Forecasting Emergency Department Wait Times

Erica Plambeck, Mohsen Bayati, Erjie Ang, Sara Kwasnick and Mike Aratow
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Erica Plambeck: Stanford University
Mohsen Bayati: Stanford University
Erjie Ang: ?
Sara Kwasnick: ?
Mike Aratow: ?

Research Papers from Stanford University, Graduate School of Business

Abstract: This paper proposes a Combined Method (combining fluid model estimators and statistical learning) to forecast the wait time for low-acuity patients in an Emergency Department, and describes the implementation of the Combined Method at the San Mateo Medical Center (SMMC). In historical data from four different hospitals, the Combined Method is more accurate than stand-alone fluid model estimators and statistical learning, and also more accurate than the rolling averages that hospitals currently use to forecast the ED wait time. In historical data and post-implementation data for SMMC, the Combined Method reduces the mean squared forecast error by a nearly third relative to the best rolling average, notably by correcting errors of underestimation in which a patient waits for longer than the forecast. The paper provides a general recipe by which any hospital with an Electronic Medical Records (EMR) can implement the Combined Method.

Date: 2014
New Economics Papers: this item is included in nep-for and nep-mfd
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Persistent link: https://EconPapers.repec.org/RePEc:ecl:stabus:3187

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