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Detecting False Alarms by Analyzing Alarm-Context Information: Algorithm Development and Validation

BACKGROUND: Although alarm safety is a critical issue that needs to be addressed to improve patient care, hospitals have not given serious consideration about how their staff should be using, setting, and responding to clinical alarms. Studies have indicated that 80%-99% of alarms in hospital units...

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Detalles Bibliográficos
Autores principales: Fernandes, Chrystinne, Miles, Simon, Lucena, Carlos José Pereira
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7270842/
https://www.ncbi.nlm.nih.gov/pubmed/32432551
http://dx.doi.org/10.2196/15407
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author Fernandes, Chrystinne
Miles, Simon
Lucena, Carlos José Pereira
author_facet Fernandes, Chrystinne
Miles, Simon
Lucena, Carlos José Pereira
author_sort Fernandes, Chrystinne
collection PubMed
description BACKGROUND: Although alarm safety is a critical issue that needs to be addressed to improve patient care, hospitals have not given serious consideration about how their staff should be using, setting, and responding to clinical alarms. Studies have indicated that 80%-99% of alarms in hospital units are false or clinically insignificant and do not represent real danger for patients, leading caregivers to miss relevant alarms that might indicate significant harmful events. The lack of use of any intelligent filter to detect recurrent, irrelevant, and/or false alarms before alerting health providers can culminate in a complex and overwhelming scenario of sensory overload for the medical team, known as alarm fatigue. OBJECTIVE: This paper’s main goal is to propose a solution to mitigate alarm fatigue by using an automatic reasoning mechanism to decide how to calculate false alarm probability (FAP) for alarms and whether to include an indication of the FAP (ie, FAP_LABEL) with a notification to be visualized by health care team members designed to help them prioritize which alerts they should respond to next. METHODS: We present a new approach to cope with the alarm fatigue problem that uses an automatic reasoner to decide how to notify caregivers with an indication of FAP. Our reasoning algorithm calculates FAP for alerts triggered by sensors and multiparametric monitors based on statistical analysis of false alarm indicators (FAIs) in a simulated environment of an intensive care unit (ICU), where a large number of warnings can lead to alarm fatigue. RESULTS: The main contributions described are as follows: (1) a list of FAIs we defined that can be utilized and possibly extended by other researchers, (2) a novel approach to assess the probability of a false alarm using statistical analysis of multiple inputs representing alarm-context information, and (3) a reasoning algorithm that uses alarm-context information to detect false alarms in order to decide whether to notify caregivers with an indication of FAP (ie, FAP_LABEL) to avoid alarm fatigue. CONCLUSIONS: Experiments were conducted to demonstrate that by providing an intelligent notification system, we could decide how to identify false alarms by analyzing alarm-context information. The reasoner entity we described in this paper was able to attribute FAP values to alarms based on FAIs and to notify caregivers with a FAP_LABEL indication without compromising patient safety.
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spelling pubmed-72708422020-06-05 Detecting False Alarms by Analyzing Alarm-Context Information: Algorithm Development and Validation Fernandes, Chrystinne Miles, Simon Lucena, Carlos José Pereira JMIR Med Inform Original Paper BACKGROUND: Although alarm safety is a critical issue that needs to be addressed to improve patient care, hospitals have not given serious consideration about how their staff should be using, setting, and responding to clinical alarms. Studies have indicated that 80%-99% of alarms in hospital units are false or clinically insignificant and do not represent real danger for patients, leading caregivers to miss relevant alarms that might indicate significant harmful events. The lack of use of any intelligent filter to detect recurrent, irrelevant, and/or false alarms before alerting health providers can culminate in a complex and overwhelming scenario of sensory overload for the medical team, known as alarm fatigue. OBJECTIVE: This paper’s main goal is to propose a solution to mitigate alarm fatigue by using an automatic reasoning mechanism to decide how to calculate false alarm probability (FAP) for alarms and whether to include an indication of the FAP (ie, FAP_LABEL) with a notification to be visualized by health care team members designed to help them prioritize which alerts they should respond to next. METHODS: We present a new approach to cope with the alarm fatigue problem that uses an automatic reasoner to decide how to notify caregivers with an indication of FAP. Our reasoning algorithm calculates FAP for alerts triggered by sensors and multiparametric monitors based on statistical analysis of false alarm indicators (FAIs) in a simulated environment of an intensive care unit (ICU), where a large number of warnings can lead to alarm fatigue. RESULTS: The main contributions described are as follows: (1) a list of FAIs we defined that can be utilized and possibly extended by other researchers, (2) a novel approach to assess the probability of a false alarm using statistical analysis of multiple inputs representing alarm-context information, and (3) a reasoning algorithm that uses alarm-context information to detect false alarms in order to decide whether to notify caregivers with an indication of FAP (ie, FAP_LABEL) to avoid alarm fatigue. CONCLUSIONS: Experiments were conducted to demonstrate that by providing an intelligent notification system, we could decide how to identify false alarms by analyzing alarm-context information. The reasoner entity we described in this paper was able to attribute FAP values to alarms based on FAIs and to notify caregivers with a FAP_LABEL indication without compromising patient safety. JMIR Publications 2020-05-20 /pmc/articles/PMC7270842/ /pubmed/32432551 http://dx.doi.org/10.2196/15407 Text en ©Chrystinne Fernandes, Simon Miles, Carlos José Pereira Lucena. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 20.05.2020. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on http://medinform.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Fernandes, Chrystinne
Miles, Simon
Lucena, Carlos José Pereira
Detecting False Alarms by Analyzing Alarm-Context Information: Algorithm Development and Validation
title Detecting False Alarms by Analyzing Alarm-Context Information: Algorithm Development and Validation
title_full Detecting False Alarms by Analyzing Alarm-Context Information: Algorithm Development and Validation
title_fullStr Detecting False Alarms by Analyzing Alarm-Context Information: Algorithm Development and Validation
title_full_unstemmed Detecting False Alarms by Analyzing Alarm-Context Information: Algorithm Development and Validation
title_short Detecting False Alarms by Analyzing Alarm-Context Information: Algorithm Development and Validation
title_sort detecting false alarms by analyzing alarm-context information: algorithm development and validation
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7270842/
https://www.ncbi.nlm.nih.gov/pubmed/32432551
http://dx.doi.org/10.2196/15407
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