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We determine how pediatric emergency department (ED) visits changed during the COVID-19 pandemic in a large sample of U.S. EDs. Using retrospective data from January–June 2020, compared to a similar 2019 period, we calculated weekly 2020–2019 ratios of Non-COVID-19 ED visits for adults and children (age 18 years or less) by age range. Outcomes were pediatric ED visit rates before and after the onset of pandemic, by age, disposition, and diagnosis. We included data from 2,213,828 visits to 144 EDs and 4 urgent care centers in 18 U.S. states, including 7 EDs in children's hospitals. During the pandemic period, adult non-COVID-19 visits declined to 60% of 2019 volumes and then partially recovered but remained below 2019 levels through June 2020. Pediatric visits declined even more sharply, with peak declines through the week of April 15 of 74% for children age < 10 years and 67% for 14–17 year. Visits recovered by June to 72% for children age 14–17, but to only 50% of 2019 levels for children < age 10 years. Declines were seen across all ED types and locations, and across all diagnoses, with an especially sharp decline in non-COVID-19 communicable diseases. During the pandemic period, there was 22% decline in common serious pediatric conditions, including appendicitis. Pediatric ED visits fell more sharply than adult ED visits during the COVID-19 pandemic, and remained depressed through June 2020, especially for younger children. Declines were also seen for serious conditions, suggesting that parents may have avoided necessary care for their children.

We examine how emergency department (ED) visits for serious cardiovascular conditions evolved in the coronavirus (COVID-19) pandemic over January–October 2020, compared to 2019, in a large sample of U.S. EDs. We compared 2020 ED visits before and during the COVID-19 pandemic, relative to 2019 visits in 108 EDs in 18 states in 115,716 adult ED visits with diagnoses for five serious cardiovascular conditions: ST-segment elevation myocardial infarction (STEMI), non-ST-segment elevation myocardial infarction (NSTEMI), ischemic stroke (IS), hemorrhagic stroke (HS), and heart failure (HF). We calculated weekly ratios of ED visits in 2020 to visits in 2019 in the pre-pandemic (Jan 1-March 10), early-pandemic (March 11–April 21), and later-pandemic (April 22–October 31) periods. ED visit ratios show that NSTEMI, IS, and HF visits dropped to lows of 56%, 64%, and 61% of 2019 levels, respectively, in the early-pandemic and gradually returned to 2019 levels over the next several months. HS visits also dropped early pandemic period to 60% of 2019 levels, but quickly rebounded. We find mixed evidence on whether STEMI visits fell, relative to pre-pandemic rates. Total adult ED visits nadired at 57% of 2019 volume during the early-pandemic period and have only party recovered since, to approximately 84% of 2019 by the end of October 2020. We confirm prior studies that ED visits for serious cardiovascular conditions declined early in the COVID-19 pandemic for NSTEMI, IS, HS, and HF, but not for STEMI. Delays or non-receipt in ED care may have led to worse outcomes.

Emergency department (ED) care is a focus of cost reduction efforts. Costs for acute care originating in the ED, including outpatient and inpatient encounters (i.e. ED episodes), have not been estimated. We estimate total US costs of ED episodes, potentially avoidable costs, and proportional costs of national health expenditures (NHEs). We conducted a secondary analysis of 2010 data from the Medical Expenditure Panel Survey, National Hospital Ambulatory Medical Care Survey, and the Healthcare Cost and Utilization Project's Nationwide Inpatient Sample. Outpatient ED encounters were categorized based on the New York University algorithm and admissions by ambulatory care–sensitive condition (ACSC) vs non-ACSC. Potentially avoidable encounters were nonemergent ED visits and ACSC hospital admissions. Using the Medical Expenditure Panel Survey, we determined mean per-visit payments for each visit type. Using the National Hospital Ambulatory Medical Care Survey and Nationwide Inpatient Sample, we estimated aggregate expenditures and proportional costs of NHE by visit category. Emergency department episodes of care accounted for $328.1 billion in payments in 2010. This represented 12.5% of NHE; ED admissions were 8.3% and outpatient ED care was 4.2%. Nonemergent outpatient visits were the most common, comprising 30.4% of ED episodes, and non-ACSC admissions were the most costly at $188.3 billion. Potentially avoidable encounters accounted for $64.4 billion, 19.6% of ED episodes, and 2.4% of NHE. More than 1 in 10 health care dollars is spent on ED episodes of care. Of this, less than 1 in 5 dollars is potentially avoidable; therefore, efforts to reduce ED visits through improved primary care may have little impact on overall costs.

