Multisite implementation of a workflow-integrated machine learning system to optimize COVID-19 hospital admission decisions.

ABSTRACT: Demand has outstripped healthcare supply during the coronavirus disease 2019 (COVID-19) pandemic. Emergency departments (EDs) are tasked with distinguishing patients who require hospital resources from those who may be safely discharged to the community. The novelty and high variability of COVID-19 have made these determinations challenging. In this study, we developed, implemented and evaluated an electronic health record (EHR) embedded clinical decision support (CDS) system that leverages machine learning (ML) to estimate short-term risk for clinical deterioration in patients with or under investigation for COVID-19. The system translates model-generated risk for critical care needs within 24 h and inpatient care needs within 72 h into rapidly interpretable COVID-19 Deterioration Risk Le

SUBMITTER: Hinson JS 

PROVIDER: S-EPMC9287691 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature