Abstract Out-of-hospital cardiac arrest (OHCA) impacts public health, with variable survival across the US. This study used a population-based risk adjustment model to understand factors influencing regional variability in OHCA survival to hospital discharge. We evaluated 202,406 OHCA cases from 2013-2015 Medicare Fee-For-Service claims across 205 hospital regions. A matched cohort from the Cardiac Arrest Registry to Enhance Survival (CARES) and Medicare claims was used to develop logistic regression models predicting survival. Standardized Incidence Ratios (SIRs) identified regions performing better or worse than expected. Of 205 regions, 101 (49.3%) demonstrated lower-than-expected risk-adjusted survival, while only 9 (4.4%) had higher-than-expected survival. Overperforming regions had smaller populations, higher proportions of residents aged 65 + , and more large hospitals (400+ beds). Hospitals with ≥100 beds were more likely in overperforming regions, while cardiac catheterization capability showed inverse association. These nationwide disparities highlight the need for targeted interventions and regionalized care approaches to improve survival rates.

Importance:Hispanic and non-Hispanic Black patients with ST-segment elevation myocardial infarction (STEMI) are less likely than White non-Hispanic patients to receive guideline-recommended percutaneous coronary intervention (PCI). Research suggests disparities arise before and during STEMI treatment, but it is unclear when the largest disparities in PCI emerge. Objective:To assess when in the care process the largest disparities in PCI receipt occur in patients with STEMI presenting to an emergency department. Design, Setting, and Participants:This cross-sectional study evaluated adult patients with STEMI presenting to Florida hospitals from January 1, 2011, to December 31, 2021. Data were analyzed from June 29, 2023, to May 29, 2025. Exposure:Patient race and ethnicity. Main Outcomes and Measures:The main outcomes were presentation to PCI-capable hospitals, receipt of PCI if initially presenting to PCI-capable hospitals, transfer if initially presenting to non-PCI capable hospitals, and receipt of PCI at receiving hospital if transferred. Logistic regression was used to compare outcomes for patients with STEMI by race and ethnicity, controlling for payer, age, sex, weekend presentation, time of presentation, comorbidities, and hospital characteristics. Results:Among 139 629 patients with STEMI included in the analysis, 68.81% were male. Mean (SD) age was 64.4 (13.0) years. A total of 9.09% identified as Black, 15.17% as Hispanic, 70.56% as White, and 5.17% as other or missing race. In adjusted analyses, Black (-1.8 [95% CI, -2.6 to 1.1] percentage points [pp]) and Hispanic (-3.1 [95% CI, -3.7 to -2.4] pp) patients were less likely than White patients to present to PCI-capable hospitals (P < .001 for both). Among patients initially presenting to PCI-capable hospitals, Black patients were less likely to receive PCI than White patients (-8.6 [95% CI, -9.5 to -7.7] pp; P < .001). Among patients initially presenting to non-PCI-capable hospitals, Black (-4.0 [95% CI, -6.4 to -1.5] pp; P = .001) and Hispanic (-4.2 [95% CI, -6.3 to -2.0] pp; P < .001) patients were less likely to be transferred than White patients. Among transferred patients, Black patients were less likely to undergo PCI at the receiving hospital than White patients (-13.3 [95% CI, -16.6 to -9.9] pp; P < .001). Conclusions and Relevance:In this cross-sectional study examining racial and ethnic disparities in receipt of PCI for patients with STEMI, racial and ethnic disparities persisted throughout the care process. The largest magnitude of disparity was PCI receipt if transferred, but the disparity with the largest impact was PCI receipt when initially presenting to PCI-capable hospitals.

