Multisite evaluation of prediction models for emergency department crowding before and during the COVID-19 pandemic

Author:

Smith Ari J1ORCID,Patterson Brian W123ORCID,Pulia Michael S12,Mayer John4,Schwei Rebecca J2,Nagarajan Radha4,Liao Frank5ORCID,Shah Manish N2,Boutilier Justin J12ORCID

Affiliation:

1. Department of Industrial and Systems Engineering, University of Wisconsin–Madison , Madison, Wisconsin, USA

2. BerbeeWalsh Department of Emergency Medicine, University of Wisconsin–Madison , Madison, Wisconsin, USA

3. Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison , Madison, Wisconsin, USA

4. Marshfield Clinic Research Institute , Marshfield, Wisconsin, USA

5. Applied Data Science, Information Services, UW-Health , Madison, Wisconsin, USA

Abstract

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 4 datasets, identified by the location: 1—large academic hospital and 2—rural hospital, and time period: pre-coronavirus disease (COVID) (January 1, 2019–February 1, 2020) and COVID-era (May 15, 2020–February 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 4 h 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 2 experiments: (1) we evaluated the impact of sudden temporal drift by training models using pre-COVID data and testing them during the COVID-era, (2) we evaluated the impact of spatial drift by testing models trained at location 1 on data from location 2, and vice versa. Results The baseline AUC values for ED boarding ranged from 0.54 (pre-COVID at location 2) to 0.81 (COVID-era at location 1). Models trained with pre-COVID data performed similarly to COVID-era models (0.82 vs 0.78 at location 1). Models that were transferred from location 2 to location 1 performed worse than models trained at location 1 (0.51 vs 0.78). Discussion and Conclusion Our results demonstrate that ED boarding is a predictable metric for ED crowding, models were not significantly impacted by temporal data drift, and any attempts at implementation must consider spatial data drift.

Funder

UW School of Medicine and Public Health

the Wisconsin Partnership Program

Publisher

Oxford University Press (OUP)

Subject

Health Informatics

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