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Accurate Emergency Department Wait Time Prediction

Author

Listed:
  • 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)

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.

Suggested Citation

  • Erjie Ang & Sara Kwasnick & Mohsen Bayati & Erica L. Plambeck & Michael Aratow, 2016. "Accurate Emergency Department Wait Time Prediction," Manufacturing & Service Operations Management, INFORMS, vol. 18(1), pages 141-156, February.
  • Handle: RePEc:inm:ormsom:v:18:y:2016:i:1:p:141-156
    DOI: 10.1287/msom.2015.0560
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