Multisite Evaluation of Prediction Models for Emergency Department Crowding Before and During the COVID-19 Pandemic.

ABSTRACT:

Objective

To develop a machine learning framework to forecast emergency department (ED) crowding and to evaluate model performance under spatial and temporal data drift.

Materials and methods

We obtained four datasets, identified by the location: 1-large academic hospital and 2-rural hospital, and time period: pre-COVID (Jan 1, 2019-Feb 1, 2020) and COVID-era (May 15, 2020-Feb 1, 2021). Our primary target was a binary outcome that is equal to 1 if the number of patients with acute respiratory illness that were ED boarding for more than four hours was above a prescribed historical percentile. We trained a random forest and used the area under the curve (AUC) to evaluate out-of-sample performance for two experiments: 1) we evaluated the impact of sudden temporal drift by train

SUBMITTER: Smith AJ 

PROVIDER: S-EPMC9620348 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature