The Clinical Utility of Traditional and Machine Learning Alarms during the Care of Acutely Ill Patients.
Despite low-level evidence, acutely ill patients are often continuously monitored. This creates high false alarm rates and alarm fatigue with unclear clinical effectiveness. We compare metrics, including alarm burden, area under the receiver operator characteristic curve (auROC), sensitivity, and specificity for threshold, score (i.e., National Early Warning Score [NEWS]), and machine learning (ML) alarms.We retrospectively annotated continuous biometric data for acutely ill patients receiving hospital care at home for clinical [...]
Author(s): Rosario, Nicole, Mitchell, Henry M, Zhang, Sylvia, Selvaraj, Nandakumar, Zhang, Xiaozhu, Hernandez, Carme, Lipsitz, Stuart R, Levine, David M
DOI: 10.1055/a-2815-1912