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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. [ABSTRACT FROM AUTHOR]

Objective: To examine racial/ethnic differences in emergency department (ED) transfers to public hospitals and factors explaining these differences. Data Sources and Study Setting: ED and inpatient data from the Healthcare Cost and Utilization Project for Florida (2010–2019); American Hospital Association Annual Survey (2009–2018). Study Design: Logistic 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 Methods: We examined 1,265,588 adult ED patients transferred from 187 hospitals. Principal Findings: Black 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. Conclusions: Large 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. [ABSTRACT FROM AUTHOR]

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. 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. 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. 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. [ABSTRACT FROM AUTHOR]

Policy PointsCurrent 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 proposea 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. [ABSTRACT FROM AUTHOR]

Racial and racialized economic residential segregation has been empirically associated with outcomes across multiple health conditions but not yet explored in relation to out-of-hospital cardiac arrest (OHCA). We sought to examine if measures of racial and economic residential segregation are associated with differences in survival to discharge after OHCA for Black and White Medicare beneficiaries. Utilizing age-eligible Medicare fee-for-service claims data from 2013 to 2015, we identified OHCA claims and determined survival to discharge. The primary predictor, residential segregation, was calculated using the index of concentration at the extremes (ICE) for the beneficiary residential ZIP code. Multilevel modified Poisson regression models were used to determine the association of OHCA outcomes and ZIP code level ICE measures. In total, 194,263 OHCA cases were identified among beneficiaries residing in 75% of US ZIP codes. Black beneficiaries exhibited 12.1% survival to discharge, compared with 12.5% of White beneficiaries. In fully adjusted models of the three ICE measures accounting for differences in treating hospital characteristics, there was as high as a 28% (RR 1.28, CI 1.23–1.26) higher relative likelihood of survival to discharge in the most segregated White ZIP codes (Q5) as compared to the most segregated Black ZIP codes (Q1). Racial residential segregation is independently associated with disparities in OHCA outcomes; among Medicare beneficiaries who generated a claim after suffering an OHCA, ICE measures of racial segregation are associated with a lower likelihood of survival to discharge for those living in the most segregated Black and lower income quintiles compared to higher quintiles. [ABSTRACT FROM AUTHOR]

Background: One in nine emergency department (ED) visits by Medicare beneficiaries are for ambulatory care sensitive conditions (ACSCs). This study aimed to examine the association between ACSC ED visits to hospitals with the highest proportion of ACSC visits ("high ACSC hospitals) and safety-net status.Methods: This was a cross-sectional study of ED visits by Medicare fee-for-service beneficiaries ≥ 65 years using 2013-14 claims data, Area Health Resources File data, and County Health Rankings. Logistic regression estimated the association between an ACSC ED visit to high ACSC hospitals, accounting for individual, hospital, and community factors, including whether the visit was to a safety-net hospital. Safety net status was measured by Disproportionate Share Hospital (DSH) index patient percentage; public hospital status; and proportion of dual-eligible beneficiaries. Hospital-level correlation was calculated between ACSC visits, DSH index, and dual-eligible patients. We stratified by type of ACSC visit: acute or chronic.Results: Among 5,192,729 ACSC ED visits, the odds of visiting a high ACSC hospital were higher for patients who were Black (1.37), dual-eligible (1.18), and with the highest comorbidity burden (1.26, p < 0.001 for all). ACSC visits had increased odds of being to high ACSC hospitals if the hospitals were high DSH (1.43), served the highest proportion of dual-eligible beneficiaries (2.23), and were for-profit (relative to non-profit) (1.38), and lower odds were associated with public hospitals (0.64) (p < 0.001 for all). This relationship was similar for visits to high chronic ACSC hospitals (high DSH: 1.59, high dual-eligibility: 2.60, for-profit: 1.41, public: 0.63, all p < 0.001) and to a lesser extent, high acute ACSC hospitals (high DSH: 1.02; high dual-eligibility: 1.48, for-profit: 1.17, public: 0.94, p < 0.001). The proportion of ACSC visits at all hospitals was weakly correlated with DSH proportion (0.2) and the proportion of dual-eligible patients (0.29), and this relationship was also seen for both chronic and acute ACSC visits, though stronger for the chronic ACSC visits.Conclusion: Visits to hospitals with a high proportion of acute ACSC ED visits may be less likely to be to hospitals classified as safety net hospitals than those with a high proportion of chronic ACSC visits. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The Epic electronic health record (EHR) is a commonly used EHR in the United States. This EHR contain large semi-structured "flowsheet" fields. Flowsheet fields lack a well-defined data dictionary and are unique to each site. We evaluated a simple free-text-like method to extract these data. As a use case, we demonstrate this method in predicting mortality during emergency department (ED) triage. We retrieved demographic and clinical data for ED visits from the Epic EHR (1/2014-12/2018). Data included structured, semi-structured flowsheet records and free-text notes. The study outcome was in-hospital death within 48 h. Most of the data were coded using a free-textlike Bag-of-Words (BoW) approach. Two machine-learning models were trained: gradient boosting and logistic regression. Term frequency-inverse document frequency was employed in the logistic regression model (LR-tf-idf). An ensemble of LR-tf-idf and gradient boosting was evaluated. Models were trained on years 2014-2017 and tested on year 2018. Among 412,859 visits, the 48-h mortality rate was 0.2%. LR-tf-idf showed AUC 0.98 (95% CI: 0.98-0.99). Gradient boosting showed AUC 0.97 (95% CI: 0.96-0.99). An ensemble of both showed AUC 0.99 (95% CI: 0.98-0.99). In conclusion, a free-text-like approach can be useful for extracting knowledge from large amounts of complex semi-structured EHR data. [ABSTRACT FROM AUTHOR]

