Key result
Fast-and-frugal tree models using visually available parameters predicted patient acuity with 77.78% accuracy, performing comparably to more complex ordered logistic regression models.
Observational (n=17)
No
Absolute Event Rate: 77.78% vs 81.48%
Fast-and-frugal heuristics based on ventilator presence, number of drips, and medication quantity can mimic nurse assessments of patient acuity and could be integrated into automated monitors to reduce alarm fatigue.
May support heuristic-based alarm prioritization in monitoring; hypothesis-generating for fatigue reduction pending prospective validation.
Automated patient monitoring systems suer from several design problems. Among them, alarm fatigue is one of the most critical issues, as evidenced by the Sentinel Event Alert that The Joint Commission – the U.S. hospital-accrediting body – recently issued. In this study, we explore fast-and-frugal heuristics that may be used to prioritize patient alarms, while continuing to monitor patient physiological state. By using a combination of human factors methodologies and the theory of Distributed Cognition (DCog), we studied alarm fatigue and its relationship to the underlying hospital systems. We identified three specific factors that we envision to be helpful for clinical personnel: ventilator presence, number of intravenous drips, and number of medications. We discuss their application in daily hospital operation. We also address cost-benefit considerations and possible monitor designs.
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Hussain et al. (2017) conducted an observational in Alarm fatigue in Intensive Care Units (n=17). Fast-and-frugal cognitive heuristics (ventilator presence, number of drips, medication quantity) vs. Ordered logistic regression models was evaluated on Accuracy in predicting patient acuity (acute vs not acute). Fast-and-frugal tree models using visually available parameters predicted patient acuity with 77.78% accuracy, performing comparably to more complex ordered logistic regression models.
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