Artificial intelligence (AI) in healthcare is the ability of a computer to perform tasks typically associated with clinical care (e.g. medical decision-making and documentation). AI will soon be integrated into an increasing number of healthcare applications, including elements of emergency department (ED) care. Here, we describe the basics of AI, various categories of its functions (including machine learning and natural language processing) and review emerging and potential future use-cases for emergency care. For example, AI-assisted symptom checkers could help direct patients to the appropriate setting, models could assist in assigning triage levels, and ambient AI systems could document clinical encounters. AI could also help provide focused summaries of charts, summarize encounters for hand-offs, and create discharge instructions with an appropriate language and reading level. Additional use cases include medical decision making for decision rules, real-time models that predict clinical deterioration or sepsis, and efficient extraction of unstructured data for coding, billing, research, and quality initiatives. We discuss the potential transformative benefits of AI, as well as the concerns regarding its use (e.g. privacy, data accuracy, and the potential for changing the doctor-patient relationship).

The study authors also suggest that the TIDieR checklist could be supported by editorial organizations like International Committee of Medical Journals (ICJME), which historically helped boost the recognition of CONSORT when it was originally published. Real world data may diverge somewhat, but this is potentially explicable through patient-level factors because trials – particularly drug trials – are designed to focus on populations where the effect is most pronounced or adverse events are minimized. The success or failure of initiatives may also be highly context dependent, meaning a particular factor related to how it was implemented in a certain setting may be responsible for its efficacy or lack thereof [5]. [...]groups aiming to replicate the intervention without this information may do so ineffectively, wasting valuable time, resources, and money.

Background Diagnostic testing and treatment recommendations can vary when medical care is sought by individuals for low back pain (LBP), leading to variation in quality and costs of care. We examine how the first provider seen by an individual at initial diagnosis of LBP influences downstream utilization and costs. Methods Using national private health insurance claims data, individuals age 18 or older were retrospectively assigned to cohorts based on the first provider seen at the index date of LBP diagnosis. Exclusion criteria included individuals with a diagnosis of LBP or any serious medical conditions or an opioid prescription recorded in the 6 months prior to the index date. Outcome measures included use of imaging, back surgery rates, hospitalization rates, emergency department visits, early- and long-term opioid use, and costs (out-of-pocket and total costs of care) twelve months post-index date. We used a two-stage residual inclusion (2SRI) estimation approach comparing copay for the initial provider visit and differential distance as the instrumental variable to reduce selection bias in the choice of first provider, controlling for demographics. Results Among 3,799,593 individuals, cost and utilization varied considerably based on the first provider seen by the patient. Copay and differential distance provided similar results, with copay preserving a greater sample size. The frequency of early opioid prescription was significantly lower when care began with an acupuncturist or chiropractor, and highest for those who began with an emergency medicine physician or advanced practice registered nurse (APRN). Long-term opioid prescriptions were low across most providers except physical medicine and rehabilitation physicians and APRNs. The frequency and time to serious illness varied little across providers. Total cost of care was lowest when starting with a chiropractor ($5093) or primary care physician ($5660), and highest when starting with an orthopedist ($9434) or acupuncturist ($9205). Conclusion The first provider seen by individuals with LBP was associated with large differences in health care utilization, opioid prescriptions, and cost while there were no differences in delays in diagnosis of serious illness.

During and after the storm, many of Houston's acute care hospitals and clinics closed or provided limited services [3]. In Hurricane Sandy, there were increases in ED visits in surrounding EDs outside of lower Manhattan that appeared in the immediate post-storm period; however, visits rapidly move back to baseline within a week [4]. [...]FSEDs in Houston surged up to a peak >100% in volume during Hurricane Harvey and visit volumes remained elevated for 9days after the storm.