PURPOSE: For individuals with diabetes, diabetes health status may not align with A1C targets. Patients may use nonclinical targets when assessing their diabetes management success. Identifying these targets is important in developing patient-centered management plans. The purpose of this study was to identify patient markers of successful diabetes management among patients in an urban academic health system. METHODS: A secondary analysis of semistructured interviews was completed with 89 adults with type 1 or type 2 diabetes. Participants had a recent diabetes-related emergency department (ED) visits or hospitalization or were primary care patients with an A1C >7.5%. Interviews were conducted to saturation. Demographic data were collected via self-report and electronic medical records. Interviews were analyzed using conventional content analysis. This analysis focused on patient perceptions of successful management coded to "measuring management success." RESULTS: Although most participants cited A1C or blood glucose as a marker of successful diabetes management, they had varied understanding of these metrics. Most used a combination of targets from the following categories: 1) A1C, blood glucose, and numbers; 2) engagement in medical care; 3) taking medication and medication types; 4) symptoms; 5) diet, exercise, and weight; and 6) stress management and social support. CONCLUSION: Individuals not meeting glycemic goals and/or with recent diabetes-related ED visits or hospitalizations had varied understanding of A1C and blood glucose targets. They use multiple additional markers of successful management and had a desire for management discussions that incorporate these markers. These measures should be incorporated into their care plans along with clinical targets. [ABSTRACT FROM AUTHOR]

Objectives. To create a tool to rapidly determine where pandemic demand for critical care overwhelms county-level surge capacity and to compare public health and medical responses. Methods. In March 2020, COVID-19 cases requiring critical care were estimated using an adaptive metapopulation SEIR (susceptible-exposed-infectious-recovered) model for all 3142 US counties for future 21-day and 42-day periods from April 2, 2020, to May 13, 2020, in 4 reactive patterns of contact reduction--0%, 20%, 30%, and 40%--and 4 surge response scenarios--very low, low, medium, and high. Results. In areas with increased demand, surge response measures could avert 104 120 additional deaths--55% through high clearance of critical care beds and 45% through measures such as greater ventilator access. The percentages of lives saved from high levels of contact reduction were 1.9 to 4.2 times greater than high levels of hospital surge response. Differences in projected versus actual COVID-19 demands were reasonably small over time. Conclusions. Nonpharmaceutical public health interventions had greater impact in minimizing preventable deaths during the pandemic than did hospital critical care surge response. Ready-to-go spatiotemporal supply and demand data visualization and analytics tools should be advanced for future preparedness and all-hazards disaster response. [ABSTRACT FROM AUTHOR]

There is growing evidence on the effect of face mask use in controlling the spread of COVID-19. However, few studies have examined the effect of local face mask policies on the pandemic. In this study, we developed a dynamic compartmental model of COVID-19 transmission in New York City (NYC), which was the epicenter of the COVID-19 pandemic in the USA. We used data on daily and cumulative COVID-19 infections and deaths from the NYC Department of Health and Mental Hygiene to calibrate and validate our model. We then used the model to assess the effect of the executive order on face mask use on infections and deaths due to COVID-19 in NYC. Our results showed that the executive order on face mask use was estimated to avert 99,517 (95% CIs 72,723–126,312) COVID-19 infections and 7978 (5692–10,265) deaths in NYC. If the executive order was implemented 1 week earlier (on April 10), the averted infections and deaths would be 111,475 (81,593–141,356) and 9017 (6446–11,589), respectively. If the executive order was implemented 2 weeks earlier (on April 3 when the Centers for Disease Control and Prevention recommended face mask use), the averted infections and deaths would be 128,598 (94,373–162,824) and 10,515 (7540–13,489), respectively. Our study provides public health practitioners and policymakers with evidence on the importance of implementing face mask policies in local areas as early as possible to control the spread of COVID-19 and reduce mortality. [ABSTRACT FROM AUTHOR]

