Search

This study examines publication timelines, completeness, and spin in the abstracts of all randomized clinical trials related to COVID-19 posted to medRxiv during the first 2 years of the pandemic and compared the latter 2 with their published counterparts.

One in 4 deaths from COVID-19 has been attributed to hospital crowding. We simulated how many ambulances would be required to rebalance hospital load through systematic interhospital transfers. We assessed the potential feasibility of such a strategy and explored whether transfer requirement was a helpful measure and visualization of regional hospital crowding during COVID-19 surges.Using data from the United States hospitals reporting occupancy to the Department of Health and Human Services from July 2020 to March 2022 and road network driving times, we estimated the number of ambulances required weekly to relieve overcapacity hospitals.During the peak week, which ended on January 8, 2021, approximately 1,563 ambulances would be needed for 15,389 simulated patient transports, of which 6,530 (42%) transports involved a 1-way driving time of more than 3 hours. Transfer demands were dramatically lower during most other weeks, with the median week requiring only 134 ambulances (interquartile range, 84 to 295) and involving only 116 transports with 1-way driving times above 3 hours (interquartile range, 4 to 548). On average, receiving hospitals were larger and located in more rural areas than sending hospitals.This simulation demonstrated that for most weeks during the pandemic, ambulance availability and bed capacity were unlikely to have been the main impediments to rebalancing hospital loads. Our metric provided an immediately available and much more complete measure of hospital system strain than counts of hospital admissions alone.

Objective To test whether providing relevant clinical trial registry information to peer reviewers evaluating trial manuscripts decreases discrepancies between registered and published trial outcomes. Design Stepped wedge, cluster-randomised trial, with clusters comprised of eligible manuscripts submitted to each participating journal between 1 November 2018 and 31 October 2019. Setting Thirteen medical journals. Participants Manuscripts were eligible for inclusion if they were submitted to a participating journal during the study period, presented results from the primary analysis of a clinical trial, and were peer reviewed. Interventions During the control phase, there were no changes to pre-existing peer review practices. After journals crossed over into the intervention phase, peer reviewers received a data sheet describing whether trials were registered, the initial registration and enrolment dates, and the registered primary outcome(s) when enrolment began. Main outcome measure The presence of a clearly defined, prospectively registered primary outcome consistent with the primary outcome in the published trial manuscript, as determined by two independent outcome assessors. Results We included 419 manuscripts (243 control and 176 intervention). Participating journals published 43% of control-phase manuscripts and 39% of intervention-phase manuscripts (model-estimated percentage difference between intervention and control trials = −10%, 95% CI −25% to 4%). Among the 173 accepted trials, published primary outcomes were consistent with clearly defined, prospectively registered primary outcomes in 40 of 105 (38%) control-phase trials and 27 of 68 (40%) intervention-phase trials. A linear mixed model did not show evidence of a statistically significant primary outcome effect from the intervention (estimated difference between intervention and control=−6% (90% CI −27% to 15%); one-sided p value=0.68). Conclusions These results do not support use of the tested intervention as implemented here to increase agreement between prospectively registered and published trial outcomes. Other approaches are needed to improve the quality of outcome reporting of clinical trials. Trial registration number NCT41225307.

Background The peer review process has been questioned as it may fail to allow the publication of high-quality articles. This study aimed to evaluate the accuracy in identifying inadequate reporting in RCT reports by early career researchers (ECRs) using an online CONSORT-based peer-review tool (COBPeer) versus the usual peer-review process. Methods We performed a cross-sectional diagnostic study of 119 manuscripts, from BMC series medical journals, BMJ, BMJ Open, and Annals of Emergency Medicine reporting the results of two-arm parallel-group RCTs. One hundred and nineteen ECRs who had never reviewed an RCT manuscript were recruited from December 2017 to January 2018. Each ECR assessed one manuscript. To assess accuracy in identifying inadequate reporting, we used two tests: (1) ECRs assessing a manuscript using the COBPeer tool (after completing an online training module) and (2) the usual peer-review process. The reference standard was the assessment of the manuscript by two systematic reviewers. Inadequate reporting was defined as incomplete reporting or a switch in primary outcome and considered nine domains: the eight most important CONSORT domains and a switch in primary outcome(s). The primary outcome was the mean number of domains accurately classified (scale from 0 to 9). Results The mean (SD) number of domains (0 to 9) accurately classified per manuscript was 6.39 (1.49) for ECRs using COBPeer versus 5.03 (1.84) for the journal’s usual peer-review process, with a mean difference [95% CI] of 1.36 [0.88–1.84] (p Conclusions Trained ECRs using the COBPeer tool were more likely to detect inadequate reporting in RCTs than the usual peer review processes used by journals. Implementing a two-step peer-review process could help improve the quality of reporting. Trial registration Clinical.Trials.govNCT03119376 (Registered April, 18, 2017).

