Premium
Alarm limit settings for early warning systems to identify at‐risk patients
Author(s) -
Burgess Lawrence P.A.,
Herdman Tracy Heather,
Berg Benjamin W.,
Feaster William W.,
Hebsur Shashidhar
Publication year - 2009
Publication title -
journal of advanced nursing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.948
H-Index - 155
eISSN - 1365-2648
pISSN - 0309-2402
DOI - 10.1111/j.1365-2648.2009.05048.x
Subject(s) - alarm , warning system , medicine , false alarm , early warning score , population , emergency medicine , medical emergency , vital signs , constant false alarm rate , intensive care medicine , computer science , environmental health , artificial intelligence , surgery , engineering , telecommunications , aerospace engineering
Title. Alarm limit settings for early warning systems to identify at‐risk patients.Aim. This paper is a report of a study conducted to provide objective data to assist with setting alarm limits for early warning systems. Background. Early warning systems are used to provide timely detection of patient deterioration outside of critical care areas, but with little data from the general ward population to guide alarm limit settings. Monitoring systems used in critical care areas are known for excellent sensitivity in detecting signs of deterioration, but give high false positive alarm rates, which are managed with nurses caring for two or fewer patients. On general wards, nurses caring for four or more patients will be unable to manage a high number of false alarms. Physiological data from a general ward population would help to guide alarm limit settings. Methods. A dataset of continuous heart rate and respiratory rate data from a general ward population, previously collected from July 2003–January 2006, was analyzed for adult patients with no severe adverse events. Dataset modeling was constructed to analyze alarm frequency at varying heart rate and respiratory rate alarm limits. Results. A total of 317 patients satisfied the inclusion criteria, with 780·71 days of total monitoring. Sample alarm settings appeared to optimize false positive alarm rates for the following settings: heart rate high 130–135, low 40–45; respiratory rate high 30–35, low 7–8. Rates for each selected limit can be added to calculate the total alarm frequency, which can be used to judge the impact on nurse workflow. Conclusion. Alarm frequency data will assist with evidence‐based configuration of alarm limits for early warning systems.