• Amol A Verma, Therese A Stukel, Michael Colacci, Shirley Bell, Jonathan Ailon, Jan O Friedrich, Joshua Murray, Sebnem Kuzulugil, Zhen Yang, Yuna Lee, Chloe Pou-Prom, and Muhammad Mamdani.
• St. Michael's Hospital (Verma, Colacci, Bell, Ailon, Friedrich, Kuzulugil, Yang, Lee, Pou-Prom, Mamdani), Unity Health Toronto; Department of Medicine (Verma, Colacci, Ailon, Friedrich, Lee, Mamdani), and Institute of Health Policy, Management, and Evaluation (Verma, Stukel, Colacci, Murray, Mamdani), and Department of Laboratory Medicine and Pathobiology (Verma, Mamdani), University of Toronto; ICES Central (Stukel); Leslie Dan Faculty of Pharmacy (Mamdani), University of Toronto, Toronto, Ont. amol.verma@mail.utoronto.ca.
• CMAJ. 2024 Sep 15; 196 (30): E1027E1037E1027-E1037.

BackgroundThe 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.MethodsIn 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.ResultsThe 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%; adjusted relative risk [RR] 0.74, 95% confidence interval [CI] 0.55-1.00) but not in the subspecialty cohorts (1.9% v. 2.1%; adjusted RR 0.89, 95% CI 0.63-1.28). Among high-risk patients in GIM for whom the system triggered at least 1 alert, the proportion of non-palliative deaths was 7.1% in the intervention period, compared with 10.3% in the pre-intervention period (adjusted RR 0.69, 95% CI 0.46-1.02), with no meaningful difference in subspecialty cohorts (10.4% v. 10.6%; adjusted RR 0.98, 95% CI 0.60-1.59). In the difference-indifferences analysis, the adjusted relative risk reduction for non-palliative death in GIM was 0.79 (95% CI 0.50-1.24).InterpretationImplementing a machine learning-based early warning system in the GIM unit was associated with lower risk of non-palliative death than in the pre-intervention period. Machine learning-based early warning systems are promising technologies for improving clinical outcomes.© 2024 CMA Impact Inc. or its licensors.

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