Impact of COVID-19 on Emergency Medicine Residency Programs: A Cross-Sectional Study in New York State.

INTRODUCTION: The 2019 novel coronavirus pandemic has caused significant disruptions in the clinical operations of hospitals as well as clinical education, training, and research at academic centers. New York State was among the first and largest epicenters of the pandemic, resulting in significant disruptions across its 29 emergency medicine (EM) residency programs. We conducted a cross-sectional observational study of EM residency programs in New York State to assess the impact of the pandemic on resident education and training programs. METHODS: We surveyed a cross-sectional sample of residency programs throughout New York State in June 2020, in the timeframe immediately after the state's first "wave" of the pandemic. The survey was distributed to program leadership and elicited information on pandemic-prompted curricular modifications and other educational changes. The survey covered topics related to disruptions in medical education and sought details on solutions to educational issues encountered by programs. RESULTS: Of the 29 accredited EM residency programs in New York State, leadership from 22 (76%) responded. Of these participating programs, 11 (50%) experienced high pandemic impact on clinical services, 21 (95%) canceled their own trainees' off-service rotations, 22 (100%) canceled or postponed visiting medical student rotations, 22 (100%) adopted virtual conference formats (most within the first week of the pandemic wave), and 11 (50%) stopped all prospective research (excluding COVID-19 research), while most programs continued retrospective research. CONCLUSION: This study highlights the profound educational impact of the pandemic on residency programs in one of the hardest- and earliest-hit regions in the United States. Specifically, it highlights the ubiquity of virtual conferencing, the significant impact on research, and the concerns about canceled rotations and missed training opportunities for residents, as well as prehospital and non-physician practitioner trainees. This data should be used to prompt discussion regarding the necessity of alternate educational modalities for pandemic times and the sequelae of implementing these plans.

Detection of COVID-19 using multimodal data from a wearable device: results from the first TemPredict Study.

Early detection of diseases such as COVID-19 could be a critical tool in reducing disease transmission by helping individuals recognize when they should self-isolate, seek testing, and obtain early medical intervention. Consumer wearable devices that continuously measure physiological metrics hold promise as tools for early illness detection. We gathered daily questionnaire data and physiological data using a consumer wearable (Oura Ring) from 63,153 participants, of whom 704 self-reported possible COVID-19 disease. We selected 73 of these 704 participants with reliable confirmation of COVID-19 by PCR testing and high-quality physiological data for algorithm training to identify onset of COVID-19 using machine learning classification. The algorithm identified COVID-19 an average of 2.75 days before participants sought diagnostic testing with a sensitivity of 82% and specificity of 63%. The receiving operating characteristic (ROC) area under the curve (AUC) was 0.819 (95% CI [0.809, 0.830]). Including continuous temperature yielded an AUC 4.9% higher than without this feature. For further validation, we obtained SARS CoV-2 antibody in a subset of participants and identified 10 additional participants who self-reported COVID-19 disease with antibody confirmation. The algorithm had an overall ROC AUC of 0.819 (95% CI [0.809, 0.830]), with a sensitivity of 90% and specificity of 80% in these additional participants. Finally, we observed substantial variation in accuracy based on age and biological sex. Findings highlight the importance of including temperature assessment, using continuous physiological features for alignment, and including diverse populations in algorithm development to optimize accuracy in COVID-19 detection from wearables.