Real-time prediction of inpatient length of stay for discharge prioritization.

Journal: Journal of the American Medical Informatics Association : JAMIA
Published Date: Apr 1, 2016

Abstract

OBJECTIVE: Hospitals are challenged to provide timely patient care while maintaining high resource utilization. This has prompted hospital initiatives to increase patient flow and minimize nonvalue added care time. Real-time demand capacity management (RTDC) is one such initiative whereby clinicians convene each morning to predict patients able to leave the same day and prioritize their remaining tasks for early discharge. Our objective is to automate and improve these discharge predictions by applying supervised machine learning methods to readily available health information.

Authors

  • Sean Barnes
    Department of Decision, Operations & Information Technologies, Robert H. Smith School of Business, 4352 Van Munching Hall, University of Maryland, College Park, MD 20742, USA sbarnes@rhsmith.umd.edu.
  • Eric Hamrock
    Department of Operations Integration, Johns Hopkins Health System, Baltimore, MD, USA.
  • Matthew Toerper
    Department of Emergency Medicine, Johns Hopkins University, Baltimore, MD 21218, United States.
  • Sauleh Siddiqui
    Department of Civil Engineering, Johns Hopkins System Institute, Johns Hopkins University, Baltimore, Maryland.
  • Scott Levin
    Department of Emergency Medicine, Johns Hopkins University, Baltimore, MD 21218, United States.

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