more often had a fever on arrival (adjusted odds ratio (aOR) 6.03; p<0.001) and a white blood cell (WBC) count 11,000-14,999 (aOR 4.57; p<0.001) or 15,000-19,999/uL (aOR 2.01; p<0.001). One percent of discharged participants experienced a 7-day return admission with a diagnosis of sepsis or infection, which was associated with older age, White or Asian race, diastolic hypotension, WBC count [&le;]15,000/uL or <4,000/uL, and an index visit BPA response indicating sepsis or non-response. Conclusions Return admissions among discharged ED patients with a sepsis BPA are rare. Several factors are associated both with hospital admission at the index visit and return admission, indicating a need for additional risk-stratification tools.

Background Hospital outpatient departments (OPDs) deliver specialist care at scale, under close scrutiny over waiting times. Interruptions are common in this setting and are linked to error. Little is known about how they cause harm, for whom, and under what conditions. Methods Qualitative study using focus groups and interviews with 42 doctors working in UK National Health Service OPDs (19 sessions, September to November 2025). Participants were purposively sampled by clinical experience and self-reported patient complexity. Data were analysed using the Framework Method, informed by Cognitive Load Theory and Social Identity Theory, with independent double coding of 10% of transcripts (kappa = 0.97) and participant validation. Results We developed four themes. (1) Interruptions as a structural and increasingly ambiguous feature of clinical work. Interruptions were a normal part of clinical coordination, but new communication channels lacked shared norms about urgency, so each doctor had to judge what mattered. (2) The hidden cognitive cost of interruptions. The main cost came from returning to the interrupted task. This cost grew with complexity and was only partly offset by experience. (3) The social fabric of interruption burden. Transient teams weakened the norms that regulate interrupting, and approachability and hierarchy concentrated the burden on particular doctors. (4) The social amplification of cognitive burden. Understaffing multiplied cognitive costs. Doctors used familiarity and track record to decide which requests deserved attention, so concerns from junior or unfamiliar colleagues risked being discounted. Conclusions Interruptions are structural, and their impact depends on cognitive and social conditions. Reducing interruption frequency alone may miss the main risk. Interventions should support task resumption, protect capacity at busy times, rebuild shared communication norms and take account of the receiver's capacity when designing escalation.

Objectives: Heatwaves drive surges in acute care demand, yet heat-health action plans (HHAPs) lack tools to scale emergency care responses. This study validated a forecasting tool for heatwave-related emergency department (ED) demand across four clusters comprising the emergency care network of Ticino Canton, Switzerland. Methods: This retrospective, ecological, time-series study modeled daily mean temperature and ED presentations using a distributed lag non-linear model framework with autoregressive and calendar adjustment components. The model was trained on summers (June-August) 2016-2024 and tested on summer 2025, defining heatwaves as daily mean temperature >25 {degrees}C for [&ge;]3 consecutive days. Results: A non-linear temperature-demand association was shown across the clusters, with ED visits increasing at higher temperatures. Model fit varied across clusters (R2 0.389-0.672), and the reference model consistently outperformed naive baselines. During two heatwaves in 2025, prediction errors remained within acceptable ranges. Baseline demand, heatwave occurrence, and model performance varied substantially within the canton. Conclusions: These findings have implications for the design of HHAPs, suggesting that emergency care preparedness should rely on locally calibrated demand forecast rather than standardized activation protocols.

BackgroundIn rural Alaska, where most communities lack road access, air ambulances serve as a critical link between remote communities and hospital services. ObjectiveTo examine how air ambulance utilization decisions are made within the rural Alaskan health organization ecosystem. MethodsWe conducted semi-structured interviews with five key stakeholders. Transcripts were analyzed thematically using the Practical, Robust Implementation and Sustainability Model (PRISM). ResultsFour themes emerged that collectively define the structure of the air ambulance decision-making system, each reflecting interactions across multiple PRISM domains: limited formal guidance, logistical constraints affecting transport options, variability driven by communication and clinician risk tolerance, and reliance on unwritten rules. Together, these describe decision-making in a limited information state with incomplete feedback, leading to experiential and locally evolved practices. ConclusionAir ambulance use reflects clinical-logistical constraints rather than critical illness alone. Linking transport decisions to outcomes and developing structured decision support may improve consistency and optimize care delivery.

