Classification of Methods to Reduce Clinical Alarm Signals for Remote Patient Monitoring: A Critical Review
This addresses alarm fatigue in clinicians by reducing false alarms in RPM, but it is incremental as it reviews and organizes existing methods rather than introducing new solutions.
The study critically reviews literature to identify causes of false-positive alarms in Remote Patient Monitoring (RPM) and categorizes interventions to reduce them, proposing a step-by-step approach and a pentagon strategy for developing effective alarm signal generators.
Remote Patient Monitoring (RPM) is an emerging technology paradigm that helps reduce clinician workload by automated monitoring and raising intelligent alarm signals. High sensitivity and intelligent data-processing algorithms used in RPM devices result in frequent false-positive alarms, resulting in alarm fatigue. This study aims to critically review the existing literature to identify the causes of these false-positive alarms and categorize the various interventions used in the literature to eliminate these causes. That act as a catalog and helps in false alarm reduction algorithm design. A step-by-step approach to building an effective alarm signal generator for clinical use has been proposed in this work. Second, the possible causes of false-positive alarms amongst RPM applications were analyzed from the literature. Third, a critical review has been done of the various interventions used in the literature depending on causes and classification based on four major approaches: clinical knowledge, physiological data, medical sensor devices, and clinical environments. A practical clinical alarm strategy could be developed by following our pentagon approach. The first phase of this approach emphasizes identifying the various causes for the high number of false-positive alarms. Future research will focus on developing a false alarm reduction method using data mining.