High-Confidence Data Programming for Evaluating Suppression of Physiological Alarms

False alarms generated by physiological monitors can overwhelm clinical caretakers with a variety of alarms. The resulting alarm fatigue can be mitigated with alarm suppression. Before being deployed, such suppression mechanisms need to be evaluated through a costly observational study, which would determine and label the truly suppressible alarms. This paper proposes a lightweight method for evaluating alarm suppression without access to the true alarm labels. The method is based on the data programming paradigm, which combines noisy and cheap-to-obtain labeling heuristics into probabilistic labels. Based on these labels, the method estimates the sensitivity/specificity of a suppression mechanism and describes the likely outcomes of an observational study in the form of confidence bounds. We evaluate the proposed method in a case study of low SpO2 alarms using a dataset collected at Children's Hospital of Philadelphia and show that our method provides tight and accurate bounds that significantly outperform the naive comparative method.