Artificial Intelligence Technologies for Coping with Alarm Fatigue in Hospital Environments Because of Sensory Overload: Algorithm Development and Validation

Abstract

BackgroundInformed estimates claim that 80% to 99% of alarms set off in hospital units are false or clinically insignificant, representing a cacophony of sounds that do not present a real danger to patients. These false alarms can lead to an alert overload that causes a health care provider to miss important events that could be harmful or even life-threatening. As health care units become more dependent on monitoring devices for patient care purposes, the alarm fatigue issue has to be addressed as a major concern for the health care team as well as to enhance patient safety.ObjectiveThe main goal of this paper was to propose a feasible solution for the alarm fatigue problem by using an automatic reasoning mechanism to decide how to notify members of the health care team. The aim was to reduce the number of notifications sent by determining whether or not to group a set of alarms that occur over a short period of time to deliver them together, without compromising patient safety.MethodsThis paper describes: (1) a model for supporting reasoning algorithms that decide how to notify caregivers to avoid alarm fatigue; (2) an architecture for health systems that support patient monitoring and notification capabilities; and (3) a reasoning algorithm that specifies how to notify caregivers by deciding whether to aggregate a group of alarms to avoid alarm fatigue.ResultsExperiments were used to demonstrate that providing a reasoning system can reduce the notifications received by the caregivers by up to 99.3% (582/586) of the total alarms generated. Our experiments were evaluated through the use of a dataset comprising patient monitoring data and vital signs recorded during 32 surgical cases where patients underwent anesthesia at the Royal Adelaide Hospital. We present the results of our algorithm by using graphs we generated using the R language, where we show whether the algorithm decided to deliver an alarm immediately or after a delay.ConclusionsThe experimental results strongly suggest that this reasoning algorithm is a useful strategy for avoiding alarm fatigue. Although we evaluated our algorithm in an experimental environment, we tried to reproduce the context of a clinical environment by using real-world patient data. Our future work is to reproduce the evaluation study based on more realistic clinical conditions by increasing the number of patients, monitoring parameters, and types of alarm.

Pragmatic Considerations on Clinical Decision Support from the 2019 Literature

“…Design alerts may also avoid rejection of CDSSs by caregivers. Fernandes et al, [15] created algorithms able to aggregate, filter, and reduce the notifications delivered to healthcare professionals. Amrose et al, [16] tried to understand in real life the impact of alerts on users and to find the actions they triggered.…”

Uma Arquitetura para Sistemas de Saúde Eletrônicos que Suporta o Monitoramento de Pacientes e a Notificação de Cuidadores com base em Raciocínio Automático para evitar a Fadiga de Alarme

“…Nesta seção, discutimos nosso algoritmo desenvolvido para mitigar a FA através do uso de um mecanismo de raciocínio que notifica os cuidadores agrupando alertas semelhantes (Fernandes et al, 2019). A solução que fornecemos permite reduzir o número de notificações recebidas pelo staff médico, de modo que eles possam focar nas atividades que realmente requerem a devida atenção.…”

“…Neste estudo de caso, definimos um MNI de 5 minutos para todos os experimentos. Os delays ocorridos entre o disparo dos alertas e a entrega das notificações, para o experimento 5, podem ser verificados através da visualização dos gráficos plotados em (Fernandes et al, 2019). Ao final dos experimentos, comparamos o número de alarmes disparados em nosso sistema com o número de notificações entregues aos cuidadores.…”

“…Nesta seção, nós discutimos como podemos raciocinar sobre como adicionar uma indicação de probabilidade de falso alarme a uma notificação (FAP_LABEL) (Fernandes et al, 2019). O FAP_LABEL é calculado de acordo com indicadores de falso alarme que nós definimos com base na nossa revisão da literatura.…”

Information Overload in Emergency Medicine Physicians: A Multisite Case Study Exploring the Causes, Impact, and Solutions in Four North England National Health Service Trusts

“…Digital technologies are proving to be both a curse and a blessing. On the one hand, they represent yet another source of—often unwanted information [ 31 ]—but, on the other hand, they can support physicians in the detection of adverse events and patients at risk of deterioration with the potential to improve health outcomes [ 32 ]. This is only achievable if, when planning and implementing digital information solutions in hospitals and clinics, factors such as the provision of clinical training, resources to cope with the additional workload, and optimization of algorithms to reduce superfluous alerts are taken into consideration [ 32 ].…”