The technology in a hospital intensive care unit (ICU) is extraordinary in its ability to pick up the slightest alterations in bodily functions and recognize when they slip out of the “normal” range. But as most ICU nurses understand all too well, having all of that clinical information does not always translate into improvements in patient care.
For one, despite their remarkable sensitivity, ICU monitors still miss some critical clinical events, in part because they cannot adequately synthesize disparate data points to give clinicians appropriate early warnings. Equally important, a UCSF study found that there were 187 audible alarms per bed per day in the ICU, with a false-positive rate of over 88 percent for arrhythmia alarms. Put that in the context of other noise in the ICU and all of the other duties nurses must perform, and you begin to understand the concept of alarm fatigue. It’s no surprise that in many settings, clinicians ignore alarms, turn them down or turn them off, leading to a nationwide epidemic that goes beyond the ICU to other hospital units.
It also explains why an interdisciplinary team of UC San Francisco researchers, led by Xiao Hu, Michele Pelter and Richard Fidler from UC San Francisco School of Nursing, is working furiously to create and test a “super alarm.” This device would aggregate disparate data, capture trending patterns and filter out false alarms, so clinicians are, in theory, only and always alerted when there is a situation that truly demands clinical attention.
The team has already shown they can achieve 90 percent sensitivity in predicting ICU cases where a patient is in need of resuscitation – known in hospitals as code blue – and is on track to complete a prospective National Institutes of Health (NIH)-funded clinical study by the end of 2017.