No description available.

No description available.

There is limited clinical research on the effectiveness of alarm management strategies and nursing behaviors related to alarms in clinical settings. As many as 76% of physiological monitor alarms are overlooked as clinically insignificant by nursing staff. Excessive alarms may impact patient outcomes and cause cognitive overload for nurses that can result in medical errors and missed patient resuscitations. The purpose of this systematic review was to rate alarm management studies on level of evidence for interventions, nursing responses to alarms, and impact on alarm fatigue behavior. The nursing role effectiveness model guided this project. Twenty-seven studies were reviewed to analyze outcome effectiveness by addressing structure, process, and outcomes related to how the roles of the nurse affect nurse-sensitive patient outcomes. The Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) and the Cochrane guidelines guided study selection and analysis. A second reviewer collaborated on the search strategy and provided an independent review of the identified literature. The effectiveness of alarm management was difficult to determine because most studies were descriptive, cohort, or nonrandomized trials. Review findings did not support a relationship between the amount of alarms and increased alarm fatigue behaviors. Findings indicated that nurses' attitudes and alarm fatigue behaviors are present globally and have not significantly altered since reduction strategies were implemented. The findings may impact social change by decreasing nurses' stress levels related to cognitive workloads, improving patient outcomes, and supporting increased levels of nurses' workforce satisfaction.

Background: Alarm interpretation and management are fundamental to managing critically ill patients. 1 There is little research as to the decision process nurses use to prioritize alarms or manage specific monitor parameters. Objective: The purpose of this study is to gain insight into the intricacy of the intensive care unit (ICU) nurses'critical decision process, using a human performance framework, when responding to monitor alarms. Method: Design: Descriptive design using semi-structured interview. Open-ended questions were developed based on the critical decision method (CDM) to explore ICU nurses' critical decision making process related to monitor alarms. Sixteen ICU nurses at a community hospital were interviewed to elicit perceptions and thought processes related to monitor alarms. Results: Responses to monitor alarms were affected by nursing experience, tones of the alarm, nurses' knowledge of the patient's condition as well as immediate visualization of patient to judge the urgency of an alarm. Both advanced beginner and expert nurses had similar initial response to monitor alarms; however, expert nurses added depth to their immediate assessment process by using previous experiences, intuition, and clinical expertise. Advanced beginner nurses frequently look to expert nurses for advice, guidance, and examples of clinical expertise. The majority of nurses had little or no formal training on the cardiac monitors used by that facility and all felt it would be beneficial in monitor alarm management. Conclusion: Understanding the decision-making process used by nurses can guide the development of policies and learning experiences that are crucial clinical support for alarm management.

According to The Joint Commission (TJC), 98 unexpected and unacceptable events related to alarm fatigue were reported in United States hospitals between January 2009 and June 2012. There were 80 deaths, 13 permanent loss of function, and 5 extended care stays that occurred during this time period. The problem identified in this quality improvement (QI) initiative was the TJC report that nursing staff in the US was experiencing alarm fatigue due to the overstimulation of senses from continuous beeping from alarms on the unit. Framed within the Iowa model of evidence-based practice to promote quality care, the purpose of the project was to develop a patient care alarm fatigue initiative as mandated by TJC including a policy and procedure for managing alarm fatigue, a curriculum plan for educating the nursing staff on alarm fatigue, and a survey on nurse attitudes toward alarm fatigue to be administered at the beginning of the education. The developed policy and procedure was approved by the committee with the recommendation to revise the policy to involve all ancillary staff in direct contact with clinical alarms. The curriculum objectives were evaluated by 2 content experts using a 4 item met/not met response format. Findings showed that all objectives were met. The content of the nurse survey was reviewed by the experts using a 3 item Likert scale and all the items were deemed relevant. Finally, team members (n = 9) completed a summative evaluation of the project using an 8 item, 5-option Likert scale. All were in agreement that the project met its intent. The implementation of this project after graduation has the potential to bring about social change by increasing patient safety, patient well being and reducing healthcare costs.

Alarm fatigue is a practice problem that applies to hospitalized patients and the nurses who care for them. Addressing alarm fatigue is important to promote alarm safety and to decrease the risk of patient harm or death. The purpose of this study was to decrease alarm fatigue and improve alarm safety in a regional neonatal intensive care unit (RNICU). Guided by the conceptual model for alarm fatigue and alarm safety, this study addressed whether or not alarm management protocols designed to decrease false and nuisance alarms in the physiological monitoring of neonates improve alarm safety via decreased alarm burden and alarm fatigue as evidenced by statistically significant reductions in false and nuisance alarms. A quantitative, time series quasi-experimental design was used with 4 waves of data collection. One wave was baseline data collected preintervention, and 3 waves of data were postprotocol implementation to obtain an initial indication of sustainability. Alarm observation data collection sheets were developed and used to track numbers and types of alarms pre- and post-protocol implementation. The data analysis showed statistically significant decreases in both false alarms and nuisance alarms related to the physiological monitoring protocol and lead changing protocol. Overall, high protocol adherence was noted, and the total number of alarms per hour per bed was reduced by 42% (p < .001), 46% (p < .001), and 50% (p < .001) from baseline at Weeks 2, 4, and 6, respectively. Implications from this study include impact on practice and policy, direction for future study, and a call for social change to promote alarm safety in the care of neonates.

