This paper is in the following e-collection/theme issue:

Data Science (357) Clinical Information and Decision Making (1914) Clinical Informatics (1325) mHealth for Health Administration (184) Organizational Issues (221) Electronic Health Records (1319) Advanced Data Analytics in eHealth (209) Quality Improvement (294)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/63765, first published .
Toward Real-Time Discharge Volume Predictions in Multisite Health Care Systems: Longitudinal Observational Study

Toward Real-Time Discharge Volume Predictions in Multisite Health Care Systems: Longitudinal Observational Study

Toward Real-Time Discharge Volume Predictions in Multisite Health Care Systems: Longitudinal Observational Study

Authors of this article:

Fernando Acosta-Perez1, 2 Author Orcid Image ;   Justin Boutilier1 Author Orcid Image ;   Gabriel Zayas-Caban1 Author Orcid Image ;   Sabrina Adelaine2 Author Orcid Image ;   Frank Liao2, 3 Author Orcid Image ;   Brian Patterson1, 2, 3 Author Orcid Image
Article Authors Cited by Tweetations Metrics

Fernando Acosta-Perez   1, 2 , MS ;   Justin Boutilier   1 , PhD ;   Gabriel Zayas-Caban   1 , PhD ;   Sabrina Adelaine   2 , PhD ;   Frank Liao   2, 3 , PhD ;   Brian Patterson   1, 2, 3 , MPH, MD

1 Department of Industrial and Systems Engineering, University of Wisconsin–Madison, Madison, WI, United States

2 UW Health: University of Wisconsin Hospitals and Clinics, Madison, WI, United States

3 Department of Emergency Medicine, University of Wisconsin–Madison, Madison, WI, United States

Corresponding Author:

  • Fernando Acosta-Perez, MS
  • Department of Industrial and Systems Engineering
  • University of Wisconsin–Madison
  • 1513 University Avenue
  • Madison, WI 53706
  • United States
  • Phone: 1 (608) 262-2686
  • Email: facostaperez@wisc.edu