Importance. Discriminatory language in clinical documentation impacts patient care and reinforces systemic biases. Scalable tools to detect and mitigate this are needed. Objective. Determine utility of a frontier large language model (GPT-4) in identifying and categorizing biased language and evaluate its suggestions for debiasing. Design. Cross-sectional study analyzing emergency department (ED) notes from the Mount Sinai Health System (MSHS) and discharge notes from MIMIC-IV. Setting. MSHS, a large urban healthcare system, and MIMIC-IV, a public dataset. Participants. We randomly selected 50,000 ED medical and nursing notes from 230,967 MSHS 2023 adult ED visiting patients, and 500 randomly selected discharge notes from 145,915 patients in MIMIC-IV database. One note was selected for each unique patient. Main Outcomes and Measures. Primary measure was accuracy of detection and categorization (discrediting, stigmatizing/labeling, judgmental, and stereotyping) of bias compared to human review. Secondary measures were proportion of patients with any bias, differences in the prevalence of bias across demographic and socioeconomic subgroups, and provider ratings of effectiveness of GPT-4's debiasing language.   Results. Bias was detected in 6.5% of MSHS and 7.4% of MIMIC-IV notes. Compared to manual review, GPT-4 had sensitivity of 95%, specificity of 86%, positive predictive value of 84% and negative predictive value of 96% for bias detection. Stigmatizing/labeling (3.4%), judgmental (3.2%), and discrediting (4.0%) biases were most prevalent. There was higher bias in Black patients (8.3%), transgender individuals (15.7% for trans-female, 16.7% for trans-male), and undomiciled individuals (27%). Patients with non-commercial insurance, particularly Medicaid, also had higher bias (8.9%). Higher bias was also seen in health-related characteristics like frequent healthcare utilization (21% for >100 visits) and substance use disorders (32.2%). Physician-authored notes showed higher bias than nursing notes (9.4% vs. 4.2%, p < 0.001). GPT-4's suggested revisions were rated highly effective by physicians, with an average improvement score of 9.6/10 in reducing bias. Conclusions and Relevance. A frontier LLM effectively identified biased language, without further training, showing utility as a scalable fairness tool. High bias prevalence linked to certain patient characteristics underscores the need for targeted interventions. Integrating AI to facilitate unbiased documentation could significantly impact clinical practice and health outcomes. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement Work was supported in part by the Clinical and Translational Science Awards (CTSA) grant UL1TR004419 from the National Center for Advancing Translational Sciences. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: IRB of Icahn School of Medicine at Mount Sinai gave ethical approval for this work I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors

ObjectiveTo examine racial/ethnic differences in emergency department (ED) transfers to public hospitals and factors explaining these differences.Data Sources and Study SettingED and inpatient data from the Healthcare Cost and Utilization Project for Florida (2010-2019); American Hospital Association Annual Survey (2009-2018).Study DesignLogistic regression examined race/ethnicity and payer on the likelihood of transfer to a public hospital among transferred ED patients. The base model was controlled for patient and hospital characteristics and year fixed effects. Models II and III added urbanicity and hospital referral region (HRR), respectively. Model IV used hospital fixed effects, which compares patients within the same hospital. Models V and VI stratified Model IV by payer and condition, respectively. Conditions were classified as emergency care sensitive conditions (ECSCs), where transfer is protocolized, and non-ECSCs. We reported marginal effects at the means.Data Collection/Extraction MethodsWe examined 1,265,588 adult ED patients transferred from 187 hospitals.Principal FindingsBlack patients were more likely to be transferred to public hospitals compared with White patients in all models except ECSC patients within the same initial hospital (except trauma). Black patients were 0.5-1.3 percentage points (pp) more likely to be transferred to public hospitals than White patients in the same hospital with the same payer. In the base model, Hispanic patients were more likely to be transferred to public hospitals compared with White patients, but this difference reversed after controlling for HRR. Hispanic patients were - 0.6 pp to -1.2 pp less likely to be transferred to public hospitals than White patients in the same hospital with the same payer.ConclusionsLarge population-level differences in whether ED patients of different races/ethnicities were transferred to public hospitals were largely explained by hospital market and the initial hospital, suggesting that they may play a larger role in explaining differences in transfer to public hospitals, compared with other external factors.