Today’s intensive care units monitor patients through the use of various medical devices, which generate a high ratio of false positive alarms due to a low alarm specificity. The false alarms have resulted in a stressful working environment for healthcare professionals that are getting more desensitized to triggered alarms and causing alarm fatigue. The patient safety is also compromised by having high noise levels in the patient room, which disturbs their sleep. This thesis has developed an intelligent alarm system with an improved alarm management and the use of 23 intelligent algorithms to minimize the number of false positive alarms. The suggested system is capable of improving the alarm situation and increasing the patient safety in critical care. The algorithms were modeled with fuzzy logics consisting of delays and multi parameter validation. The results were iteratively developed by having focus groups with various experts.

[pt] Estimativas informam que 80 por cento a 99 por cento dos alarmes disparados em unidades hospitalares são falsos ou clinicamente insignificantes, representando uma cacofonia de sons que não apresenta perigo real aos pacientes. Estes falsos alertas podem culminar em uma sobrecarga de alertas que leva um profissional da saúde a perder eventos importantes que podem ser prejudiciais aos pacientes ou até mesmo fatais. À medida que as unidades de saúde se tornam mais dependentes de dispositivos de monitoramento que acionam alarmes, o problema da fadiga de alarme deve ser tratado como uma das principais questões, a fim de prevenir a sobrecarga de alarme para os profissionais da saúde e aumentar a segurança do paciente. O principal objetivo desta tese é propor uma solução para o problema de fadiga de alarme usando um mecanismo de raciocínio automático para decidir como notificar os membros da equipe de saúde. Nossos objetivos específicos são: reduzir o número de notificações enviadas à equipe de cuidadores; detectar alarmes falsos com base em informações de contexto do alarme; decidir o melhor cuidador a quem uma notificação deve ser atribuída. Esta tese descreve: um modelo para suportar algoritmos de raciocínio que decidem como notificar os profissionais de saúde para evitar a fadiga de alarme; uma arquitetura para sistemas de saúde que suporta recursos de monitoramento, raciocínio e notificação de pacientes; e três algoritmos de raciocínio que decidem: (i) como notificar os profissionais de saúde decidindo quando agrupar um conjunto de alarmes; (ii) se deve ou não notificar os profissionais de saúde com uma indicação de probabilidade de falso alarme; (iii) quem é o melhor cuidador a ser notificado considerando um grupo de cuidadores. Experimentos foram realizados para demonstrar que, ao fornecer um sistema de raciocínio que agrupa alarmes semelhantes e recorrentes, pode-se reduzir o total de notificações recebidas pelos cuidadores em até 99.3 por cento do total de alarmes gerados, sem perda de informação útil. Esses experimentos foram avaliados através do uso de um conjunto de dados reais de monitoramento de sinais vitais de pacientes registrados durante 32 casos cirúrgicos nos quais os pacientes foram submetidos à anestesia, no hospital Royal Adelaide. Apresentamos os resultados desse algoritmo através de gráficos gerados na linguagem R, onde mostramos se o algoritmo decidiu emitir um alarme imediatamente ou após um determinado delay. Para a tarefa de atribuição de notificações realizada pelo nosso algoritmo de raciocínio que decide sobre qual cuidador notificar, também alcançamos nossos resultados esperados, uma vez que o algoritmo priorizou o cuidador que estava disponível no momento do alarme, além de ser o mais experiente e capaz de atender à notificação. Os resultados experimentais sugerem fortemente que nossos algoritmos de raciocínio são uma estratégia útil para evitar a fadiga de alarme. Embora tenhamos avaliado nossos algoritmos em um ambiente experimental, tentamos reproduzir o contexto de um ambiente clínico utilizando dados reais de pacientes. Como trabalho futuro, visamos avaliar os resultados de nossos algoritmos utilizando condições clínicas mais realistas, aumentando, por exemplo, o número de pacientes, os parâmetros de monitoramento e os tipos de alarme. / [en] Estimates show that 80 per cent to 99 per cent 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 in order to prevent healthcare providers from undergoing alarm burden, as well as to increase patient safety. The main goal of this thesis is to propose a solution for the alarm fatigue problem by using an automatic reasoning mechanism to decide how to notify members of the health care team. Our specific goals are: to reduce the number of notifications sent to caregivers; to detect false alarms based on alarm-context information; to decide the best caregiver to whom a notification should be assigned. This thesis describes: a model to support reasoning algorithms that decide how to notify caregivers in order to avoid alarm fatigue; an architecture for health systems that supports patient monitoring, reasoning and notification capabilities; and three reasoning algorithms that decide: (i) how to notify caregivers by deciding whether to aggregate a group of alarms; (ii) whether, or not, to notify caregivers with an indication of a false alarm probability; (iii) who is the best caregiver to notify considering a group of caregivers. Experiments were used to demonstrate that by providing a reasoning system that aggregates alarms we can reduce the total of notifications received by the caregivers by up to 99.3 per cent of the total alarms generated. These experiments were evaluated through the use of a dataset comprising real 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 this algorithm by using graphs generated with the R language, which show whether the algorithm decided to deliver an alarm immediately or after a given delay. We also achieved the expected results for our reasoning algorithm that handles the notifications assignment task, since the algorithm prioritized the caregiver that was available and was the most experienced and capable of attending to the notification. The experimental results strongly suggest that our reasoning algorithms are a useful strategy to avoid alarm fatigue. Although we evaluated our algorithms in an experimental environment, we tried to reproduce the context of a clinical environment by using real-world patient data. As future work, we aim to evaluate our algorithms using more realistic clinical conditions by increasing, for example, the number of patients, monitoring parameters, and types of alarm.