Abstract Importance Patients with behavioral health presentations leave the emergency department (ED) before evaluation more often than other patients, but outcomes after leaving without being seen (LWBS) have not been described in this population. Objective To compare return visits, admission, and mortality after LWBS vs completed care among behavioral health ED episodes. Design, Setting, and Participants This retrospective episode-level cohort study used a multisystem deidentified electronic health record database of US health systems (data snapshot June 4, 2026). Participants were patients of all ages with an ED visit and same-day behavioral health diagnosis. Index visits (recorded January 26, 2001 to April 13, 2025) each opened a 30-day episode window; in-window were counted as outcomes, and patients could contribute episodes to either group. Data were analyzed in October 2026. Exposure LWBS (diagnosis code Z53.21 on a date with an ED encounter) vs completed care ending in discharge home. Main Outcomes and Measures The primary outcome was ED return visit within 7 days. Secondary outcomes were return visit within 3 and 30 days; admission on day 0, on days 1-7 (principal measure), and within 30 days; and all-cause death within 7 and 30 days, assessed among episodes from 2017 through 2023 using a verified death-record definition. Results The analysis included 1,305,812 episodes (12,248 LWBS; 1,293,564 completed care) from 525,490 patients; 666,930 episodes (51.1%) involved female patients and 66,733 (5.1%) involved patients younger than 18 years. Return visit within 7 days followed 24.5% of LWBS episodes vs 13.2% of completed-care episodes (risk ratio [RR], 1.86; 95% CI, 1.81-1.92), and admission on days 1-7 followed 17.1% vs 3.9% (RR, 4.41; 95% CI, 4.24-4.59). Among 2017-2023 episodes, 30-day mortality was 0.69% (70 of 10,141) after LWBS vs 0.32% (2,426 of 756,140) after completed care (RR, 2.15; 95% CI, 1.70-2.73). Results were similar under alternative episode definitions. Conclusions and Relevance Among patients with behavioral health ED presentations, LWBS was followed by higher rates of return visits, admission, and short-term mortality than completed care. These unadjusted findings describe outcome burden, not causal effects, and highlight patients who leave as a target for rapid follow-up.

PurposeRural Alaska relies on air ambulances ("medevacs") to connect remote villages off the road system to higher-level care. Yet medevac decision-making is complicated by limited guidelines, high costs, and delayed transport. We aimed to quantify variability in physician medevac decisions and identify contributing factors. MethodsWe conducted a cross-sectional survey of physicians (n=20) within the Alaska Tribal Health System using 20 clinical vignettes representing varying levels of transport complexity. Participants selected transport modality (medevac, commercial, or remain in village) and rated confidence. Interrater agreement was assessed using Gwets AC1. A generalized linear mixed model evaluated predictors of medevac use. FindingsAgreement was "almost perfect" for clearly critical cases ("always medevac," AC1=0.97) but declined substantially for intermediate scenarios ("possible medevac" AC1=0.39; "never medevac" AC1=0.26; "special considerations" AC1=0.12). Confidence strongly correlated with agreement (r=0.83, p<0.001), forming distinct clusters of high-, medium-, and low-certainty decisions. Vignette type was the dominant driver of medevac use (OR 455.5 for "always medevac"), but decisions did not change with experience (OR=0.99, 0.94-1.04). Between-physician variability persisted after adjustment (ICC 3.8%; MOR 1.67), consistent with differences in risk tolerance. ConclusionsMedevac decision-making is highly consistent in certain clinical cases but interrater agreement is limited for intermediate cases. Without a shared gold standard of "correct" medevac decisions, future clinical decision support cannot rely on automated medevac decision making. Efforts to improve care should focus on deconstructing decisions, enhancing outcome feedback, and developing targeted decision support rather than prescriptive decision models.

Introduction: Hospitals face increasing financial pressures. Hospital reimbursement is tied to the Case Mix Index (CMI). The CMI is a measure of complexity of care provided by the hospital and the total reimbursement is the CMI multiplied by a base pay factor, multiplied by the total number of discharges from the hospital. The few studies in the literature have involved logistically complex attempts to improve documentation. We sought to determine whether limited, structured chart review by surgeons can improve CMI in a multihospital system. Methods: We performed a prospective, observational study in a four-hospital academic medical system on all surgical patients from 10/2022 to 7/2026. Two clinically busy trauma surgeons performed limited structured chart reviews for select surgical patients. In the limited intervention period (1/2024-9/2024) this focused only on the trauma division. In the full intervention period (10/2024-7/2026) expanded to all surgical patients across the system. The main outcome was surgical case mix index (CMI). Results: A total of 35,695 patients were included (13,811 pre-intervention, 9,337 limited intervention, 12,547 full intervention). CMI increased significantly among surgical patients across the whole system (2.68 (95%CI 2.65-2.71) to 2.87 (95%CI 2.82-2.92) to 2.97 (95%CI 2.93-3.01), ANOVA p <0.001). Overall length of stay was unchanged among surgical patients across the whole system. The estimated increase in revenue per patient attributable to this intervention was $3,912 in surgical patients across the system. Conclusion: Having trauma surgeons review targeted surgical patients for high value documentation capture significantly enhanced the CMI and estimated revenue of surgical services across a multi-hospital integrated health system. Such documentation reviews may represent a practical method to improve hospital documentation and revenue.

