On The Quick
UCSF Takes Aim at Alarm Fatigue
A super alarm is in the works
Anyone who’s ever worked in acute care is all too familiar with the concept of alarm fatigue. Now, a team from UCSF School of Nursing is doing something about it.
Making Alarms Smarter
A 2014 study conducted in five UCSF Medical Center ICUs over a single month recorded 2,558,760 different alarms, of which more than 40 percent were crying wolf. The negative effects on patients and providers alike hardly needs elaboration.
What can be done? Turning the alarms off clearly isn’t the answer. Instead, the alarms need to get smarter.
Tackling that challenge is an interdisciplinary alliance of UCSF School of Nursing researchers, including Assistant Professor Michele Pelter, RN, Ph.D., director of UCSF’s ECG Monitoring Research Lab; Xiao Hu, Ph.D., associate professor and biomedical engineering expert; and Assistant Clinical Professor Rich Fidler, CRNA, NP, Ph.D., MBA, co-director of the VA’s Interdisciplinary Simulation Center in San Francisco
Together, they’ve embarked on an NIH-funded project to develop and validate a “SuperAlarm” that can more accurately detect developing emergencies with a much lower rate of false positives.
More Sophisticated Analysis
The heart of the SuperAlarm is a concept called “data fusion”: combining data from multiple sources (including different sensors and a patient’s medical history) to allow more sophisticated analysis.
Data fusion makes it possible to recognize trends and patterns that suggest adverse events like an imminent heart attack while filtering out false positives.
For the NIH study, which will be completed by late next year, the UCSF team is using this principle to better identify potential code blue events. The prototype SuperAlarm will be tested in ICUs at UCSF Medical Center to assess how well the new approach compares with existing alarms and protocols.
“We hope that this is only a starting point to developing an approach for other conditions,” says Pelter.
This article is from workingnurse.com.