Classification of Methods to Reduce Clinical Alarm Signals for Remote Patient Monitoring: A Critical Review

TL;DR

This paper critically reviews methods to reduce false-positive alarms in remote patient monitoring, categorizing interventions based on causes and proposing a step-by-step approach for clinical alarm signal improvement.

Contribution

It provides a comprehensive classification of false alarm causes and interventions, and introduces a pentagon approach for developing effective clinical alarm strategies.

Findings

Identified key causes of false-positive alarms in RPM.

Categorized interventions into four major approaches.

Proposed a step-by-step alarm signal development process.

Abstract

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.