Objectives To investigate the association between time spent in NHS Type 1 emergency departments (EDs, consultant led 24 hour services with full resuscitation facilities) and risk of leaving employment and hospitalisation after leaving the department alive up to two years after attendance. Methods We conducted a retrospective cohort study using linked administrative data from the NHS Emergency Care Dataset, Hospital Episode Statistics, Census 2021, and mortality records. ED length of stay was categorised from under 2 hours to 12 hours or more. Cox proportional hazards models were fitted to estimate the association between ED length of stay and subsequently leaving employment or being hospitalised over a two year follow-up period, adjusting for demographic, socioeconomic, and clinical characteristics. Results Our study population comprised 3,347,008 individuals aged 25 to 64 who attended a Type 1 ED in England between April 2021 and March 2023. For leaving employment, adjusted hazard ratios (aHRs) increased from 1.04 (95% CI 1.03 to 1.06) for stays of 2 to <4 hours to 1.21 (1.17 to 1.24) for 12 hours or more, compared with stays under 2 hours. A similar but more pronounced gradient was observed for post-discharge hospitalisation, with aHRs rising from 1.06 (1.04 to 1.07) at 2 to <4 hours to 1.95 (1.91 to 1.99) for stays of 12 hours or more. Conclusions Prolonged time spent in the ED is associated with poorer outcomes among working-age employees, including reduced labour market participation and increased hospital use. These findings suggest that long ED stays may have wider and longer-term implications beyond immediate clinical outcomes, including potentially for national labour markets and economic growth.

Background: Intracerebral hemorrhage (ICH) is associated with high early mortality. Although routine coagulation markers are clinically relevant, their incremental value for mortality prediction beyond neurologic and physiologic information is uncertain. We developed and externally validated models using first-24-hour ICU data and evaluated the added predictive value of routine coagulation measurements. Methods: We used MIMIC-IV for model development and eICU-CRD for external validation. The development cohort included 2,669 adults with non-traumatic ICH and 388 ICU deaths. Logistic regression, random forest, and XGBoost were evaluated using a patient-level 70:30 holdout split and 5-fold stratified cross-validation. External validation used a high-specificity primary eICU cohort (396 patients; 39 deaths) and a broader secondary hemorrhagic-stroke/likely-ICH cohort (1,555 patients; 168 deaths). Performance was assessed using AUROC, AUPRC, Brier score, calibration, and paired bootstrap comparisons. GCS-only and age-plus-GCS models served as clinical benchmarks. Coagulation-only, clinical-only, and combined XGBoost models were compared to assess incremental value. Results: In the internal holdout cohort, AUROCs were 0.870 for logistic regression, 0.859 for random forest, and 0.867 for XGBoost. In the primary external cohort, corresponding AUROCs were 0.804, 0.839, and 0.817; AUPRCs were 0.342, 0.419, and 0.406; and Brier scores were 0.080, 0.072, and 0.074. In the broader cohort, AUROCs were 0.826, 0.848, and 0.838. Random forest showed the numerically strongest external performance, but pairwise AUROC differences among multivariable models were not clearly supported. GCS-only achieved AUROCs of 0.780 and 0.813 in the primary and broader cohorts. Coagulation-only XGBoost showed weak discrimination (AUROC 0.598 internally, 0.598 primary external, and 0.566 broader external). Adding coagulation variables to the clinical model produced AUROC differences of 0.014 (95% CI -0.009 to 0.037) and 0.003 (95% CI -0.007 to 0.013) in the two external cohorts. Conclusions: Multivariable models based on first-24-hour ICU data retained useful discrimination across external cohorts and provided modest gains over simple GCS-based benchmarks. Routine coagulation markers alone were weak predictors and did not provide clearly supported incremental discrimination beyond broader clinical information. Cross-database transportability should be interpreted together with phenotype alignment and calibration.

