  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.