Generative Large Language Models (LLMs) hold significant promise in healthcare, demonstrating capabilities such as passing medical licensing exams and providing clinical knowledge. However, their current use as information retrieval tools is limited by challenges like data staleness, resource demands, and occasional generation of incorrect information. This study assessed the potential of LLMs to function as autonomous agents in a simulated tertiary care medical center, using real-world clinical cases across multiple specialties. Both proprietary and open-source LLMs were evaluated, with Retrieval Augmented Generation (RAG) enhancing contextual relevance. Proprietary models, particularly GPT-4, generally outperformed open-source models, showing improved guideline adherence and more accurate responses with RAG. The manual evaluation by expert clinicians was crucial in validating models' outputs, underscoring the importance of human oversight in LLM operation. Further, the study emphasizes Natural Language Programming (NLP) as the appropriate paradigm for modifying model behavior, allowing for precise adjustments through tailored prompts and real-world interactions. This approach highlights the potential of LLMs to significantly enhance and supplement clinical decision-making, while also emphasizing the value of continuous expert involvement and the flexibility of NLP to ensure their reliability and effectiveness in healthcare settings.

Importance High emergency department (ED) pediatric readiness is associated with improved survival, but the impact of changes to ED readiness is unknown. Objective To evaluate the association of changes in ED pediatric readiness at US trauma centers between 2013 and 2021 with pediatric mortality. Design, Setting, and Participants This retrospective cohort study was performed from January 1, 2012, through December 31, 2021, at EDs of trauma centers in 48 states and the District of Columbia. Participants included injured children younger than 18 years with admission or injury-related death at a participating trauma center, including transfers to other trauma centers. Data analysis was performed from May 2023 to January 2024. Exposure Change in ED pediatric readiness, measured using the weighted Pediatric Readiness Score (wPRS, range 0-100, with higher scores denoting greater readiness) from national assessments in 2013 and 2021. Change groups included high-high (wPRS >= 93 on both assessments), low-high (wPRS <93 in 2013 and wPRS >= 93 in 2021), high-low (wPRS >= 93 in 2013 and wPRS <93 in 2021), and low-low (wPRS <93 on both assessments). Main Outcomes and Measures The primary outcome was lives saved vs lost, according to ED and in-hospital mortality. The risk-adjusted association between changes in ED readiness and mortality was evaluated using a hierarchical, mixed-effects logistic regression model based on a standardized risk-adjustment model for trauma, with a random slope-random intercept to account for clustering by the initial ED. Results The primary sample included 467 932 children (300 024 boys [64.1%]; median [IQR] age, 10 [4 to 15] years; median [IQR] Injury Severity Score, 4 [4 to 15]) at 417 trauma centers. Observed mortality by ED readiness change group was 3838 deaths of 144 136 children (2.7%) in the low-low ED group, 1804 deaths of 103 767 children (1.7%) in the high-low ED group, 1288 deaths of 64 544 children (2.0%) in the low-high ED group, and 2614 deaths of 155 485 children (1.7%) in the high-high ED group. After risk adjustment, high-readiness EDs (persistent or change to) had 643 additional lives saved (95% CI, -328 to 1599 additional lives saved). Low-readiness EDs (persistent or change to) had 729 additional preventable deaths (95% CI, -373 to 1831 preventable deaths). Secondary analysis suggested that a threshold of wPRS 90 or higher may optimize the number of lives saved. Among 716 trauma centers that took both assessments, the median (IQR) wPRS decreased from 81 (63 to 94) in 2013 to 77 (64 to 93) in 2021 because of reductions in care coordination and quality improvement. Conclusions and Relevance Although the findings of this study of injured children in US trauma centers were not statistically significant, they suggest that trauma centers should increase their level of ED pediatric readiness to reduce mortality and increase the number of pediatric lives saved after injury.

