Hospital Length of Stay Prediction for Planned Admissions Using Observational Medical Outcomes Partnership Common Data Model: Retrospective Study.

Journal: Journal of medical Internet research
PMID: 39576284

Abstract

BACKGROUND: Accurate hospital length of stay (LoS) prediction enables efficient resource management. Conventional LoS prediction models with limited covariates and nonstandardized data have limited reproducibility when applied to the general population.

Authors

  • Haeun Lee
    Department of Biomedical Informatics and Data Science, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, MD, United States.
  • Seok Kim
    Office of eHealth Research and Business, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
  • Hui-Woun Moon
    Office of eHealth Research and Businesses, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea.
  • Ho-Young Lee
    Office of eHealth Research and Businesses, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea.
  • Kwangsoo Kim
    Transdisciplinary Department of Medicine & Advanced Technology, Seoul National University Hospital, Seoul, Republic of Korea.
  • Se Young Jung
    Office of eHealth Research and Business, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
  • Sooyoung Yoo
    Office of eHealth Research and Business, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.

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