Protocol for the impact of machine learning-based clinician decision support algorithims in perioperative care (IMAGINATIVE) in Singapore general hospital : a large prospective randomised controlled trial.

Journal: BMJ open
PMID: 39806608

Abstract

INTRODUCTION: As surgical accessibility improves, the incidence of postoperative complications is expected to rise. The implementation of a precise and objective risk stratification tool holds the potential to mitigate these complications by early identification of high-risk patients. Moreover, it could address the escalating costs from resource misallocation. In Singapore General Hospital (SGH), we introduced the Combined Assessment of Risk Encountered in Surgery-Machine Learning (CARES-ML) in June 2023, focusing on predicting 30-day postoperative mortality and the need for post-surgery intensive care unit (ICU) stays. The IMAGINATIVE Trial aims to evaluate the efficacy of such systems in a large academic medical centre.

Authors

  • Hairil Rizal Abdullah
    Department of Anesthesiology, Singapore General Hospital, Singapore, Singapore.
  • Tan Pei Yi Brenda
    Department of Anesthesiology, Singapore General Hospital, Singapore.
  • Celestine Loh
    Duke-NUS Medical School, Singapore.
  • Marcus Ong
    Department of Emergency Medicine, Singapore General Hospital, Singapore.
  • Ecosse Lamoureux
    Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; Duke-NUS Medical School, Singapore.
  • Gek Hsiang Lim
  • Elaine Lum
    Health Services and Systems Research, Duke-NUS Medical School, Singapore.

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