Background: Evidence-based medicine (EBM) is fundamental to modern clinical practice, requiring clinicians to continually update their knowledge and apply the best clinical evidence in patient care. The practice of EBM faces challenges due to rapid advancements in medical research, leading to information overload for clinicians. The integration of artificial intelligence (AI), specifically Generative Large Language Models (LLMs), offers a promising solution towards managing this complexity. Methods: This study involved the curation of real-world clinical cases across various specialties, converting them into .json files for analysis. LLMs, including proprietary models like ChatGPT 3.5 and 4, Gemini Pro, and open-source models like LLaMA v2 and Mixtral-8x7B, were employed. These models were equipped with tools to retrieve information from case files and make clinical decisions similar to how clinicians must operate in the real world. Model performance was evaluated based on correctness of final answer, judicious use of tools, conformity to guidelines, and resistance to hallucinations. Results: GPT-4 was most capable of autonomous operation in a clinical setting, being generally more effective in ordering relevant investigations and conforming to clinical guidelines. Limitations were observed in terms of model ability to handle complex guidelines and diagnostic nuances. Retrieval Augmented Generation made recommendations more tailored to patients and healthcare systems. Conclusions: LLMs can be made to function as autonomous practitioners of evidence-based medicine. Their ability to utilize tooling can be harnessed to interact with the infrastructure of a real-world healthcare system and perform the tasks of patient management in a guideline directed manner. Prompt engineering may help to further enhance this potential and transform healthcare for the clinician and the patient.

Background Artificial intelligence (AI) and large language models (LLMs) can play a critical role in emergency room operations by augmenting decision-making about patient admission. However, there are no studies for LLMs using real-world data and scenarios, in comparison to and being informed by traditional supervised machine learning (ML) models. We evaluated the performance of GPT-4 for predicting patient admissions from emergency department (ED) visits. We compared performance to traditional ML models both naively and when informed by few-shot examples and/or numerical probabilities. Methods We conducted a retrospective study using electronic health records across 7 NYC hospitals. We trained Bio-Clinical-BERT and XGBoost (XGB) models on unstructured and structured data, respectively, and created an ensemble model reflecting ML performance. We then assessed GPT-4 capabilities in many scenarios: through Zero-shot, Few-shot with and without retrieval-augmented generation (RAG), and with and without ML numerical probabilities. Results The Ensemble ML model achieved an area under the receiver operating characteristic curve (AUC) of 0.88, an area under the precision-recall curve (AUPRC) of 0.72 and an accuracy of 82.9%. The naïve GPT-4's performance (0.79 AUC, 0.48 AUPRC, and 77.5% accuracy) showed substantial improvement when given limited, relevant data to learn from (ie, RAG) and underlying ML probabilities (0.87 AUC, 0.71 AUPRC, and 83.1% accuracy). Interestingly, RAG alone boosted performance to near peak levels (0.82 AUC, 0.56 AUPRC, and 81.3% accuracy). Conclusions The naïve LLM had limited performance but showed significant improvement in predicting ED admissions when supplemented with real-world examples to learn from, particularly through RAG, and/or numerical probabilities from traditional ML models. Its peak performance, although slightly lower than the pure ML model, is noteworthy given its potential for providing reasoning behind predictions. Further refinement of LLMs with real-world data is necessary for successful integration as decision-support tools in care settings.

The quality of emergency department (ED) care for children in the US is highly variable. The National Pediatric Readiness Project aims to improve survival for children receiving emergency services. We conducted a cost-effectiveness analysis of increasing ED pediatric readiness, using a decision-analytic simulation model. Previously published primary analyses of a nationally representative, population-based cohort of children receiving emergency services at 747 EDs in eleven states provided clinical and cost parameters. From a health care sector perspective, we used a 3 percent annual discount rate and quantified lifetime costs, quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratios (ICERs). We performed probabilistic, one-way, and subgroup sensitivity analyses. Increasing ED pediatric readiness yields 69,100 QALYs for the eleven-state cohort, costing $9,300 per QALY gained. Achieving high readiness nationally yields 179,000 QALYs at the same ICER (with implementation costs of approximately $260 million). Implementing high ED pediatric readiness for all EDs in the US is highly cost-effective.