Background: Post-discharge deaths are common in patients hospitalized for sepsis, but the drivers of post-discharge deaths are unclear. The objective of this study was to test the hypothesis that hospitals with high risk-adjusted inpatient sepsis mortality also have high post-discharge mortality, readmissions, and discharge to nursing homes.Methods: Retrospective cohort study of age-qualifying Medicare beneficiaries with sepsis hospitalization between January 2013 and December 2014. Hospital survivors were followed for 180-days post-discharge, and mortality, readmissions, and new admission to skilled nursing facility were measured. Inpatient hospital-specific sepsis risk-adjusted mortality ratio (observed: expected) was the primary exposure.Results: A total of 830,721 patients in the cohort were hospitalized for sepsis, with inpatient mortality of 20% and 90-day mortality of 48%. Higher hospital-specific sepsis risk-adjusted mortality was associated with increased 90-day post-discharge mortality (aOR 1.03 per each 0.1 increase in hospital inpatient O:E ratio, 95% CI 1.03-1.04). Higher inpatient risk adjusted mortality was also associated with increased probability of being discharged to a nursing facility (aOR 1.03, 95% CI 1.02-1.03) and 90-day readmissions (aOR 1.03, 95% CI 1.02-1.03).Conclusions: Hospitals with the highest risk-adjusted sepsis inpatient mortality also have higher post-discharge mortality and increased readmissions, suggesting that post-discharge complications are a modifiable risk that may be affected during inpatient care. Future work will seek to elucidate inpatient and healthcare practices that can reduce sepsis post-discharge complications. [ABSTRACT FROM AUTHOR]

Background: A family history of diabetes and family members' experiences with diabetes may influence individuals' beliefs and expectations about their own diabetes. No qualitative studies have explored the relationship between family history and experiences and individuals' diabetes illness representations. Methods: Secondary data analysis of 89 exploratory, semi-structured interviews with adults with type 1 or type 2 diabetes seeking care in an urban health system. Participants had a recent diabetes-related ED visit/hospitalization or hemoglobin A1c > 7.5%. Interviews were conducted until thematic saturation was achieved. Demographic data were collected via self-report and electronic medical record review. Interviews were audio-recorded, transcribed, and coded using a conventional content analysis approach. References to family history and family members' experiences with diabetes were analyzed using selected domains of Leventhal's Common Sense Model of Self-Regulation. Results: Participants cited both genetic and behavioral family history as a major cause of their diabetes. Stories of relatives' diabetes complications and death figured prominently in their discussion of consequences; however, participants felt controllability over diabetes through diet, physical activity, and other self-care behaviors. Conclusions: Findings supported an important role of family diabetes history and experience in development of diabetes illness representations. Further research is needed to expand our understanding of the relationships between these perceptions, self-management behaviors, and outcomes. Family practice providers, diabetes educators and other team members should consider expanding assessment of current family structure and support to also include an exploration of family history with diabetes, including which family members had diabetes, their self-care behaviors, and their outcomes, and how this history fits into the patient's illness representations. [ABSTRACT FROM AUTHOR]

Abstract: Objectives: We determined the impact of including race, ethnicity, and poverty in risk adjustment models for emergency care–sensitive conditions mortality that could be used for hospital pay‐for‐performance initiatives. We hypothesized that adjusting for race, ethnicity, and poverty would bolster rankings for hospitals that cared for a disproportionate share of nonwhite, Hispanic, or poor patients. Methods: We performed a cross‐sectional analysis of patients admitted from the emergency department to 157 hospitals in Pennsylvania with trauma, sepsis, stroke, cardiac arrest, and ST‐elevation myocardial infarction. We used multivariable logistic regression models to predict in‐hospital mortality. We determined the predictive accuracy of adding patient race and ethnicity (dichotomized as non‐Hispanic white vs. all other Hispanic or nonwhite patients) and poverty (uninsured, on Medicaid, or lowest income quartile zip code vs. all others) to other patient‐level covariates. We then ranked each hospital on observed‐to‐expected mortality, with and without race, ethnicity, and poverty in the model, and examined characteristics of hospitals with large changes between models. Results: The overall mortality rate among 170,750 inpatients was 6.9%. Mortality was significantly higher for nonwhite and Hispanic patients (adjusted odds ratio [aOR] = 1.27, 95% confidence interval [CI] = 1.19–1.36) and poor patients (aOR = 1.21, 95% CI = 1.12–1.31). Adding race, ethnicity, and poverty to the risk adjustment model resulted in a small increase in C‐statistic (0.8260 to 0.8265, p = 0.002). No hospitals moved into or out of the highest‐performing decile when adjustment for race, ethnicity, and poverty was added, but the three hospitals that moved out of the lowest‐performing decile, relative to other hospitals, had significantly more nonwhite and Hispanic patients (68% vs. 11%, p < 0.001) and poor patients (56% vs. 10%, p < 0.001). Conclusions: Sociodemographic risk adjustment of emergency care–sensitive mortality improves apparent performance of some hospitals treating a large number of nonwhite, Hispanic, or poor patients. This may help these hospitals avoid financial penalties in pay‐for‐performance programs. [ABSTRACT FROM AUTHOR]