Study objective Patients with acute psychiatric emergencies who receive an involuntary hold often spend hours in the emergency department (ED) because of a deficit in inpatient psychiatric beds. One solution to address the lack of prompt psychiatric evaluation in the ED has been to establish regional stand-alone psychiatric emergency services. However, patients receiving involuntary holds still need to be screened and evaluated to ensure that their behavior is not caused by an underlying and life-threatening nonpsychiatric illness. Although traditional regional emergency medical services (EMS) systems depend on the medical ED for this function, a field-screening protocol can allow EMS to directly transport a substantial portion of patients to a stand-alone psychiatric emergency service. The purpose of this investigation is to describe overall EMS use for patients receiving involuntary holds, compare patients receiving involuntary holds with all EMS patients, and evaluate the safety of field medical clearance of an established field-screening protocol in Alameda County, CA. Methods We obtained data for all EMS encounters between November 1, 2011, and November 1, 2016, using Alameda County's standardized data set. After unique patient identification, we describe the data at the patient level and at the encounter level. At the patient level, we compare "involuntary hold patients" (≥1 involuntary hold during the study period) with those who were "never held." Additionally, we assess the safety of out-of-hospital medical clearance by calculating the rate of failed diversion, defined as retransport of a patient to a medical ED within 12 hours of transport to the psychiatric emergency services by EMS. Results Of the 541,731 total EMS encounters in Alameda County during the study period, 10% (N=53,887) were identified as involuntary hold encounters. Of these involuntary hold patient encounters, 41% (N=22,074) resulted in direct transport of the patient to the stand-alone psychiatric emergency service for evaluation; 0.3% (N=60) failed diversion and required retransport within 12 hours. At the patient level, Alameda County EMS encountered 257,625 unique patients, and 10% (N=26,283) had at least one encounter for an involuntary hold during the study period. These "involuntary hold patients" were substantially younger, more likely to be men, and less likely to be insured. Additionally, they had higher overall EMS use: "involuntary hold patients" accounted for 24% of all encounters (N=128,003); 53,887 of these encounters were for involuntary holds, whereas an additional 74,116 were for other reasons. Similarly, 4% of "involuntary hold patients" had 20 or more encounters, whereas only 0.4% of "never held" patients were in this category. Last, the 7% of "involuntary hold patients" (N=1,907) who received greater than or equal to 5 involuntary holds during the study period accounted for 39% of all involuntary holds and 9% of all EMS encounters. Conclusion Ten percent of all EMS encounters were for involuntary psychiatric holds. With an EMS-directed screening protocol, 41% of all such patient encounters resulted in direct transport of the patient to the psychiatric emergency service, bypassing medical clearance in the ED. Overall, only 0.3% of these patients required retransport to a medical ED within 12 hours of arrival to psychiatric emergency services. We found that 24% of all EMS encounters in Alameda County were attributable to "involuntary hold patients," reinforcing the importance of the effects of mental illness on EMS use.

Neonatal lumbar punctures (LPs) are most often performed when a baby younger than 30 days of age presents with a fever.1,2,3 The procedure can be challenging, even for experienced providers.4 The challenge is compounded when the patient is having respiratory or other issues such that they cannot tolerate a sitting or lateral decubitus position. Various methods for optimizing of the success of neonatal LPs have been proposed and studied.5,6,7 Nevertheless, failure rates range up to 40% in some studies.2,4,5 Traditionally, neonatal LPs have been performed in the lateral decubitus position or the sitting position, both of which require firm holding to maintain proper spacing of the spinous processes, a distance that is widely regarded as crucial to successful LP.

Background Shareable e-scooters have become popular, but injuries to riders and bystanders have not been well characterized. The goal of this study was to describe e-scooter injuries and estimate the rate of injury per e-scooter trip. Methods and findings Retrospective review of patients presenting to 180 clinics and 2 hospitals in greater Los Angeles between January 1, 2014 and May 14, 2020. Injuries were identified using a natural language processing (NLP) algorithm not previously used to identify injuries, tallied, and described along with required healthcare resources. We combine these tallies with municipal data on scooter use to report a monthly utilization-corrected rate of e-scooter injuries. We searched 36 million clinical notes. Our NLP algorithm correctly classified 92% of notes in the testing set compared with the gold standard of investigator review. In total, we identified 1,354 people injured by e-scooters; 30% were seen in more than one clinical setting (e.g., emergency department and a follow-up outpatient visit), 29% required advanced imaging, 6% required inpatient admission, and 2 died. We estimate 115 injuries per million e-scooter trips were treated in our health system. Conclusions Our observed e-scooter injury rate is likely an underestimate, but is similar to that previously reported for motorcycles. However, the comparative severity of injuries is unknown. Our methodology may prove useful to study other clinical conditions not identifiable by existing diagnostic systems.