ImportanceHigh emergency department (ED) pediatric readiness is associated with improved survival among children receiving emergency care, but state and national costs to reach high ED readiness and the resulting number of lives that may be saved are unknown.ObjectiveTo estimate the state and national annual costs of raising all EDs to high pediatric readiness and the resulting number of pediatric lives that may be saved each year.Design, Setting, and ParticipantsThis cohort study used data from EDs in 50 US states and the District of Columbia from 2012 through 2022. Eligible children were ages 0 to 17 years receiving emergency services in US EDs and requiring admission, transfer to another hospital for admission, or dying in the ED (collectively termed at-risk children). Data were analyzed from October 2023 to May 2024.ExposureEDs considered to have high readiness, with a weighted pediatric readiness score of 88 or above (range 0 to 100, with higher numbers representing higher readiness).Main Outcomes and MeasuresAnnual hospital expenditures to reach high ED readiness from current levels and the resulting number of pediatric lives that may be saved through universal high ED readiness.ResultsA total 842 of 4840 EDs (17.4%; range, 2.9% to 100% by state) had high pediatric readiness. The annual US cost for all EDs to reach high pediatric readiness from current levels was $207 335 302 (95% CI, $188 401 692-$226 268 912), ranging from $0 to $11.84 per child by state. Of the 7619 child deaths occurring annually after presentation, 2143 (28.1%; 95% CI, 678-3608) were preventable through universal high ED pediatric readiness, with population-adjusted state estimates ranging from 0 to 69 pediatric lives per year.Conclusions and RelevanceIn this cohort study, raising all EDs to high pediatric readiness was estimated to prevent more than one-quarter of deaths among children receiving emergency services, with modest financial investment. State and national policies that raise ED pediatric readiness may save thousands of children’s lives each year.

Background The national impact of racial residential segregation on out‐of‐hospital cardiac arrest outcomes after initial resuscitation remains poorly understood. We sought to characterize the association between measures of racial and economic residential segregation at the ZIP code level and long‐term survival and readmissions after out‐of‐hospital cardiac arrest among Medicare beneficiaries. Methods and Results In this retrospective cohort study, using Medicare claims data, our primary predictor was the index of concentration at the extremes, a measure of racial and economic segregation. The primary outcomes were death up to 3 years and readmissions. We estimated hazard ratios (HRs) across all 3 types of index of concentration at the extremes measures for each outcome while adjusting for beneficiary demographics, treating hospital characteristics, and index hospital procedures. In fully adjusted models for long‐term survival, we found a decreased hazard of death and risk of readmission for beneficiaries residing in the more segregated White communities and higher‐income ZIP codes compared with the more segregated Black communities and lower‐income ZIP codes across all 3 indices of concentration at the extremes measures (race: HR, 0.87 [95% CI, 0.81–0.93]; income: HR, 0.75 [95% CI, 0.69–0.78]; and race+income: HR, 0.77 [95% CI, 0.72–0.82]). Conclusions We found a decreased hazard of death and risk for readmission for those residing in the more segregated White communities and higher‐income ZIP codes compared with the more segregated Black communities and lower‐income ZIP codes when using validated measures of racial and economic segregation. Although causal pathways and mechanisms remain unclear, disparities in outcomes after out‐of‐hospital cardiac arrest are associated with the structural components of race and wealth and persist up to 3 years after discharge.