In January 2015, the US Secretary of Health and Human Services announced targets for the transformation of Medicare reimbursement from a fee-for-service model to payments based on alternative payment models. People now use technology for virtually everything – from paying bills to purchasing almost anything; it is therefore natural to think that they will use technology to take ownership of their own health care. The remote provision of health care, where providers and patients are not in the same location, will allow patients to receive the right care, at the right time, at the right place, and in the manner they consider right for them. To date, much of the technological advances in medicine have been led by the technology creators rather than providers or patients. A meeting of leaders from academic medical centers was convened to brainstorm and explore new opportunities to educate the workforce, expand the science, and improve the delivery of quality care to patients through the use of telemedicine. The academic community needs to develop an evidence base that can inform new care delivery models, including the role for remote monitoring and wearable technology, as well as the methods by which the best patient-centered care can be provided. It is important that the future of medicine be determined by solid research and education rather than the latest “cool toy” to reach the market. Academic medical centers are in a unique position to help shape this future direction, collaborating to create innovative and efficient solutions for patient care. Specific calls for action are summarized. [ABSTRACT FROM AUTHOR]

Objectives: To determine how frequently patients revisit the emergency department after an initial encounter, and to describe revisit capture rates for the same hospital, health system, and geographic region.Data Sources/study Setting: Florida state data from January 1, 2010, to June 30, 2011, from the Healthcare Cost and Utilization Project.Study Design: This is a retrospective cohort study of emergency department return visits among Florida adults over an 18-month period. We evaluated pairs of index and 30-day return emergency department visits and compared capture rates for hospital, health system, and geographic units.Data Collection/extraction Methods: Data were obtained from the Agency for Healthcare Research and Quality's Healthcare Cost and Utilization Project and the American Hospital Association Annual Survey Database.Principal Findings: Among 9,416,212 emergency department visits, 22.6 percent (2,124,441) were associated with a 30-day return. Seventy percent (1,477,772) of 30-day returns occurred to the same hospital. The 30-day return capture rates were highest within the same geographic area: county-level capture at 92 percent (IQR=86-96 percent) versus health system capture at 75 percent (IQR = 68-81 percent).Conclusions: Acute care utilization patterns are often independent of health system boundaries. Current population-based health care models that attribute patients to a single provider or health system may be strengthened by considering geographic patterns of acute care utilization. [ABSTRACT FROM AUTHOR]

Background: Police transport (PT) of penetrating trauma patients has the potential to decrease prehospital times for patients with life-threatening hemorrhage and is part of official policy in Philadelphia, Pennsylvania. We hypothesized that rates of PT of bluntly injured patients have increased over the past decade. Methods: We used Pennsylvania Trauma Outcomes Study registry data from 2006-15 to identify bluntly injured adult patients transported to all 8 trauma centers in Philadelphia. PT was compared to ambulance transport, excluding transfers, burn patients, and private transport. We compared demographics, mechanism, and injury outcomes between PT and ambulance transport patients and used multivariable logistic regression to identify independent predictors of PT. We also identified physiological indicators and injury patterns that might have benefitted from prehospital intervention by EMS. Results: Of 28 897 bluntly injured patients, 339 (1.2%) were transported by police and 28 558 (98.8%) by ambulance. Blunt trauma accounted for 11% of PT and penetrating trauma for 89%. PT patients were younger, more likely to be male, and more likely to be African American or Asian and were more often injured by assault or motor vehicle crash. There were no significant differences presenting physiology between PT and EMS patients. In multivariable logistic regression analysis, male sex (OR 1.89, 95%CI 1.40-2.55), African American race (OR 1.71 95%CI 1.34-2.18), and Asian race (OR 2.25, 95%CI 1.22-4.14) were independently associated with PT. Controlling for injury severity and physiology, there was no significant difference in mortality between PT and EMS. Overall, 64% of PT patients had a condition that might have benefited from prehospital intervention such as supplemental oxygen for brain injury or spine stabilization for vertebral fractures. Conclusions: PT affects a small minority of blunt trauma patients, and did not appear associated with higher mortality. However, PT patients included many who might have benefited from proven, prehospital intervention. Clinicians, EMS providers, and law enforcement should collaborate to optimize use of PT within the trauma system. [ABSTRACT FROM AUTHOR]

Introduction: National health security requires that healthcare facilities be prepared to provide rapid, effective emergency and trauma care to all patients affected by a catastrophic event. We sought to quantify changes in healthcare utilization patterns for an at-risk Medicare population before, during and after Superstorm Sandy's 2012 landfall in New Jersey (NJ). Methods: This study is a retrospective cohort study of Medicare beneficiaries impacted by Superstorm Sandy. We compared hospital emergency department (ED) and healthcare facility inpatient utilization in the weeks before and after Superstorm Sandy landfall using a 20% random sample of Medicare fee-for-service beneficiaries continuously enrolled in 2011 and 2012 (N=224,116). Outcome measures were pre-storm discharges (or transfers), average length of stay, service intensity weight and post-storm ED visits resulting in either discharge or hospital admission. Results: In the pre-storm week, hospital transfers from skilled nursing facilities (SNF) increased by 39% and inpatient discharges had a 0.3 day decreased mean length of stay compared to the prior year. In the post-storm week, ED visits increased by 14% statewide; of these additional "surge" patients, 20% were admitted to the hospital. The increase in ED demand was more than double the statewide average in the most highly impacted coastal regions (35% versus 14%). Conclusion: Superstorm Sandy impacted both pre- and post-storm patient movement in New Jersey; post-landfall ED surge was associated with overall storm impact, which was greatest in coastal counties. A significant increase in the number and severity of pre-storm transfer patients, in particular from SNF, as well as in post-storm ED visits and inpatient admissions, draws attention to the importance of collaborative regional approaches to healthcare in large-scale events. [ABSTRACT FROM AUTHOR]

