Date of Completion
Dr. Quing Zhu; Frank Painter
Field of Study
Master of Science
Alarm fatigue, the progressive desensitization of clinical staff to audible alarms in their environment, has been re-established as a National Patient Safety Goal by The Joint Commission as of January 1, 2014. In order to manage the number of alarms experienced by hospital employees, facilities are charged with finding a way to monitor their existing alarm load and then develop methods and policies by which to reduce or mitigate the threat of alarm fatigue. This study used an archive of 9.76 million patient monitoring alarms collected over an eight-month period in order to develop a process by which patient monitoring alarms could be analyzed with high specificity across multiple units of differing specialties and acuity levels.
Trends in the distributions of patient monitoring alarm data were identified, with the greatest potential contributors to alarm fatigue being confirmed as the medium and low priority alarms, comprising greater than 70% of all patient monitoring alarms observed, and condition-based distributions showed strong correlation to average distributions across patient care units of differing specialties and acuity levels. These distributions demonstrated the expected exponential decay in frequency of alarms as the condition shifted farther away from the physiologically acceptable range, with one exception. A trend of spikes in high heart rate alarms at heart rate values that were multiples of 10 was observed, and at multiples of five for low heart rate alarms, suggesting that heart rate limits should be considered differently than those for other vital signs. The time of day in which alarms initiated was also analyzed, showing a direct relation between periods of time where patients are interacted with by clinical and/or support staff, with a physiological sensitivity to these interactions increasing as the acuity level of the unit increased.
Ryan, Edward J. IV, "Improving Alarm Management Efficacy through Predictive Modeling and Trending" (2014). Master's Theses. 566.
Dr. John Enderle