TY - JOUR
T1 - Alert-Grouping
T2 - Smart Personalization of Monitoring System Thresholds to Help Healthcare Teams Struggle with Alarm Fatigue in Intensive Care
AU - Rozenes, Shai
AU - Fux, Adi
AU - Kagan, Ilya
AU - Hellerman, Moran
AU - Tadmor, Boaz
AU - Benis, Arriel
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2023/12
Y1 - 2023/12
N2 - In Intensive Care Units (ICUs), patients are monitored using various devices that generate alerts when specific metrics, such as heart rate and oxygen saturation, exceed predetermined thresholds. However, these alerts can be inaccurate and lead to alert fatigue, resulting in errors and inaccurate diagnoses. We propose Alert grouping, a “Smart Personalization of Monitoring System Thresholds to Help Healthcare Teams Struggle Alarm Fatigue in Intensive Care” model. The alert grouping looks at patients at the individual and cluster levels, and healthcare-related constraints to assist medical and nursing teams in setting personalized alert thresholds of vital parameters. By simulating the function of ICU patient bed devices, we demonstrate that the proposed alert grouping model effectively reduces the number of alarms overall, improving the alert system’s validity and reducing alarm fatigue. Implementing this personalized alert model in ICUs boosts medical and nursing teams’ confidence in the alert system, leading to better care for ICU patients by significantly reducing alarm fatigue, thereby improving the quality of care for ICU patients.
AB - In Intensive Care Units (ICUs), patients are monitored using various devices that generate alerts when specific metrics, such as heart rate and oxygen saturation, exceed predetermined thresholds. However, these alerts can be inaccurate and lead to alert fatigue, resulting in errors and inaccurate diagnoses. We propose Alert grouping, a “Smart Personalization of Monitoring System Thresholds to Help Healthcare Teams Struggle Alarm Fatigue in Intensive Care” model. The alert grouping looks at patients at the individual and cluster levels, and healthcare-related constraints to assist medical and nursing teams in setting personalized alert thresholds of vital parameters. By simulating the function of ICU patient bed devices, we demonstrate that the proposed alert grouping model effectively reduces the number of alarms overall, improving the alert system’s validity and reducing alarm fatigue. Implementing this personalized alert model in ICUs boosts medical and nursing teams’ confidence in the alert system, leading to better care for ICU patients by significantly reducing alarm fatigue, thereby improving the quality of care for ICU patients.
KW - Alarm fatigue
KW - Alert fatigue
KW - Intensive care units
KW - Patient safety
KW - Personalized medicine
KW - Quality of health care
UR - http://www.scopus.com/inward/record.url?scp=85175791292&partnerID=8YFLogxK
U2 - 10.1007/s10916-023-02010-6
DO - 10.1007/s10916-023-02010-6
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C2 - 37934335
AN - SCOPUS:85175791292
SN - 0148-5598
VL - 47
JO - Journal of Medical Systems
JF - Journal of Medical Systems
IS - 1
M1 - 113
ER -