Background and purpose: Sex and race disparities in recombinant tissue plasminogen activator (rt-PA) use have been reported. We sought to explore sex and race differences in the utilization of rt-PA at primary stroke centers (PSCs) compared to non-PSCs across the US. Methods: Data from the National (Nationwide) Inpatient Sample (NIS) 2004-2010 was utilized to assess sex differences in treatment for ischemic stroke in PSCs compared to non-PSCs. results: There were 304,152 hospitalizations with a primary diagnosis of ischemic stroke between 2004 and 2010 in the analysis: 75,160 (24.7%) patients were evaluated at a PSC. A little over half of the patients evaluated at PSCs were female (53.8%). A lower proportion of women than men received rt-PA at both PSCs (6.8 vs. 7.5%, p < 0.001) and non-PSCs (2.3 vs. 2.8%, p < 0.001). After adjustment for potential con-founders the odds of being treated with rt-PA remained lower for women regardless of presentation to a PSC (OR 0.87, 95% CI 0.81-0.94) or non-PSC (OR 0.88, 95% CI 0.82-0.94). After stratifying by sex and race, the lowest absolute treatment rates were observed in black women (4.4% at PSC, 1.9% at non-PSC). The odds of treatment, relative to white men, was however lowest for white women (PSC OR = 0.85, 95% CI 0.78-0.93; non-PSC OR = 0.80, 95% CI 0.75-0.85). In the multivariable model, sex did not modify the effect of PSC certification on rt-PA utilization (p-value for interaction = 0.58). conclusion: Women are less likely to receive rt-PA than men at both PSCs and non-PSCs. Absolute treatment rates are lowest in black women, although the relative difference in men and women was greatest for white women. [ABSTRACT FROM AUTHOR]

Background: Accurate recognition of stroke symptoms by Emergency Medical Services (EMS) is necessary for timely care of acute stroke patients. We assessed the accuracy of stroke diagnosis by EMS in clinical practice in a major US city. Methods and results: Philadelphia Fire Department data were merged with data from a single comprehensive stroke center to identify patients diagnosed with stroke or TIA from 9/2009 to 10/2012. Sensitivity and positive predictive value (PPV) were calculated. Multivariable logistic regression identified variables associated with correct EMS diagnosis. There were 709 total cases, with 400 having a discharge diagnosis of stroke or TIA. EMS crew sensitivity was 57.5% and PPV was 69.1%. EMS crew identified 80.2% of strokes with National Institutes of Health Stroke Scale (NIHSS) ≥5 and symptom duration <6 h. In a multivariable model, correct EMS crew diagnosis was positively associated with NIHSS (NIHSS 5-9, OR 2.62, 95% CI 1.41-4.89; NIHSS ≥10, OR 4.56, 95% CI 2.29-9.09) and weakness (OR 2.28, 95% CI 1.35-3.85), and negatively associated with symptom duration >270 min (OR 0.41, 95% CI 0.25-0.68). EMS dispatchers identified 90 stroke cases that the EMS crew missed. EMS dispatcher or crew identified stroke with sensitivity of 80% and PPV of 50.9%, and EMS dispatcher or crew identified 90.5% of patients with NIHSS ≥5 and symptom duration <6 h. conclusion: Prehospital diagnosis of stroke has limited sensitivity, resulting in a high proportion of missed stroke cases. Dispatchers identified many strokes that EMS crews did not. Incorporating EMS dispatcher impression into regional protocols may maximize the effectiveness of hospital destination selection and pre-notification. [ABSTRACT FROM AUTHOR]

Background: Methods for comparing hospitals regarding cardiac arrest (CA) outcomes, vital for improving resuscitation performance, rely on data collected by cardiac arrest registries. However, most CA patients are treated at hospitals that do not participate in such registries. This study aimed to determine whether CA risk standardization modeling based on administrative data could perform as well as that based on registry data. Methods and results: Two risk standardization logistic regression models were developed using 2453 patients treated from 2000–2015 at three hospitals in an academic health system. Registry and administrative data were accessed for all patients. The outcome was death at hospital discharge. The registry model was considered the “gold standard” with which to compare the administrative model, using metrics including comparing areas under the curve, calibration curves, and Bland-Altman plots. The administrative risk standardization model had a c-statistic of 0.891 (95% CI: 0.876–0.905) compared to a registry c-statistic of 0.907 (95% CI: 0.895–0.919). When limited to only non-modifiable factors, the administrative model had a c-statistic of 0.818 (95% CI: 0.799–0.838) compared to a registry c-statistic of 0.810 (95% CI: 0.788–0.831). All models were well-calibrated. There was no significant difference between c-statistics of the models, providing evidence that valid risk standardization can be performed using administrative data. Conclusions: Risk standardization using administrative data performs comparably to standardization using registry data. This methodology represents a new tool that can enable opportunities to compare hospital performance in specific hospital systems or across the entire US in terms of survival after CA. [ABSTRACT FROM AUTHOR]