Recent health reforms have created incentives for cardiologists and accountable care organizations to participate in value-based care models for heart failure (HF). Accurate risk stratification of HF patients is critical to efficiently deploy interventions aimed at reducing preventable utilization. The goal of this paper was to compare deep learning approaches with traditional logistic regression (LR) to predict preventable utilization among HF patients. We conducted a prognostic study using data on 93,260 HF patients continuously enrolled for 2-years in a large U.S. commercial insurer to develop and validate prediction models for three outcomes of interest: preventable hospitalizations, preventable emergency department (ED) visits, and preventable costs. Patients were split into training, validation, and testing samples. Outcomes were modeled using traditional and enhanced LR and compared to gradient boosting model and deep learning models using sequential and non-sequential inputs. Evaluation metrics included precision (positive predictive value) at k, cost capture, and Area Under the Receiver operating characteristic (AUROC). Deep learning models consistently outperformed LR for all three outcomes with respect to the chosen evaluation metrics. Precision at 1% for preventable hospitalizations was 43% for deep learning compared to 30% for enhanced LR. Precision at 1% for preventable ED visits was 39% for deep learning compared to 33% for enhanced LR. For preventable cost, cost capture at 1% was 30% for sequential deep learning, compared to 18% for enhanced LR. The highest AUROCs for deep learning were 0.778, 0.681 and 0.727, respectively. These results offer a promising approach to identify patients for targeted interventions.

AbstractIntroductionUnited States emergency medicine (EM) post-graduate training programs vary in training length, either 4 or 3 years. However, it is unknown if clinical care by graduates from the two curricula differs in the early post-residency period. MethodsWe performed a retrospective observational study comparing measures of clinical care and practice patterns between new graduates from 4- and 3-year EM programs with experienced new physician hires as a reference group. We included emergency department (ED) encounters from a national EM group (2016–19) between newly hired physicians from 4- and 3- year programs and experienced new hires (>2 years' experience) during their first year of practice with the group. Primary outcomes were at the physician-shift level (patients per hour and relative value units [RVUs] per hour) and encounter-level (72-h return visits with admission/transfer and discharge length of stay [LOS]). Secondary outcomes included discharge opioid prescription rates, test ordering, computer tomography (CT) use, and admission/transfer rate. We compared outcomes using multivariable linear regression models that included patient, shift, and facility-day characteristics, and a facility fixed effect. We hypothesized that experienced new hires would be most efficient, followed by new 4-year graduates and then new 3-year graduates. ResultsWe included 1,084,085 ED encounters by 4-year graduates ( n = 39), 3-year graduates ( n = 70), and experienced new hires ( n = 476). There were no differences in physician-level and encounter-level primary outcomes except discharge LOS was 10.60 min (2.551, 18.554) longer for 4-year graduates compared to experienced new hires. Secondary outcomes were similar among the three groups except 4- and 3-year new graduates were less likely to prescribe opioids to discharged patients, −3.70% (−5.768, −1.624) and − 3.38% (−5.136, −1.617) compared to experienced new hires. ConclusionsIn this sample, measures of clinical care and practice patterns related to efficiency, safety, and flow were largely similar between the physician groups; however, experienced new hires were more likely to prescribe opioids than new graduates. These results do not support recommending a specific length of residency training in EM.

We explored Hospital Compare data on emergency department (ED) crowding metrics to assess characteristics of reporting vs nonreporting hospitals, whether hospitals ranked as the US News Best Hospitals (2012-2013) vs unranked hospitals differed in ED performance and relationships between ED crowding and other reported hospital quality measures. An ecological study was conducted using data from Hospital Compare data sets released March 2013 and from a popular press publication, US News Best Hospitals 2012 to 2013. We compared hospitals on 5 ED crowding measures: left-without-being-seen rates, waiting times, boarding times, and length of stay for admitted and discharged patients. Of 4810 hospitals included in the Hospital Compare sample, 2990 (62.2%) reported all ED 5 crowding measures. Median ED length of stay for admitted patients was 262 minutes (interquartile range [IQR], 215-326), median boarding was 88 minutes (IQR, 60-128), median ED length of stay for discharged patients was 139 minutes (IQR, 114-168), and median waiting time was 30 minutes (IQR, 20-44). Hospitals ranked as US News Best Hospitals 2012 to 2013 (n = 650) reported poorer performance on ED crowding measures than unranked hospitals (n = 4160) across all measures. Emergency department boarding times were associated with readmission rates for acute myocardial infarction (r = 0.14, P < .001) and pneumonia (r = 0.17, P < .001) as well as central line–associated bloodstream infections (r = 0.37, P < .001). There is great variation in measures of ED crowding across the United States. Emergency department crowding was related to several measures of in-patient quality, which suggests that ED crowding should be a hospital-wide priority for quality improvement efforts.