Summary
Provides consensus-defined methods and metrics for evaluating machine-learning sepsis early-warning systems, including clinical usefulness, discrimination and calibration, timeliness, provider interaction, alert performance, safety, fairness, and postdeployment monitoring.
Healthcare Implications
Healthcare organizations and developers should conduct local validation, assess calibration and alert burden, examine subgroup performance, define clinically meaningful response and outcome measures, monitor drift and workflow effects, and maintain clinical oversight of alerts and treatment decisions.