A Neurosurgical Readmissions Reduction Program in an Academic Hospital Leveraging Machine Learning, Workflow Analysis, and Simulation.

Journal: Applied clinical informatics
PMID: 38897230

Abstract

BACKGROUND:  Predicting 30-day hospital readmissions is crucial for improving patient outcomes, optimizing resource allocation, and achieving financial savings. Existing studies reporting the development of machine learning (ML) models predictive of neurosurgical readmissions do not report factors related to clinical implementation.

Authors

  • Tzu-Chun Wu
    Department of Biomedical Informatics, College of Medicine, University of Cincinnati, Cincinnati, Ohio, United States.
  • Abraham Kim
    Department of Biomedical Informatics, College of Medicine, University of Cincinnati, Cincinnati, Ohio, United States.
  • Ching-Tzu Tsai
    Department of Biomedical Informatics, College of Medicine, University of Cincinnati, Cincinnati, Ohio, United States.
  • Andy Gao
    Department of Biomedical Informatics, College of Medicine, University of Cincinnati, Cincinnati, Ohio, United States.
  • Taran Ghuman
    Department of Biomedical Informatics, College of Medicine, University of Cincinnati, Cincinnati, Ohio, United States.
  • Anne Paul
    UCHealth, Cincinnati, Ohio, United States.
  • Alexandra Castillo
    UCHealth, Cincinnati, Ohio, United States.
  • Joseph Cheng
    Dept. Advanced manufacturing system, Boardtek Electronics Corporation, Taiwan.
  • Owoicho Adogwa
    Department of Neurosurgery, University of Cincinnati College of Medicine, 231 Albert Sabin Way, Cincinnati, OH 45229, USA. Electronic address: adogwaoo@ucmail.uc.edu.
  • Laura B Ngwenya
    Neuroinformatics Laboratory, Department of Neurosurgery, College of Medicine, University of Cincinnati, Cincinnati, Ohio, United States.
  • Brandon Foreman
    University of Cincinnati, OH, USA.
  • Danny T Y Wu
    Department of Biomedical Informatics, College of Medicine, University of Cincinnati, Cincinnati, OH.

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