Background Accurately assessing the regional activity of diseases such as COVID-19 is important in guiding public health interventions. Leveraging electronic health records (EHRs) to monitor outpatient clinical encounters may lead to the identification of emerging outbreaks. Objective The aim of this study is to investigate whether excess visits where the word “cough” was present in the EHR reason for visit, and hospitalizations with acute respiratory failure were more frequent from December 2019 to February 2020 compared with the preceding 5 years. Methods A retrospective observational cohort was identified from a large US health system with 3 hospitals, over 180 clinics, and 2.5 million patient encounters annually. Data from patient encounters from July 1, 2014, to February 29, 2020, were included. Seasonal autoregressive integrated moving average (SARIMA) time-series models were used to evaluate if the observed winter 2019/2020 rates were higher than the forecast 95% prediction intervals. The estimated excess number of visits and hospitalizations in winter 2019/2020 were calculated compared to previous seasons. Results The percentage of patients presenting with an EHR reason for visit containing the word “cough” to clinics exceeded the 95% prediction interval the week of December 22, 2019, and was consistently above the 95% prediction interval all 10 weeks through the end of February 2020. Similar trends were noted for emergency department visits and hospitalizations starting December 22, 2019, where observed data exceeded the 95% prediction interval in 6 and 7 of the 10 weeks, respectively. The estimated excess over the 3-month 2019/2020 winter season, obtained by either subtracting the maximum or subtracting the average of the five previous seasons from the current season, was 1.6 or 2.0 excess visits for cough per 1000 outpatient visits, 11.0 or 19.2 excess visits for cough per 1000 emergency department visits, and 21.4 or 39.1 excess visits per 1000 hospitalizations with acute respiratory failure, respectively. The total numbers of excess cases above the 95% predicted forecast interval were 168 cases in the outpatient clinics, 56 cases for the emergency department, and 18 hospitalized with acute respiratory failure. Conclusions A significantly higher number of patients with respiratory complaints and diseases starting in late December 2019 and continuing through February 2020 suggests community spread of SARS-CoV-2 prior to established clinical awareness and testing capabilities. This provides a case example of how health system analytics combined with EHR data can provide powerful and agile tools for identifying when future trends in patient populations are outside of the expected ranges.

BACKGROUND Accurately assessing the regional activity of diseases such as COVID-19 is important in guiding public health interventions. Leveraging electronic health records (EHRs) to monitor outpatient clinical encounters may lead to the identification of emerging outbreaks. OBJECTIVE The aim of this study is to investigate whether excess visits where the word “cough” was present in the EHR reason for visit, and hospitalizations with acute respiratory failure were more frequent from December 2019 to February 2020 compared with the preceding 5 years. METHODS A retrospective observational cohort was identified from a large US health system with 3 hospitals, over 180 clinics, and 2.5 million patient encounters annually. Data from patient encounters from July 1, 2014, to February 29, 2020, were included. Seasonal autoregressive integrated moving average (SARIMA) time-series models were used to evaluate if the observed winter 2019/2020 rates were higher than the forecast 95% prediction intervals. The estimated excess number of visits and hospitalizations in winter 2019/2020 were calculated compared to previous seasons. RESULTS The percentage of patients presenting with an EHR reason for visit containing the word “cough” to clinics exceeded the 95% prediction interval the week of December 22, 2019, and was consistently above the 95% prediction interval all 10 weeks through the end of February 2020. Similar trends were noted for emergency department visits and hospitalizations starting December 22, 2019, where observed data exceeded the 95% prediction interval in 6 and 7 of the 10 weeks, respectively. The estimated excess over the 3-month 2019/2020 winter season, obtained by either subtracting the maximum or subtracting the average of the five previous seasons from the current season, was 1.6 or 2.0 excess visits for cough per 1000 outpatient visits, 11.0 or 19.2 excess visits for cough per 1000 emergency department visits, and 21.4 or 39.1 excess visits per 1000 hospitalizations with acute respiratory failure, respectively. The total numbers of excess cases above the 95% predicted forecast interval were 168 cases in the outpatient clinics, 56 cases for the emergency department, and 18 hospitalized with acute respiratory failure. CONCLUSIONS A significantly higher number of patients with respiratory complaints and diseases starting in late December 2019 and continuing through February 2020 suggests community spread of SARS-CoV-2 prior to established clinical awareness and testing capabilities. This provides a case example of how health system analytics combined with EHR data can provide powerful and agile tools for identifying when future trends in patient populations are outside of the expected ranges.

