Machine Learning Approach to Identifying Wrong-Site Surgeries Using Centers for Medicare and Medicaid Services Dataset: Development and Validation Study.

Journal: JMIR formative research
Published Date:

Abstract

BACKGROUND: Wrong-site surgery (WSS) is a critical but preventable medical error, often resulting in severe patient harm and substantial financial costs. While protocols exist to reduce wrong-site surgery, underreporting and inconsistent documentation continue to contribute to its persistence. Machine learning (ML) models, which have shown success in detecting medication errors, may offer a solution by identifying unusual procedure-diagnosis combinations. This study investigated whether an ML approach can effectively adapt to detect surgical errors.

Authors

  • Yuan-Hsin Chen
    Department of Surgery, Massachusetts General Hospital, Boston, MA, United States.
  • Ching-Hsuan Lin
    Center for the Evaluation of Value and Risk in Health, Tufts Medical Center, Boston, MA, United States.
  • Chiao-Hsin Fan
    AESOP Technology Inc, Taipei, Taiwan.
  • An Jim Long
    AESOP Technology Inc, Taipei, Taiwan.
  • Jeremiah Scholl
    AESOP Technology Inc, Taipei, Taiwan.
  • Yen-Pin Kao
    Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, New Taipei City, Taiwan.
  • Usman Iqbal
    Institute for Evidence-Based Healthcare, Faculty of Health Sciences & Medicine, Bond University, Gold Coast, Australia.
  • Yu-Chuan Jack Li
    Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei, Taiwan.