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Alarm limit settings for early warning systems to identify at-risk patients |
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| Authors: | Lawrence PA Burgess Tracy Heather Herdman Benjamin W Berg William W Feaster & Shashidhar Hebsur |
| Institution: | Lawrence P.A. Burgess MD Vice President for Medical Affairs, Hoana Medical, Inc. and Professor of Surgery, University of Hawaii School of Medicine Honolulu, USA; Tracy Heather Herdman PhD RN Vice President for Nursing and Safety Hoana Medical, Inc. Honolulu, Hawaii, USA; Benjamin W. Berg MD Associate Professor of Medicine University of Hawaii School of Medicine Honolulu, USA; William W. Feaster MD Clinical Professor of Anesthesia and Pediatrics Stanford University School of Medicine California, USA; Shashidhar Hebsur MSEE Software Manager Hoana Medical, Inc. Honolulu, Hawaii, USA |
| Abstract: | 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. |
| Keywords: | alarm limits early warning systems monitor nursing patient safety vigilance |