Study objective Emergency medical services (EMS) may serve as a key source of real-time data about the evolving health of coronavirus disease 2019 (COVID-19)–affected populations, especially in low- and middle-income countries with less rapid and reliable vital statistics registration systems. Although official COVID-19 statistics in Mexico report almost exclusively inhospital mortality events, excess out-of-hospital mortality has been identified in other countries, including 1 EMS study in Italy that showed a 58% increase. Additionally, EMS and hospital reports from several countries have suggested that silent hypoxemia—low Sp o 2 in the absence of dyspnea—is associated with COVID-19. It is unclear, however, how these phenomena can be generalized to low- and middle-income countries. We assess how EMS data can be used in a sentinel capacity in Tijuana, a city on the Mexico–United States border with earlier exposure to COVID-19 than many low- and middle-income country settings. Methods In this observational study, we calculated numbers of weekly out-of-hospital deaths and respiratory cases handled by EMS in Tijuana, and estimated the difference between peak epidemic rates and expected trends based on data from 2014 to 2019. Results were compared with official COVID-19 statistics, stratified by neighborhood socioeconomic status, and examined for changing demographic or clinical features, including mean Sp o 2. Results An estimated 194.7 excess out-of-hospital deaths (95% confidence interval 135.5 to 253.9 deaths) occurred during the peak window (April 14 to May 11), representing an increase of 145% (95% CI 70% to 338%) compared with expected levels. During the same window, only 5 COVID-19–related out-of-hospital deaths were reported in official statistics. This corresponded with an increase in respiratory cases of 236.5% (95% CI 100.7% to 940.0%) and a decrease in mean Sp o 2 to 77.7% from 90.2% at baseline. The highest out-of-hospital death rates were observed in low-socioeconomic-status areas, although respiratory cases were more concentrated in high-socioeconomic-status areas. Conclusion EMS systems may play an important sentinel role in monitoring excess out-of-hospital mortality and other trends during the COVID-19 crisis in low- and middle-income countries. Using EMS data, we observed increases in out-of-hospital deaths in Tijuana that were nearly 3-fold greater than increases reported in EMS data in Italy. Increased testing in out-of-hospital settings may be required to determine whether excess mortality is being driven by COVID-19 infection, health system saturation, or patient avoidance of health care. We also found evidence of worsening rates of hypoxemia among respiratory patients treated by EMS, suggesting a possible increase in silent hypoxemia, which should be met with increased detection and clinical management efforts. Finally, we observed social disparities in out-of-hospital death that warrant monitoring and amelioration.

Study objective We conducted this study to better understand how emergency physicians estimate risk and make admission decisions for patients with low-risk chest pain. Methods We created a Web-based survey consisting of 5 chest pain scenarios that included history, physical examination, ECG findings, and basic laboratory studies, including a negative initial troponin-level result. We administered the scenarios in random order to emergency medicine residents and faculty at 11 US emergency medicine residency programs. We randomized respondents to receive questions about 1 of 2 endpoints, acute coronary syndrome or serious complication (death, dysrhythmia, or congestive heart failure within 30 days). For each scenario, the respondent provided a quantitative estimate of the probability of the endpoint, a qualitative estimate of the risk of the endpoint (very low, low, moderate, high, or very high), and an admission decision. Respondents also provided demographic information and completed a 3-item Fear of Malpractice scale. Results Two hundred eight (65%) of 320 eligible physicians completed the survey, 73% of whom were residents. Ninety-five percent of respondents were wholly consistent (no admitted patient was assigned a lower probability than a discharged patient). For individual scenarios, probability estimates covered at least 4 orders of magnitude; admission rates for scenarios varied from 16% to 99%. The majority of respondents (>72%) had admission thresholds at or below a 1% probability of acute coronary syndrome. Respondents did not fully differentiate the probability of acute coronary syndrome and serious outcome; for each scenario, estimates for the two were quite similar despite a serious outcome being far less likely. Raters used the terms "very low risk" and "low risk" only when their probability estimates were less than 1%. Conclusion The majority of respondents considered any probability greater than 1% for acute coronary syndrome or serious outcome to be at least moderate risk and warranting admission. Physicians used qualitative terms in ways fundamentally different from how they are used in ordinary conversation, which may lead to miscommunication during shared decisionmaking processes. These data suggest that probability or utility models are inadequate to describe physician decisionmaking for patients with chest pain.

