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A systematic literature review of predicting patient discharges using statistical methods and machine learning

Author

Listed:
  • Mahsa Pahlevani

    (Dalhousie University)

  • Majid Taghavi

    (Dalhousie University
    Saint Mary’s University)

  • Peter Vanberkel

    (Dalhousie University)

Abstract

Discharge planning is integral to patient flow as delays can lead to hospital-wide congestion. Because a structured discharge plan can reduce hospital length of stay while enhancing patient satisfaction, this topic has caught the interest of many healthcare professionals and researchers. Predicting discharge outcomes, such as destination and time, is crucial in discharge planning by helping healthcare providers anticipate patient needs and resource requirements. This article examines the literature on the prediction of various discharge outcomes. Our review discovered papers that explore the use of prediction models to forecast the time, volume, and destination of discharged patients. Of the 101 reviewed papers, 49.5% looked at the prediction with machine learning tools, and 50.5% focused on prediction with statistical methods. The fact that knowing discharge outcomes in advance affects operational, tactical, medical, and administrative aspects is a frequent theme in the papers studied. Furthermore, conducting system-wide optimization, predicting the time and destination of patients after discharge, and addressing the primary causes of discharge delay in the process are among the recommendations for further research in this field.

Suggested Citation

  • Mahsa Pahlevani & Majid Taghavi & Peter Vanberkel, 2024. "A systematic literature review of predicting patient discharges using statistical methods and machine learning," Health Care Management Science, Springer, vol. 27(3), pages 458-478, September.
    • Handle: RePEc:kap:hcarem:v:27:y:2024:i:3:d:10.1007_s10729-024-09682-7
    DOI: 10.1007/s10729-024-09682-7
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    References listed on IDEAS

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