Accurate Emergency Department Wait Time Prediction

Erjie Ang (), Sara Kwasnick (), Mohsen Bayati (), Erica L. Plambeck () and Michael Aratow ()
Additional contact information
Erjie Ang: Graduate School of Business, Stanford University, Stanford, California 94305
Sara Kwasnick: Graduate School of Business, Stanford University, Stanford, California 94305
Mohsen Bayati: Graduate School of Business, Stanford University, Stanford, California 94305
Erica L. Plambeck: Graduate School of Business, Stanford University, Stanford, California 94305
Michael Aratow: San Mateo Medical Center, San Mateo, California 94403

Manufacturing & Service Operations Management, 2016, vol. 18, issue 1, 141-156

Abstract: This paper proposes the Q-Lasso method for wait time prediction, which combines statistical learning with fluid model estimators. In historical data from four remarkably different hospitals, Q-Lasso predicts the emergency department (ED) wait time for low-acuity patients with greater accuracy than rolling average methods (currently used by hospitals), fluid model estimators (from the service operations management literature), and quantile regression methods (from the emergency medicine literature). Q-Lasso achieves greater accuracy largely by correcting errors of underestimation in which a patient waits for longer than predicted. Implemented on the external website and in the triage room of the San Mateo Medical Center (SMMC), Q-Lasso achieves over 30% lower mean squared prediction error than would occur with the best rolling average method. The paper describes challenges and insights from the implementation at SMMC.

Keywords: OM practice; service operations; empirical research; healthcare management (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (18)

Downloads: (external link)
http://dx.doi.org/10.1287/msom.2015.0560 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormsom:v:18:y:2016:i:1:p:141-156

Access Statistics for this article

More articles in Manufacturing & Service Operations Management from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().