Importance Provisional records from the US Centers for Disease Control and Prevention (CDC) through July 2020 indicate that overdose deaths spiked during the early months of the COVID-19 pandemic, yet more recent trends are not available, and the data are not disaggregated by month of occurrence, race/ethnicity, or other social categories. In contrast, data from emergency medical services (EMS) provide a source of information nearly in real time that may be useful for rapid and more granular surveillance of overdose mortality. Objective To describe racial/ethnic, social, and geographic trends in EMS-observed overdose-associated cardiac arrests during the COVID-19 pandemic through December 2020 and assess the concordance with CDC-reported provisional total overdose mortality through May 2020. Design, Setting, and Participants This cohort study included more than 11 000 EMS agencies in 49 US states that participate in the National EMS Information System and 83.7 million EMS activations in which patient contact was made. Exposures Year and month of occurrence of overdose-associated cardiac arrest; patient race/ethnicity; census region and division; county-level urbanicity; and zip code–level racial/ethnic composition, poverty, and educational attainment. Main Outcomes and Measures Overdose-associated cardiac arrests per 100 000 EMS activations with patient contact in 2020 were compared with a baseline of values from 2018 and 2019. Aggregate numbers of overdose-associated cardiac arrests and percentage increases were compared with provisional total mortality in CDC records from rolling 12-month windows with end months spanning January 2018 through July 2020. Results Among 33.4 million EMS activations in 2020, 16.8 million (50.2%) involved female patients and 16.3 million (48.8%) involved non-Hispanic White individuals. Overdose-associated cardiac arrests were elevated by 42.1% nationally in 2020 (42.3 per 100 000 EMS activations at baseline vs 60.1 per 100 000 EMS activations in 2020). The highest percentage increases were seen among Latinx individuals (49.7%; 38.8 per 100 000 activations at baseline vs 58.1 per 100 000 activations in 2020) and Black or African American individuals (50.3%; 21.5 per 100 000 activations at baseline vs 32.3 per 100 000 activations in 2020), people living in more impoverished neighborhoods (46.4%; 42.0 per 100 000 activations at baseline vs 61.5 per 100 000 activations in 2020), and the Pacific states (63.8%; 33.1 per 100 000 activations at baseline vs 54.2 per 100 000 activations in 2020), despite lower rates at baseline for these groups. The EMS records were available 6 to 12 months ahead of CDC mortality figures and showed a high concordance (r = 0.98) for months in which both data sets were available. If the historical association between EMS-observed and total overdose mortality holds true, an expected total of approximately 90 632 (95% CI, 85 737-95 525) overdose deaths may eventually be reported by the CDC for 2020. Conclusions and Relevance In this cohort study, records from EMS agencies provided an effective manner to rapidly surveil shifts in US overdose mortality. Unprecedented overdose deaths during the pandemic necessitate investments in overdose prevention as an essential aspect of the COVID-19 response and postpandemic recovery. This is particularly urgent for more socioeconomically disadvantaged and racial/ethnic minority communities subjected to the compounded burden of disproportionate COVID-19 mortality and rising overdose deaths.

Emergency department (ED) crowding threatens patient safety and is associated with increased mortality. This study explored the role of nonurgent referrals to the ED in crowding and collaborated on a large quality initiative with the study institution's accountable care organization (ACO) to provide timely alternatives to such referrals. Fifty-two percent of nonemergent ED patients report contacting a medical provider prior to coming to the ED, with 70% of those providers directing the patient to go to the ED. Fifty-nine percent of patients indicated that they would have accepted a clinic appointment in lieu of going to the ED. The authors collaborated on a multidisciplinary ED alternatives quality improvement effort with leadership to address these nonemergent referrals. ED visits per 1000 ACO patients declined significantly following survey results and ACO implementation of increased alternative ambulatory resources.

Introduction Patients who doctor shop for opioids are a vulnerable population that present a difficult dilemma for their health care providers regarding best methods of immediate treatment and how to manage their risk of harm from opioids. We aim to describe and compare opioid prescription patterns among high quantity prescription patients who doctor shopped, high quantity prescription patients who did not (doctor shopping eligible patients), and the remaining patients who received opioid prescriptions to guide population health policies for high risk opioid use patients. Methods We performed a cross-sectional descriptive analysis of opioid prescriptions during an 8-year period using California's de-identified Controlled Substance Utilization Review and Evaluation System (CURES) database from years 2008-2015. We identified the prevalence of patients who doctor shopped and depicted their opioid prescription patterns including prescriber characteristics, in comparison to the aforementioned groups. Doctor shopping was defined by patients who received greater than 6 or more prescriptions from at least 6 different prescribers within 6 months of time. Results Among the 3 million individuals who received an opioid prescription during the 8-year period, 1.3% met the doctor shopper definition. These patients received high levels of chronic opioids with 82% and 33% averaging greater than 20 and 100 morphine milligram equivalents (MME) daily, respectively, in comparison to 72% and 18% in the doctor shopping eligible group. Patients who doctor shopped received a significant proportion of their MME from 1 main prescriber (54%) and only received 2-5% of their total MME from episodic care providers, despite 88% receiving a prescription from these providers. Conclusions Patients who doctor shop are at high risk of opioid use disorder but represent a small fraction of those with dangerous opioid use. Furthermore, these individuals do not receive substantial opioids from episodic providers, which challenges the utility of prescription reduction programs in curbing use among this population. These results suggest we re-evaluate physician roles in the care of these patients and focus on referral to treatment and harm reduction strategies.

