BACKGROUND
Alarm Fatigue is a scenario experienced by an overwhelmed and fatigued healthcare team that is desensitized and slow to respond to alarms. The most common alarm-related issues that may lead to Alarm Fatigue include the excessive number of alarms, a number of alarms generated by many different types of alarm devices, and the high percentage of false alarms (80%-99%). All of these alerts have to be processed by the healthcare teams who are consistently under pressure: they should analyze the high volume of inputs they are receiving in order to answer to them quickly and correctly, by making decisions in real-time about the response to the next alarm. Under alarm fatigue conditions, the staff may ignore and/or silence alarms, putting patients in risky situations.
OBJECTIVE
This paper’s main goal is to propose a feasible solution for mitigating alarm fatigue by using an automatic reasoning mechanism to choose the best caregiver to be assigned to a given notification within the set of available caregivers in an Intensive Care Unit.
METHODS
Our main contribution in this work consists of an algorithm that decides who is the best caregiver to notify in an ICU. We formalized this problem as a Constraint-Satisfaction Problem and we present one example of how it can be solved. We designed a case study where patients’ vital signs were collected through a vital signs’ generator that also triggers alarms. We conducted five experiments to test our algorithm considering different situations for an ICU. The evaluation of our algorithm was made through the comparison between the results of the choices made by our reasoning algorithm and another strategy that we call “blind” strategy, which randomly assigns caregivers to notifications.
RESULTS
Experiments are used to demonstrate that providing a reasoning system we could decide who is the best caregiver to receive a notification. By comparing the choices made by our reasoning algorithm and the “blind” strategy, our reasoning algorithm achieved a better result in terms of prioritizing the assignments we wanted to make based on our defined criteria: patient’s severity, the distance between caregivers and patients, caregivers’ experience, the probability of a notification to be false, and the number of notifications caregivers have received.
CONCLUSIONS
The experimental results strongly suggest that this reasoning algorithm is a useful strategy for mitigating alarm fatigue. We showed, in our experiments, that caregivers with higher levels of experience received more notifications than the ones with lower levels. Our future work is to deal with resource negotiation and to evaluate the distribution of the notifications to the caregivers’ teams made by the algorithms.