As part of the 2016 Academic Emergency Medicine Consensus Conference, 'Shared Decision Making in the Emergency Department: Development of a Policy-relevant Patient-centered Research Agenda,' a panel of representatives from the Office of Emergency Care Research, the Patient-Centered Outcomes Research Institute, the American Heart Association, the John A. Hartford Foundation, and the Emergency Care Coordination Center were assembled to discuss funding opportunities for future research in this field. This article summarizes their discussion of funding priorities and examples of successfully funded projects related to shared decision making in emergency medicine. [ABSTRACT FROM AUTHOR]

Objective:Hospital arrival via Emergency Medical Services (EMS) and EMS prenotification are associated with faster evaluation and treatment of stroke. We sought to determine the impact of diagnostic accuracy by prehospital providers on emergency department quality measures.Methods:A retrospective study was performed of patients presenting via EMS between September 2009 and December 2012 with a discharge diagnosis of transient ischemic attack (TIA), ischemic stroke (IS), or intracerebral hemorrhage (ICH). Hospital and EMS databases were used to determine EMS impression, prehospital and in-hospital time intervals, EMS prenotification, NIH stroke scale (NIHSS), symptom duration, and thrombolysis rate.Results:399 cases were identified: 14.5% TIA, 67.2% IS, and 18.3% ICH. EMS providers correctly recognized 57.6% of cases. Compared to cases missed by EMS, correctly recognized cases had longer median on-scene time (17 vs. 15 min,p= 0.01) but shorter transport times (12 vs. 15 min,p= 0.001). Cases correctly recognized by EMS were associated with shorter door-to-physician time (4 vs. 11 min,p< 0.001) and shorter door-to-CT time (23 vs. 48 min,p< 0.001). These findings were independent of age, NIHSS, symptom duration, and EMS prenotification. Patients with ischemic stroke correctly recognized by EMS were more likely to receive thrombolytic therapy, independent of age, NIHSS, symptom duration both with and without prenotification.Conclusion:Recognition of stroke by EMS providers was independently associated with faster door-to-physician time, faster door-to-CT time, and greater odds of receiving thrombolysis. Quality initiatives to improve EMS recognition of stroke have the potential to improve hospital-based quality of stroke care. [ABSTRACT FROM PUBLISHER]

Total population health is a key tenet of health care reform efforts, evident in initiatives such as the National Quality Strategy, shifts toward population-based payments, and community benefit requirements for tax-exempt hospitals. Representing total population health in a way that guides best practices and establishes shared accountability for geographic communities, however, remains a challenge in part because of differences in howstakeholders define populations. To better understand the landscape of potential denominators for population health, this study examined a selection of relevant geographic units. The approach included a comprehensive review of health services and public health research literature as well as recent pertinent health policy documents. Units were characterized based on whether they: exhibit "breadth" of coverage across the whole US population; are "accurate" or grounded in health care utilization patterns; are "actionable" with mechanisms for implementing funding and regulation; and promote "synergism" or effective coordination of public health and health care activities. Although other key components of a total population health unit may exist and no single identified unit possesses all of the aforementioned features, several promising candidates were identified. Specifically, healthcare coalitions link health care and public health domains to care for a geographic community, but their connection to utilization is not empiric and limited funding exists at the coalition level. Although Accountable Care Organizations do not uniformly incorporate public health or facilitate coordination across all payers or providers, they represent an effective mechanism to increase collaboration within health care systems and represent a potential building block to influence total population health. [ABSTRACT FROM AUTHOR]

Background Family-centered rounds involve purposeful interactions between patients' families and care providers to refocus the delivery of care on patients' needs. Objectives To examine perspectives of patients' family members and health care providers on family participation in rounds in the surgical intensive care unit (ICU) and the potential use of telemedicine to facilitate this process. Methods Patients' family members and surgical ICU care providers were recruited for semistructured interviews exploring stakeholders' perspectives on family participation in ICU rounds and the potential role of telemedicine. Thirty-two interviews were conducted, audio recorded, and transcribed verbatim. Common coding methods were facilitated by using NVivo 10. A mean coding agreement of 97.3% was calculated for 22% of transcripts. Results Both patients' family members and health care providers described inconsistent practices surrounding family participation in ICU rounds as well as barriers to and facilitators of family participation. Family members identified 3 primary logistical challenges to participation in ICU rounds: distance to hospitals, work/family obligations, and the rounding schedule. Both family members and providers reported receptivity to virtual participation as a potential solution to these challenges. Conclusions Understanding the barriers to and facilitators of family participation in ICU rounds is key to encouraging adoption of family-centered rounds. For families that live far away or have competing demands, telemedical options may facilitate participation. [ABSTRACT FROM AUTHOR]