Importance Most drug epidemics in the United States have disproportionately affected nonwhite communities. Notably, the current opioid epidemic is heavily concentrated among low-income white communities, and the roots of this racial/ethnic phenomenon have not been adequately explained. Objective To examine the degree to which differential exposure to opioids via the health care system by race/ethnicity and income could be driving the observed social gradient of the current opioid epidemic, as well as to compare the trends in the prevalence of prescription opioids with those observed for stimulants and benzodiazepines. Design, Setting, and Participants This population-based study used 2011 through 2015 records from California’s prescription drug monitoring program (Controlled Substance Utilization Review and Evaluation System), which longitudinally tracks all patients receiving controlled substance prescriptions in the state and contained unique records for 29.7 million individuals who received such a prescription from 2011 to 2015. Data were analyzed between January and May 2018. Exposures A total of 1760 zip code tabulation areas (ZCTAs) in California, with associated racial/ethnic composition and per capita income. Main Outcomes and Measures The percentage of individuals receiving at least 1 prescription each year was calculated for opioids, benzodiazepines, and stimulants. Results A nearly 300% difference in opioid prescription prevalence across the race/ethnicity–income gradient was observed in California, with 44.2% of adults in the quintile of ZCTAs with the lowest-income/highest proportion–white population receiving at least 1 opioid prescription each year compared with 16.1% in the quintile with the highest-income/lowest proportion–white population and 23.6% of all individuals 15 years or older. Stimulant prescriptions were highly concentrated in mostly white high-income areas, with a prevalence of 3.8% among individuals in the quintile with the highest-income/highest proportion–white population and a prevalence of 0.6% in the quintile with the lowest-income/lowest proportion–white population. Benzodiazepine prescriptions did not have an income gradient but were concentrated in mostly white areas, with 15.7% of adults in the quintile of ZCTAs with the highest proportion–white population receiving at least 1 prescription each year compared with 7.0% among the quintile with the lowest proportion–white population. Conclusions and Relevance The race/ethnicity and income pattern of opioid overdoses mirrored prescription rates, suggesting that differential exposure to opioids via the health care system may have induced the large, observed racial/ethnic gradient in the opioid epidemic. Across drug categories, controlled medications were much more likely to be prescribed to individuals living in majority-white areas. These discrepancies may have shielded nonwhite communities from the brunt of the prescription opioid epidemic but also represent disparities in treatment and access to all medications.

Importance Since September 2017, standing electric scooters have proliferated rapidly as an inexpensive, easy mode of transportation. Although there are regulations for safe riding established by both electric scooter companies and local governments, public common use practices and the incidence and types of injuries associated with these standing electric scooters are unknown. Objective To characterize injuries associated with standing electric scooter use, the clinical outcomes of injured patients, and common use practices in the first US metropolitan area to experience adoption of this technology. Design, Setting, and Participants This study of a case series used retrospective cohort medical record review of all patients presenting with injuries associated with standing electric scooter use between September 1, 2017, and August 31, 2018, at 2 urban emergency departments associated with an academic medical center in Southern California. All electric scooter riders at selected public intersections in the community surrounding the 2 hospitals were also observed during a 7-hour observation period in September 2018. Main Outcomes and Measures Incidence and characteristics of injuries and observation of riders’ common use practices. Results Two hundred forty-nine patients (145 [58.2%] male; mean [SD] age, 33.7 [15.3] years) presented to the emergency department with injuries associated with standing electric scooter use during the study period. Two hundred twenty-eight (91.6%) were injured as riders and 21 (8.4%) as nonriders. Twenty-seven patients were younger than 18 years (10.8%). Ten riders (4.4%) were documented as having worn a helmet, and 12 patients (4.8%) had either a blood alcohol level greater than 0.05% or were perceived to be intoxicated by a physician. Frequent injuries included fractures (79 [31.7%]), head injury (100 [40.2%]), and contusions, sprains, and lacerations without fracture or head injury (69 [27.7%]). The majority of patients (234 [94.0%]) were discharged home from the emergency department; of the 15 admitted patients, 2 had severe injuries and were admitted to the intensive care unit. Among 193 observed electric scooter riders in the local community in September 2018, 182 (94.3%) were not wearing a helmet. Conclusions and Relevance Injuries associated with standing electric scooter use are a new phenomenon and vary in severity. In this study, helmet use was low and a significant subset of injuries occurred in patients younger than 18 years, the minimum age permitted by private scooter company regulations. These findings may inform public policy regarding standing electric scooter use.

