Clinical evaluation of a machine learning–based early warning system for patient deterioration

作者：Amol A. Verma, Thérèse A. Stukel, Michael Colacci, Shirley Bell, Jonathan Ailon, Jan O. Friedrich, Joshua Murray, Sebnem S. Kuzulugil, Zhen Yang, Yuna Lee, Chloé Pou-Prom, Muhammad Mamdani · 发表于：Canadian Medical Association Journal · 年份：2024 · DOI：10.1503/cmaj.240132 · 被引用次数：31 · 研究领域：Sepsis Diagnosis and Treatment、Artificial Intelligence in Healthcare and Education、Machine Learning in Healthcare

BACKGROUND: The implementation and clinical impact of machine learning-based early warning systems for patient deterioration in hospitals have not been well described. We sought to describe the implementation and evaluation of a multifaceted, real-time, machine learning-based early warning system for patient deterioration used in the general internal medicine (GIM) unit of an academic medical centre. METHODS: In this nonrandomized, controlled study, we evaluated the association between the implementation of a machine learning-based early warning system and clinical outcomes. We used propensity score-based overlap weighting to compare patients in the GIM unit during the intervention period (Nov. 1, 2020, to June 1, 2022) to those admitted during the pre-intervention period (Nov. 1, 2016, to June 1, 2020). In a difference-indifferences analysis, we compared patients in the GIM unit with those in the cardiology, respirology, and nephrology units who did not receive the intervention. We retrospectively calculated system predictions for each patient in the control cohorts, although alerts were sent to clinicians only during the intervention period for patients in GIM. The primary outcome was non-palliative in-hospital death. RESULTS: The study included 13 649 patient admissions in GIM and 8470 patient admissions in subspecialty units. Non-palliative deaths were significantly lower in the intervention period than the pre-intervention period among patients in GIM (1.6% v. 2.1%; adju...