Objectives: To assess the feasibility and early performance of an AI-assisted screening workflow that reviews inpatient discharge summaries to identify potential treatment injuries for clinician review and, where appropriate, Accident Compensation Corporation (ACC) treatment injury claim consideration in New Zealand. Design: Single-site, six-month pilot reported as a quality-improvement/service-evaluation activity. A large language model (LLM)-enabled system acted as an index screening test over discharge summaries to generate candidate treatment injury signals for structured clinician adjudication and escalation. Setting: A New Zealand district hospital (Wairarapa). Screening operated on routinely produced inpatient discharge summaries. Participants: All inpatient discharge summaries produced during the pilot period were screened (n = 2,642). The unit of analysis was one discharge summary per inpatient episode. Intervention: An LLM-based screening workflow that outputs structured candidate harm signals with supporting excerpts and a certainty label. Outputs were reviewed by clinicians using a structured adjudication template. Suitable cases could be escalated to senior clinicians and followed through the local ACC treatment injury process. Main outcome measures: Primary outcomes were yield, positive predictive value (PPV) of AI- flagged signals for clinician-adjudicated iatrogenic harm, and the rate of unsupported/hallucinated outputs. Secondary outcomes included overlap with incident reporting and administrative coding, downstream ACC claim outcomes, and clinician review workload. Results: Across 2,642 inpatient discharge summaries screened, the AI-assisted workflow generated 237 candidate signals (yield 9.0%; 95% CI 7.9% to 10.1%). Clinician adjudication classified 102 as confirmed iatrogenic harm, 43 as possible harm requiring additional context, 81 as no harm, and 11 as unsupported/hallucinated outputs. PPV for confirmed harm was 43.0% (95% CI 36.8% to 49.4%). PPV for confirmed-or-possible harm was 61.2% (95% CI 54.9% to 67.1%). Seven ACC treatment injury claims were lodged as a direct result of AI-assisted surfacing; three were accepted, two declined, and two were held/pending at analysis. Organisational ACC data identified 68 treatment injury claims lodged during the same period, of which 17 overlapped with AI-identified cases. Conclusions: AI-assisted screening of discharge summaries can feasibly increase detection of potential treatment injuries for structured clinician review and may support more systematic ACC consideration and safety learning. Safe scaling requires transparent reporting of model and prompt details, robust human adjudication and escalation pathways, and governance controls to mitigate false positives and unsupported outputs. A next-stage evaluation should estimate diagnostic accuracy against a chart-review reference standard and examine implementation outcomes including workload, acceptability, fidelity and sustainability.

Background. Prediction models after stroke increasingly add neuroimaging information to bedside data. Whether findings documented in radiology reports improve short term risk stratification beyond a patient's evolving physiological state is unknown. Methods. We studied 4,827 adults with acute ischaemic stroke, intracerebral haemorrhage or subarachnoid haemorrhage in MIMIC IV (2008 to 2019). At eight landmarks from 6 to 48 hours after ICU admission, models ranked patients by their risk of deterioration or death within 24 hours. The baseline model combined demographic and clinical characteristics, a previously validated dynamic ICU state representation, and the physiology, treatment and imaging status at each landmark. A locally run language model extracted imaging phenotypes from 6,992 head CT and MRI reports; three phenotypes and four location categories met a pre specified reliability criterion for automated extraction in independent validation and entered the model. The primary outcome was the difference in the proportion of events captured among the 10% of patients ranked highest at each landmark, in temporal validation (2017 to 2019; 8,923 patient landmarks, 666 events). Results. Among the highest ranked 10%, adding imaging phenotypes captured no additional event (348 of 666 with and without them): a difference of 0.00 percentage points (95% CI -2.20 to +2.01). Discrimination (AUROC 0.855 vs 0.849), calibration and net benefit were similar. No prespecified sensitivity analysis, stroke subtype or availability-adjusted analysis showed statistically supported incremental capture. In a post hoc redundancy analysis, imaging phenotypes improved capture when added to demographic and clinical characteristics alone (+12.91 points, 7.54 to 17.81), but this gain was no longer evident once the dynamic state representation was included. Conclusions. Neuroimaging phenotypes derived from routine radiology reports were associated with short-term deterioration, but we found no evidence that they added risk stratification value beyond the dynamic ICU state and contemporaneous physiology.