This cohort study characterizes emergent trends in overdose-related cardiac arrests in the US during the coronavirus disease 2019 (COVID-19) pandemic using a large, national emergency medical services database.

Importance The Institute of Medicine described diagnostic error as the next frontier in patient safety and highlighted a critical need for better measurement tools. Objectives To estimate the proportions of emergency department (ED) visits attributable to symptoms of imminent ruptured abdominal aortic aneurysm (AAA), acute myocardial infarction (AMI), stroke, aortic dissection, and subarachnoid hemorrhage (SAH) that end in discharge without diagnosis; to evaluate longitudinal trends; and to identify patient characteristics independently associated with missed diagnostic opportunities. Design, Setting, and Participants This was a retrospective cohort study of all Medicare claims for 2006 to 2014. The setting was hospital EDs in the United States. Participants included all fee-for-service Medicare patients admitted to the hospital during 2007 to 2014 for the conditions of interest. Hospice enrollees and patients with recent skilled nursing facility stays were excluded. Main Outcomes and Measures The proportion of potential diagnostic opportunities missed in the ED was estimated using the difference between observed and expected ED discharges within 45 days of the index hospital admissions as the numerator, basing expected discharges on ED use by the same patients in earlier months. The denominator was estimated as the number of recognized emergencies (index hospital admissions) plus unrecognized emergencies (excess discharges). Results There were 1 561 940 patients, including 17 963 hospitalized for ruptured AAA, 304 980 for AMI, 1 181 648 for stroke, 19 675 for aortic dissection, and 37 674 for SAH. The mean (SD) age was 77.9 (10.3) years; 8.9% were younger than 65 years, and 54.1% were female. The proportions of diagnostic opportunities missed in the ED were as follows: ruptured AAA (3.4%; 95% CI, 2.9%-4.0%), AMI (2.3%; 95% CI, 2.1%-2.4%), stroke (4.1%; 95% CI, 4.0%-4.2%), aortic dissection (4.5%; 95% CI, 3.9%-5.1%), and SAH (3.5%; 95% CI, 3.1%-3.9%). Longitudinal trends were either nonsignificant (AMI and aortic dissection) or increasing (ruptured AAA, stroke, and SAH). Patient characteristics associated with unrecognized emergencies included age younger than 65 years, dual eligibility for Medicare and Medicaid coverage, female sex, and each of the following chronic conditions: end-stage renal disease, dementia, depression, diabetes, cerebrovascular disease, hypertension, coronary artery disease, and chronic obstructive pulmonary disease. Conclusions and Relevance Among Medicare patients, opportunities to diagnose ruptured AAA, AMI, stroke, aortic dissection, and SAH are missed in less than 1 in 20 ED presentations. Further improvement may prove difficult.

Supplemental Digital Content is available in the text., Purpose The authors hypothesize patient facial affect may influence clinician pretest probability (PTP) estimate of cardiopulmonary emergency (CPE) and desire to order a computerized tomographic pulmonary angiogram (CTPA). Method This prospective study was conducted at three Indiana University–affiliated hospitals in two parts: collecting videos of patients undergoing CTPA for suspected acute pulmonary embolism watching a humorous video (August 2014–April 2015) and presenting the medical histories and videos to clinicians to determine the impact of patient facial affect on physicians’ PTP estimate of CPE and desire to order a CTPA (June–November 2015). Patient outcomes were adjudicated as CPE+ or CPE− by three independent reviewers. Physicians completed a standardized test of facial affect recognition, read standardized medical histories, then viewed videos of the patients’ faces. Clinicians marked their PTP estimate of CPE and desire for a CTPA before and after seeing the video on a visual analog scale (VAS). Results Fifty physicians completed all 73 videos. Seeing the patient’s face produced a > 10% absolute change in PTP estimate of CPE in 1,204/3,650 (33%) cases and desire for a CTPA in 1,095/3,650 (30%) cases. The mean area under the receiver operating characteristic curve for CPE estimate was 0.55 ± 0.15, and the change in CPE VAS was negatively correlated with physicians’ standardized test scores (r = −0.23). Conclusions Clinicians may use patients’ faces to make clinically important inferences about presence of serious illness and need for diagnostic testing. However, these inferences may fail to align with actual patient outcomes.

Many clinical research studies involve paired outcome data, which consist of 2 measurements of the same variable in the same patient at 2 times. This article examines ways that researchers can graphically report data from such studies, meeting the dual goals of showing the experience of each patient while comparing differences between treatment groups. I emphasize the importance of hybrid parallel line plots, examine several useful variations of them, and provide Stata code to make them.