Introduction: Early detection and optimal resuscitation of critically ill sepsis patients may improve sepsis care delivery. The objective was to assess the feasibility of developing and implementing an end-to-end sepsis solution including early detection, monitoring, and teleconsultation. Methods: Prospective implementation of an end-to-end sepsis solution for potential sepsis patients presenting to a community hospital emergency department (ED) between 11 AM and 5 PM, Monday to Friday, during a 40-day period in 2019. Qualifying patients were compared with patients presenting at other times during the pilot screening period and to historic controls. Results: During the initial period, 203 patients met the screening criteria for potential sepsis; 77 patients (37.9%) had a primary diagnosis of sepsis, present on admission. Mean age was 60 ± 20 years; 50.7% were female; and 24 patients (11.8%) were primary sepsis, SEP-1 bundle eligible. Eighty of 203 (39.4%) had an initial lactate performed, mean, 2.7 ± 1.7 mmol/L. For the 24 primary sepsis, SEP-1 bundle eligible patients, 100% received antibiotics and intravenous fluid. Thirteen consults were performed on 12 patients; mean time from consult decision to beam in to the telemedicine robot was 7.3 ± 5.5 min; mean time from beam in to robot connection with the expert was 23.6 ± 13.2 s; mean consultation call time was 6.3 ± 4.3 min. Conclusions: In a convenience sample of patients with potential sepsis presenting to a community hospital ED, an end-to-end sepsis solution using early detection, tracking, and consultation was feasible and has the potential to improve sepsis detection and treatment.

Trauma centers use registry data to benchmark performance using a standardized risk adjustment model. Our objective was to utilize national claims to develop a risk adjustment model applicable across all hospitals, regardless of designation or registry participation. Patients from 2013-14 Pennsylvania Trauma Outcomes Study (PTOS) registry data were probabilistically matched to Medicare claims using demographic and injury characteristics. Pairwise comparisons established facility linkages and matching was then repeated within facilities to link records. Registry models were estimated using GLM and compared with five claims-based LASSO models: demographics, clinical characteristics, diagnosis codes, procedures codes, and combined demographics/clinical characteristics. Area under the curve and correlation with registry model probability of death were calculated for each linked and out-of-sample cohort. From 29 facilities, a cohort comprising 16,418 patients were linked between datasets. Patients were similarly distributed: median age 82 (PTOS IQR: 74-87 vs. Medicare IQR: 75-88); non-white 6.2% (PTOS) vs. 5.8% (Medicare). The registry model AUC was 0.86 (0.84-0.87). Diagnosis and procedure codes models performed poorest. The demographics/clinical characteristics model achieved an AUC = 0.84 (0.83-0.86) and Spearman = 0.62 with registry data. Claims data can be leveraged to create models that accurately measure the performance of hospitals that treat trauma patients.

Objective Whether ambulance transport patterns are optimized to match children to high-readiness emergency departments (EDs) and the resulting effect on survival are unknown. We quantified the number of children transported by 9-1-1 emergency medical services (EMS) to high-readiness EDs, additional children within 30 minutes of a high-readiness ED, and the estimated effect on survival. Methods This was a cross-sectional study using data from the National EMS Information System for 5,461 EMS agencies in 28 states from 1/1/2012 through 12/31/2019, matched to the 2013 National Pediatric Readiness Project assessment of ED pediatric readiness. We performed a geospatial analysis of children 0 to 17 years requiring 9-1-1 EMS transport to acute care hospitals, including day-, time-, and traffic-adjusted estimates for driving times to all EDs within 30 minutes of the scene. We categorized receiving hospitals by quartile of ED pediatric readiness using the weighted Pediatric Readiness Score (wPRS, range 0-100) and defined a high-risk subgroup of children as a proxy for admission. We used published estimates for the survival benefit of high readiness EDs to estimate the number of lives saved. Results There were 808,536 children transported by EMS, of whom 253,541 (31.4%) were high-risk. Among the 2,261 receiving hospitals, the median wPRS was 70 (IQR 57-85, range 26-100) and the median number of receiving hospitals within 30 minutes was 4 per child (IQR 2-11, range 1 to 53). Among all children, 411,685 (50.9%) were taken to EDs in the highest quartile of pediatric readiness, and 180,547 (22.3%) children transported to lower readiness EDs were within 30 minutes of a high readiness ED. Findings were similar among high-risk children. Based on high-risk children, we estimated that 3,050 pediatric lives were saved by transport to high-readiness EDs and an additional 1,719 lives could have been saved by shifting transports to high readiness EDs within 30 minutes. Conclusions Approximately half of children transported by EMS were taken to high-readiness EDs and an additional one quarter could have been transported to such an ED, with measurable effect on survival.

