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

• URL: http://arxiv.org/abs/2302.03885v1
• Date: Wed, 8 Feb 2023 05:21:02 GMT
• Title: Classification of Methods to Reduce Clinical Alarm Signals for Remote Patient Monitoring: A Critical Review
• Authors: Teena Arora, Venki Balasubramanian, Andrew Stranieri, Shenhan Mai, Rajkumar Buyya, Sardar Islam
• Abstract summary: 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.
• Score: 16.140794437173014
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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