The Patient-Centered Outcomes Research Institute ( PCORI) was established by Congress in 2010 to promote the conduct of research that could better inform patients in making decisions that reflect their desired health outcomes. PCORI has established five national priorities for research around which specific funding opportunities are issued: 1) assessment of prevention, diagnosis, and treatment options; 2) improving healthcare systems; 3) communication and dissemination research; 4) addressing disparities; and 5) improving methods for conducting patient-centered outcomes research. To date, implementation of patient-centered research in the emergency care setting has been limited, in part because of perceived challenges in meeting PCORI priorities such as the need to focus on a specific disease state or to have planned follow up. We suggest that these same factors that have been seen as challenges to performing patient-centered research within the emergency setting are also potential strengths to be leveraged to conduct PCORI research. This paper explores factors unique to patient-centered emergency care research and highlights specific areas of potential alignment within each PCORI priority. [ABSTRACT FROM AUTHOR]

Hurricane Sandy struck New York City on October 29, 2012, causing not only a large amount of physical damage, but also straining people's health and disrupting health care services throughout the city. In prior research, we determined that emergency department (ED) visits from the most vulnerable hurricane evacuation flood zones in New York City increased after Hurricane Sandy for several medical diagnoses, but also for the diagnosis of homelessness. In the current study, we aimed to further explore this increase in ED visits for homelessness after Hurricane Sandy's landfall. We performed an observational before-and-after study using an all-payer claims database of ED visits in New York City to compare the demographic characteristics, insurance status, geographic distribution, and health conditions of ED patients with a primary or secondary ICD-9 diagnosis of homelessness or inadequate housing in the first week after Hurricane Sandy's landfall versus the baseline weekly average in 2012 prior to Hurricane Sandy. We found statistically significant increases in ED visits for diagnosis codes of homelessness or inadequate housing in the week after Hurricane Sandy's landfall. Those accessing the ED for homelessness or inadequate housing were more often elderly and insured by Medicare after versus before the hurricane. Secondary diagnoses among those with a primary ED diagnosis of homelessness or inadequate housing also differed after versus before Hurricane Sandy. These observed differences in the demographic, insurance, and co-existing diagnosis profiles of those with an ED diagnosis of homelessness or inadequate housing before and after Hurricane Sandy suggest that a new population cohort-potentially including those who had lost their homes as a result of storm damage-was accessing the ED for homelessness or other housing issues after the hurricane. Emergency departments may serve important public health and disaster response roles after a hurricane, particularly for people who are homeless or lack adequate housing. Further, tracking ED visits for homelessness may represent a novel surveillance mechanism to assess post-disaster infrastructure impact and to prepare for future disasters. [ABSTRACT FROM AUTHOR]

Racial and ethnic disparities have been previously reported in acute stroke care. We sought to determine the effect of telemedicine (TM) on access to acute stroke care for racial and ethnic minorities in the state of Texas. Data were collected from the US Census Bureau, The Joint Commission and the American Hospital Association. Access for racial and ethnic minorities was determined by summing the population that could reach a primary stroke centre (PSC) or telemedicine spoke within specified time intervals using validated models. TM extended access to stroke expertise by 1.5 million residents. The odds of providing 60-minute access via TM were similar in Blacks and Whites (prevalence odds ratios (POR) 1.000, 95% CI 1.000-1.000), even after adjustment for urbanization (POR 1.000, 95% CI 1.000-1.001). The odds of providing access via TM were also similar for Hispanics and non-Hispanics (POR 1.000, 95% CI 1.000-1.000), even after adjustment for urbanization (POR 1.000, 95% CI 1.000-1.000). We found that telemedicine increased access to acute stroke care for 1.5 million Texans. While racial and ethnic disparities exist in other components of stroke care, we did not find evidence of disparities in access to the acute stroke expertise afforded by telemedicine. [ABSTRACT FROM AUTHOR]

Administrative data are critical to describing patterns of use, cost, and appropriateness of imaging in emergency care. These data encompass a range of source materials that have been collected primarily for a nonresearch use: documenting clinical care (e.g., medical records), administering care (e.g., picture archiving and communication systems), or financial transactions (e.g., insurance claims). These data have served as the foundation for large, descriptive studies that have documented the rise and expanded role of diagnostic imaging in the emergency department (ED). This article summarizes the discussions of the breakout session on the use of administrative data for emergency imaging research at the May 2015 Academic Emergency Medicine consensus conference, "Diagnostic Imaging in the Emergency Department: A Research Agenda to Optimize Utilization." The authors describe the areas where administrative data have been applied to research evaluating the use of diagnostic imaging in the ED, the common sources for these data, and the strengths and limitations of administrative data. Next, the future role of administrative data is examined for answering key research questions in an evolving health system increasingly focused on measuring appropriateness, ensuring quality, and improving value for health spending. This article specifically focuses on four thematic areas: data quality, appropriateness and value, special populations, and policy interventions. [ABSTRACT FROM AUTHOR]