Importance Emergency department (ED) pediatric readiness is associated with improved survival among children. However, the association between geographic access to high-readiness EDs in US trauma centers and mortality is unclear. Objective To evaluate the association between the proximity of injury location to receiving trauma centers, including the level of ED pediatric readiness, and mortality among injured children. Design, Setting, and Participants This retrospective cohort study used a standardized risk-adjustment model to evaluate the association between trauma center proximity, ED pediatric readiness, and in-hospital survival. There were 765 trauma centers (level I-V, adult and pediatric) that contributed data to the National Trauma Data Bank (January 1, 2012, through December 31, 2017) and completed the 2013 National Pediatric Readiness Assessment (conducted from January 1 through August 31, 2013). The study comprised children aged younger than 18 years who were transported by ground to the included trauma centers. Data analysis was performed between January 1 and March 31, 2022. Exposures Trauma center proximity within 30 minutes by ground transport and ED pediatric readiness, as measured by weighted pediatric readiness score (wPRS; range, 0-100; quartiles 1 [low readiness] to 4 [high readiness]). Main Outcomes and Measures In-hospital mortality. We used a patient-level mixed-effects logistic regression model to evaluate the association of transport time, proximity, and ED pediatric readiness on mortality. Results This study included 212 689 injured children seen at 765 trauma centers. The median patient age was 10 (IQR, 4-15) years, 136 538 (64.2%) were male, and 127 885 (60.1%) were White. A total of 4156 children (2.0%) died during their hospital stay. The median wPRS at these hospitals was 79.1 (IQR, 62.9-92.7). A total of 105 871 children (49.8%) were transported to trauma centers with high-readiness EDs (wPRS quartile 4) and another 36 330 children (33.7%) were injured within 30 minutes of a quartile 4 ED. After adjustment for confounders, proximity, and transport time, high ED pediatric readiness was associated with lower mortality (highest-readiness vs lowest-readiness EDs by wPRS quartiles: adjusted odds ratio, 0.65 [95% CI, 0.47-0.89]). The survival benefit of high-readiness EDs persisted for transport times up to 45 minutes. The findings suggest that matching children to trauma centers with high-readiness EDs within 30 minutes of the injury location may have potentially saved 468 lives (95% CI, 460-476 lives), but increasing all trauma centers to high ED pediatric readiness may have potentially saved 1655 lives (95% CI, 1647-1664 lives). Conclusions and Relevance These findings suggest that trauma centers with high ED pediatric readiness had lower mortality after considering transport time and proximity. Improving ED pediatric readiness among all trauma centers, rather than selective transport to trauma centers with high ED readiness, had the largest association with pediatric survival. Thus, increased pediatric readiness at all US trauma centers may substantially improve patient outcomes after trauma.

STUDY OBJECTIVE:We estimate the economics of US emergency department (ED) professional services, which is increasingly under strain given the longstanding effect of unreimbursed care, and falling Medicare and commercial payments.METHODS:We used data from the Nationwide Emergency Department Sample (NEDS), Medicare, Medicaid, Health Care Cost Institute, and surveys to estimate national ED clinician revenue and costs from 2016 to 2019. We compare annual revenue and cost for each payor and calculate foregone revenue, the amount clinicians may have collected had uninsured patients had either Medicaid or commercial insurance.RESULTS:In 576.5 million ED visits (2016 to 2019), 12% were uninsured, 24% were Medicare-insured, 32% Medicaid-insured, 28% were commercially insured, and 4% had another insurance source. Annual ED clinician revenue averaged $23.5 billion versus costs of $22.5 billion. In 2019, ED visits covered by commercial insurance generated $14.3 billion in revenues and cost $6.5 billion. Medicare visits generated $5.3 billion and cost $5.7 billion; Medicaid visits generated $3.3 billion and cost $7 billion. Uninsured ED visits generated $0.5 billion and cost $2.9 billion. The average annual foregone revenue for ED clinicians to treat the uninsured was $2.7 billion.CONCLUSION:Large cost-shifting from commercial insurance cross-subsidizes ED professional services for other patients. This includes the Medicaid-insured, Medicare-insured, and uninsured, all of whom incur ED professional service costs that substantially exceed their revenue. Foregone revenue for treating the uninsured relative to what may have been collected if patients had health insurance is substantial.

