The Implementation of an Emergency Medicine Telehealth System During a Pandemic.

BACKGROUND: In March of 2020, the World Health Organization declared coronavirus disease 2019 (COVID-19)-a disease caused by a novel coronavirus-a pandemic, and it continued to spread rapidly in the community. Our institution implemented an emergency medicine telehealth system that sought to expedite care of stable patients, decrease provider exposure to COVID-19, decrease overall usage rate of personal protective equipment, and provide a platform so that infected or quarantined physicians could continue to work. This effort was among the first to use telehealth to practice emergency medicine in the setting of a pandemic in the United States. DISCUSSION: Outside the main emergency departments at each of 2 sites of our academic institution, disaster tents were erected with patient care equipment and medications, as well as technology to allow for telehealth visits. The triage system was modified to appropriately select low-risk patients with symptoms suggestive of COVID-19 who could be seen in these disaster tents. Despite some issues that needed to be addressed, such as provider discomfort, limited medication availability, and connectivity problems, the model was successful overall. CONCLUSIONS: Other emergency departments might find this proof of concept article useful. Telehealth will likely be used more broadly in the future, including emergency care. It is imperative that the health care system continues to adapt to respond appropriately to challenges such as pandemics.

Deployment of artificial intelligence for radiographic diagnosis of COVID-19 pneumonia in the emergency department.

OBJECTIVE: The coronavirus disease 2019 pandemic has inspired new innovations in diagnosing, treating, and dispositioning patients during high census conditions with constrained resources. Our objective is to describe first experiences of physician interaction with a novel artificial intelligence (AI) algorithm designed to enhance physician abilities to identify ground-glass opacities and consolidation on chest radiographs. METHODS: During the first wave of the pandemic, we deployed a previously developed and validated deep-learning AI algorithm for assisted interpretation of chest radiographs for use by physicians at an academic health system in Southern California. The algorithm overlays radiographs with "heat" maps that indicate pneumonia probability alongside standard chest radiographs at the point of care. Physicians were surveyed in real time regarding ease of use and impact on clinical decisionmaking. RESULTS: Of the 5125 total visits and 1960 chest radiographs obtained in the emergency department (ED) during the study period, 1855 were analyzed by the algorithm. Among these, emergency physicians were surveyed for their experiences on 202 radiographs. Overall, 86% either strongly agreed or somewhat agreed that the intervention was easy to use in their workflow. Of the respondents, 20% reported that the algorithm impacted clinical decisionmaking. CONCLUSIONS: To our knowledge, this is the first published literature evaluating the impact of medical imaging AI on clinical decisionmaking in the emergency department setting. Urgent deployment of a previously validated AI algorithm clinically was easy to use and was found to have an impact on clinical decision making during the predicted surge period of a global pandemic.

Leveraging Remote Research Associates During a Pandemic.

INTRODUCTION: The coronavirus disease 2019 (COVID-19) pandemic has seriously impacted clinical research operations in academic medical centers due to social distancing measures and stay-at-home orders. The purpose of this paper is to describe the implementation of a program to continue clinical research based out of an emergency department (ED) using remote research associates (RA). METHODS: Remote RAs were trained and granted remote access to the electronic health record (EHR) by the health system's core information technology team. Upon gaining access, remote RAs used a dual-authentication process to gain access to a host-based, firewall-protected virtual network where the EHR could be accessed to continue screening and enrollment for ongoing studies. Study training for screening and enrollment was also provided to ensure study continuity. RESULTS: With constant support and guidance available to establish this EHR access pathway, the remote RAs were able to gain access relatively independently and without major technical troubleshooting. Each remote RA was granted access and trained on studies within one week and self-reported a high degree of program satisfaction, EHR access ease, and study protocol comfort through informal evaluation surveys. CONCLUSIONS: In response to the COVID-19 pandemic, we virtualized a clinical research program to continue important ED-based studies.