The use of computed tomography (CT) scanning in blunt head trauma has increased dramatically in recent years without an accompanying rise in the prevalence of injury or hospital admission for serious, traumatic conditions. Because CT is neither harmless nor inexpensive, researchers have attempted to optimize utilization, largely through research that elucidates which historical elements, signs, and symptoms predict intracranial injury, and use this information to develop clinical decision instruments (CDIs). While such techniques may be useful when the benefits and harms of each strategy (neuroimaging vs. observation) are known, quantifiable, and amenable to comparison, the exact magnitude of these benefits and harms remains unknown in this clinical scenario. We believe that most CDI development efforts are misguided insofar as they ignore critical, non-clinical factors influencing the decision to image. In this paper, we propose a conceptual model to illustrate how clinical- and nonclinical-factors influence emergency physicians who are making this decision. We posit that elements unrelated to standard clinical factors, such as personality of the physician, fear of litigation and of missed diagnoses, and compensation method may have equal or greater impact on actual decision-making than traditional clinical factors. Further, we suggest that the paucity of research on nonclinical factors is not surprising as such research requires expertise not typically possessed by emergency medicine clinician-researchers. Acknowledgement and study of these factors will be essential if we are to understand how EPs actually make these decisions and how test-ordering behavior can be modified.

Importance Following the adoption of electronic health records into a regulatory environment designed for paper records, there has been little investigation into the accuracy of physician documentation. Objective To quantify the percentage of emergency physician documentation of the review of systems (ROS) and physical examination (PE) that observers can confirm. Design, Setting, and Participants This case series took place at emergency departments in 2 academic medical centers between 2016 and 2018. Participants’ patient encounters were observed to compare real-time performance with clinical documentation. Exposures Resident physicians were shadowed by trained observers for 20 encounters (10 encounters per physician per site) to obtain real-time observational data; associated electronic health record data were subsequently reviewed. Main Outcomes and Measures Number of confirmed ROS systems (range, 0-14) divided by the number of documented ROS systems (range, 0-14), and number of confirmed PE systems (range, 0-14) divided by the number of documented PE systems (range, 0-14). Results The final study cohort included 9 licensed emergency medicine residents who evaluated a total of 180 patients (mean [SD] age, 48.7 [20.0] years; 91 [50.5%] women). For ROS, physicians documented a median (interquartile range [IQR]) of 14 (8-14) systems, while audio recordings confirmed a median (IQR) of 5 (3-6) systems. Overall, 755 of 1961 documented ROS systems (38.5%) were confirmed by audio recording data. For PE, resident physicians documented a median (IQR) of 8 (7-9) verifiable systems, while observers confirmed a median (IQR) of 5.5 (3-6) systems. Overall, 760 of 1429 verifiable documented PE systems (53.2%) were confirmed by concurrent observation. Interrater reliability for rating of ROS and PE was more than 90% for all measures. Conclusions and Relevance In this study of 9 licensed year emergency medicine residents, there were inconsistencies between the documentation of ROS and PE findings in the electronic health record and observational reports. These findings raise the possibility that some documentation may not accurately represent physician actions. Further studies should be undertaken to determine whether this occurrence is widespread. However, because such studies are unlikely to be performed owing to institution-level barriers that exist nationwide, payers should consider removing financial incentives to generate lengthy documentation.

Study objective We determine how often studies that evaluate the performance of an aid for decisionmaking, be it a simple laboratory or imaging test or a complex multielement decision instrument, compare the aid's performance to independent, unaided physician judgment. Methods This was a cross-sectional survey of all Original Research and Brief Research Report articles in Annals of Emergency Medicine from 1998 to 2015. We included all articles that evaluated the performance of an aid for decisionmaking in assisting a physician with a decision about testing, treatment, diagnosis, or disposition. Two authors independently characterized the intent and purpose of each aid for decisionmaking, determined whether each study had a comparison to unaided physician judgment within the article or in a separate article, and recorded the result of that comparison. Results One hundred seventy-one (8.3%) of 2,060 research articles studied the performance characteristics of an aid for decisionmaking, 48 of which were formal clinical decision instruments. Forty of the 171 studies retrospectively analyzed existing databases and therefore could not assess physician judgment. Investigators compared the aid for decisionmaking to physician judgment in 11% (15/131) of the prospective studies, including 15% (6/41) of studies that evaluated a formal clinical decision instrument. For 9 articles that had no comparison to physician judgment, we found 6 unique external publications that compared that aid to physician clinical judgment. The decision aid was superior to clinical judgment in 2 of the 21 studies that contained a comparison. Conclusion Physician judgment is infrequently assessed when the performance of an aid for decisionmaking is evaluated, and, when reported, the decision aid seldom outperformed physician judgment.