Policy Points Current pay-for-performance and other payment policies ignore hospital transfers for emergency conditions, which may exacerbate disparities. No conceptual framework currently exists that offers a patient-centered, population-based perspective for the structure of hospital transfer networks. The hospital transfer network equity-quality framework highlights the external and internal factors that determine the structure of hospital transfer networks, including structural inequity and racism. CONTEXT Emergency care includes two key components: initial stabilization and transfer to a higher level of care. Significant work has focused on ensuring that local facilities can stabilize patients. However, less is understood about transfers for definitive care. To better understand how transfer network structure impacts population health and equity in emergency care, we propose a conceptual framework, the hospital transfer network equity-quality model (NET-EQUITY). NET-EQUITY can help optimize population outcomes, decrease disparities, and enhance planning by supporting a framework for understanding emergency department transfers. METHODS To develop the NET-EQUITY framework, we synthesized work on health systems and quality of health care (Donabedian, the Institute of Medicine, Ferlie, and Shortell) and the research framework of the National Institute on Minority Health and Health Disparities with legal and empirical research. FINDINGS The central thesis of our framework is that the structure of hospital transfer networks influences patient outcomes, as defined by the Institute of Medicine, which includes equity. The structure of hospital transfer networks is shaped by internal and external factors. The four main external factors are the regulatory, economic environment, provider, and sociocultural and physical/built environment. These environments all implicate issues of equity that are important to understand to foster an equitable population-based system of emergency care. The framework highlights external and internal factors that determine the structure of hospital transfer networks, including structural racism and inequity. CONCLUSIONS The NET-EQUITY framework provides a patient-centered, equity-focused framework for understanding the health of populations and how the structure of hospital transfer networks can influence the quality of care that patients receive.

In the acutely injured patient with abdominal trauma, several diagnostic modalities are available in the emergency department (ED) to detect the presence of solid organ injury and intra-abdominal hemorrhage. A challenge often encountered with computed tomography imaging in trauma patients, however, is that it cannot always be safely performed in unstable patients. In such patients, a diagnostic peritoneal lavage (DPL) performed at the bedside in the ED was historically the test of choice to assess for intra-abdominal injury for which surgical intervention may have been needed. In recent years diagnostic ultrasound has emerged as a safe, rapid, and noninvasive alternative to DPL, and has replaced the use of DPL in hospitals at which ED ultrasound is available. The focused assessment with sonography for trauma exam is the best-studied and most commonly performed ultrasound protocol for trauma patients in the ED.

Infectious disease outbreaks and pandemics have repeatedly threatened public health and have severely strained healthcare delivery systems throughout the past century. Pathogens causing respiratory illness, such as influenza viruses and coronaviruses, as well as the highly communicable viral hemorrhagic fevers, pose a large threat to the healthcare delivery system in the United States and worldwide. Through the Hospital Preparedness Program, within the US Department of Health and Human Services Office of the Assistant Secretary for Preparedness and Response, a nationwide Regional Ebola Treatment Network (RETN) was developed, building upon a state- and jurisdiction-based tiered hospital approach. This network, spearheaded by the National Emerging Special Pathogens Training and Education Center, developed a conceptual framework and plan for the evolution of the RETN into the National Special Pathogen System of Care (NSPS). Building the NSPS strategy involved reviewing the literature and the initial framework used in forming the RETN and conducting an extensive stakeholder engagement process to identify gaps and develop solutions. From this, the NSPS strategy and implementation plan were formed. The resulting NSPS strategy is an ambitious but critical effort that will have impacts on the mitigation efforts of special pathogen threats for years to come.