Objective There is minimal evidence describing outcomes for emergency department (ED) patients with suspected coronavirus disease 2019 (COVID‐19) infection who are not hospitalized. The study objective was to assess 30‐day outcomes (ED revisit, admission, ICU admission, and death) for low‐risk patients discharged after ED evaluation for COVID‐19. Methods This was a retrospective cohort study of patients triaged to a COVID‐19 surge area within an urban ED and discharged between March 12 and April 6. Physicians were encouraged to discharge patients if they were well‐appearing with few comorbidities. Data were collected from review of medical records and phone follow‐up, and the analysis was descriptive. Results Of 452 patients, the median age was 38, and 61.7% had no comorbidities. Chest radiographs were performed for 50.4% of patients and showed infiltrates in 14% of those tested. Polymerase chain reaction testing was performed for 28.3% of patients during the index ED visit and was positive in 35.9% of those tested. Follow‐up was achieved for 75.4% of patients. ED revisits occurred for 13.7% of patients. The inpatient admission rate at 30 days was 4.6%, with 0.7% requiring intensive care. Median number of days between index ED evaluation and return for admission was 5 (interquartile range 3–7, range 1–17). There were no known deaths. Conclusions A minority of low‐risk patients with suspected COVID‐19 will require hospitalization after being discharged home from the ED. Outpatient management is likely safe for well‐appearing patients with normal vital signs, but patients should be instructed to return for worsening symptoms including labored breathing. Future work is warranted to develop and validate ED disposition guidelines.

Background Many medical conditions, perhaps 80% of them, can be diagnosed by taking a thorough history of present illness (HPI). However, in the clinical setting, situational factors such as interruptions and time pressure may cause interactions with patients to be brief and fragmented. One solution for improving clinicians’ ability to collect a thorough HPI and maximize efficiency and quality of care could be to use a digital tool to obtain the HPI before face-to-face evaluation by a clinician. Objective Our objective was to identify and characterize digital tools that have been designed to obtain the HPI directly from patients or caregivers and present this information to clinicians before a face-to-face encounter. We also sought to describe outcomes reported in testing of these tools, especially those related to usability, efficiency, and quality of care. Methods We conducted a scoping review using predefined search terms in the following databases: MEDLINE, CINAHL, PsycINFO, Web of Science, Embase, IEEE Xplore Digital Library, ACM Digital Library, and ProQuest Dissertations & Theses Global. Two reviewers screened titles and abstracts for relevance, performed full-text reviews of articles meeting the inclusion criteria, and used a pile-sorting procedure to identify distinguishing characteristics of the tools. Information describing the tools was primarily obtained from identified peer-reviewed sources; in addition, supplementary information was obtained from tool websites and through direct communications with tool creators. Results We identified 18 tools meeting the inclusion criteria. Of these 18 tools, 14 (78%) used primarily closed-ended and multiple-choice questions, 1 (6%) used free-text input, and 3 (17%) used conversational (chatbot) style. More than half (10/18, 56%) of the tools were tailored to specific patient subpopulations; the remaining (8/18, 44%) tools did not specify a target subpopulation. Of the 18 tools, 7 (39%) included multilingual support, and 12 (67%) had the capability to transfer data directly into the electronic health record. Studies of the tools reported on various outcome measures related to usability, efficiency, and quality of care. Conclusions The HPI tools we identified (N=18) varied greatly in their purpose and functionality. There was no consensus on how patient-generated information should be collected or presented to clinicians. Existing tools have undergone inconsistent levels of testing, with a wide variety of different outcome measures used in evaluation, including some related to usability, efficiency, and quality of care. There is substantial interest in using digital tools to obtain the HPI from patients, but the outcomes measured have been inconsistent. Future research should focus on whether using HPI tools can lead to improved patient experience and health outcomes, although surrogate end points could instead be used so long as patient safety is monitored.

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Objectives Obtaining body temperature is a quick and easy method to screen for acute infection such as COVID-19. Currently, the predictive value of body temperature for acute infection is inhibited by failure to account for other readily available variables that affect temperature values. In this proof-of-concept study, we sought to improve COVID-19 pretest probability estimation by incorporating covariates known to be associated with body temperature, including patient age, sex, comorbidities, month, and time of day. Methods For patients discharged from an academic hospital emergency department after testing for COVID-19 in March and April of 2020, we abstracted clinical data. We reviewed physician documentation to retrospectively generate estimates of pretest probability for COVID-19. Using patients’ COVID-19 PCR test results as a gold standard, we compared AUCs of logistic regression models predicting COVID-19 positivity that used: (1) body temperature alone; (2) body temperature and pretest probability; (3) body temperature, pretest probability, and body temperature-relevant covariates. Calibration plots and bootstrap validation were used to assess predictive performance for model #3. Results Data from 117 patients were included. The models’ AUCs were: (1) 0.69 (2) 0.72, and (3) 0.76, respectively. The absolute difference in AUC was 0.029 (95% CI −0.057 to 0.114, p=0.25) between model 2 and 1 and 0.038 (95% CI −0.021 to 0.097, p=0.10) between model 3 and 2. Conclusions By incorporating covariates known to affect body temperature, we demonstrated improved pretest probability estimates of acute COVID-19 infection. Future work should be undertaken to further develop and validate our model in a larger, multi-institutional sample.

Background: Incident reports submitted during times of organizational stress may reveal unique insights. Purpose: To understand the insights conveyed in hospital incident reports about how work system factors affected medication safety during a coronavirus disease-2019 (COVID-19) surge. Methods: We randomly selected 100 medication safety incident reports from an academic medical center (December 2020 to January 2021), identified near misses and errors, and classified contributing work system factors using the Human Factors Analysis and Classification System-Healthcare. Results: Among 35 near misses/errors, incident reports described contributing factors (mean 1.3/report) involving skill-based errors (n = 20), communication (n = 8), and tools/technology (n = 4). Reporters linked 7 events to COVID-19. Conclusions: Skill-based errors were the most common contributing factors for medication safety events during a COVID-19 surge. Reporters rarely deemed events to be related to COVID-19, despite the tremendous strain of the surge on nurses. Future efforts to improve the utility of incident reports should emphasize the importance of describing work system factors.