Emergency visits are rising nationally, whereas the number of emergency departments is shrinking. However, volume has not increased uniformly at all emergency departments. It is unclear what factors account for this variability in emergency volume growth rates. The objective of this study was to test the association of hospital and population characteristics and the effect of hospital closures with increases in emergency department volume. The study team analyzed emergency department volume at New York State hospitals from 2004 to 2010 using data from cost reports and administrative databases. Multivariate regression was used to evaluate characteristics associated with emergency volume growth. Spatial analytics and distances between hospitals were used in calculating the predicted impact of hospital closures on emergency department use. Among the 192 New York hospitals open from 2004 to 2010, the mean annual increase in emergency department visits was 2.7%, but the range was wide (-5.5% to 11.3%). Emergency volume increased nearly twice as fast at tertiary referral centers (4.8%) and nonurban hospitals (3.7% versus urban at 2.1%) after adjusting for other characteristics. The effect of hospital closures also strongly predicted variation in growth. Emergency volume is increasing faster at specific hospitals: tertiary referral centers, nonurban hospitals, and those near hospital closures. This study provides an understanding of how emergency volume varies among hospitals and predicts the effect of hospital closures in a statewide region. Understanding the impact of these factors on emergency department use is essential to ensure that these populations have access to critical emergency services. ( Population Health Management 2015;18:459-466) [ABSTRACT FROM AUTHOR]

The article offers information on a study which aims at describing how Accountable Care Organizations (ACOs) are prioritizing costreduction strategies related to acute unscheduled care using responses from a national survey of ACOs. It mentions that ACOs prioritize strategies to reduce emergency departments (ED) visits above reducing potentially avoidable inpatient admissions from the ED, despite significantly larger health care expenditures associated with inpatient hospitalizations.

Background. Ischemic stroke is a time sensitive disease with the effectiveness of treatment decreasing over time. Treatment is more likely to occur at Primary Stroke Centers (PSC); thus rapid access to acute stroke care through stand-alone PSCs or telemedicine (TM) is vital for all Americans. The objective of this study is to determine if disparities exist in access to PSCs or the extended access to acute stroke care provided by TM. Methods. Data from the US Census Bureau and the 2010 Neilson Claritas Demographic Estimation Program, American Hospital Association annual survey, and The Joint Commission list of PSCs and survey response data for all hospitals in the state of Texas were used. Results. Over 64% of block groups had 60-minute ground access to acute stroke care. The odds of a block group having 60-minute access to acute stroke care decreased with age, despite adjustment for sex, race, ethnicity, socioeconomic status, urbanization, and total population. Conclusion. Our survey of Texas hospitals found that as the median age of a block group increased, the odds of having access to acute stroke care decreased. [ABSTRACT FROM AUTHOR]

Objectives. We sought to improve public health surveillance by using a geographic analysis of emergency department (ED) visits to determine local chronic disease prevalence. Methods. Using an all-payer administrative database, we determined the proportion of unique ED patients with diabetes, hypertension, or asthma. We compared these rates to those determined by the New York City Community Health Survey. For diabetes prevalence, we also analyzed the fidelity of longitudinal estimates using logistic regression and determined disease burden within census tracts using geocoded addresses. Results. We identified 4.4 million unique New York City adults visiting an ED between 2009 and 2012. When we compared our emergency sample to survey data, rates of neighborhood diabetes, hypertension, and asthma prevalence were similar (correlation coefficient = 0.86, 0.88, and 0.77, respectively). In addition, our method demonstrated less year-to-year scatter and identified significant variation of disease burden within neighborhoods among census tracts. Conclusions. Our method for determining chronic disease prevalence correlates with a validated health survey and may have higher reliability overtime and greater granularity at a local level. Our findings can improve public health surveillance by identifying local variation of disease prevalence. [ABSTRACT FROM AUTHOR]

Objectives This study explored what smartphone health applications (apps) are used by patients, how they learn about health apps, and how information about health apps is shared. Methods Patients seeking care in an academic ED were surveyed about the following regarding their health apps: use, knowledge, sharing, and desired app features. Demographics and health information were characterized by summary statistics. Results Of 300 participants, 212 (71%) owned smartphones, 201 (95%) had apps, and 94 (44%) had health apps. The most frequently downloaded health apps categories were exercise 46 (49%), brain teasers 30 (32%), and diet 23 (24%). The frequency of use of apps varied as six (6%) of health apps were downloaded but never used, 37 (39%) apps were used only a few times, and 40 (43%) health apps were used once per month. Only five apps (2%) were suggested to participants by health care providers, and many participants used health apps intermittently (55% of apps ≤ once a month). Participants indicated sharing information from 64 (59%) health apps, mostly within social networks (27 apps, 29%) and less often with health care providers (16 apps, 17%). Conclusions While mobile health has experienced tremendous growth over the past few years, use of health apps among our sample was low. The most commonly used apps were those that had broad functionality, while the most frequently used health apps encompassed the topics of exercise, diet, and brain teasers. While participants most often shared information about health apps within their social networks, information was less frequently shared with providers, and physician recommendation played a small role in influencing patient use of health apps. [ABSTRACT FROM AUTHOR]