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

Abstract

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.

Experiences and mediating factors in nurses' responses to electronic device alarms: A phenomenological study

“…This need to reduce clinically irrelevant alarms should be addressed in collaboration with key stakeholders (e.g., patients and engineers) and health services, taking advantage of the development of technology with better algorithms that reduce false alarms (Bollepalli et al, 2021; Hyland et al, 2020; Ruppel, De Vaux, et al, 2018; Wilken et al, 2019). In addition, the devices must be useful, adjusting the sensitivity and specificity of the alarms, and their operation must be easy to learn (Fernandes et al, 2020; Muroi et al, 2020). Nurse managers must acquire modern devices and facilitate the training and expertise of nurses in the use of those devices.…”

How to Annotate Patient Monitoring Alarms in Intensive Care Medicine for Machine Learning

“…We identi ed 20 studies containing 23 associated alarm annotation reports in our systematic review [3,[15][16][17][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41] (details in the PRISMA ow chart, Supplementary Fig. 1 [42]), and summarized the ndings (Supplementary Table 1).…”

“…[37] The researchers mostly used data from monitoring systems including waveforms, measurements, alarms, and settings. Four reports used Medical Information Mart for Intensive Care (MIMIC) II records [17,32], recordings from the training set of the PhysioNet Challenge 2015 [40], or simulated data [39]. Video recordings aided the annotation in six reports [3, 23-25, 28, 34], and three studies used documentation from medical records, discharge summaries, consultation notes, etc.…”

“…Most of the reports (22 of 23) stated the size of the annotated dataset, ranging from 20 to 12,671, with an average of 4,183 annotated alarms [29,35,39]. The annotation (and adjudication) process involved two or more people in 14 reports, with a maximum of 11 volunteers [17]; six involved one person and three did not mention any numbers.…”

Determining Factors of Alarm Fatigue among Nurses in Intensive Care Units—A Polish Pilot Study

“…Alarms resulting in a clinical response or intervention constitute only 5-13% of alarms in the monitoring systems [6]. A large number of false alarms is still a problem that is difficult to solve in clinical practice [7].…”