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Advancing Continuous Predictive Analytics Monitoring: Moving from Implementation to Clinical Action in a Learning
Jessica Keim-Malpass1, Rebecca R Kitzmiller2, Angela Skeeles-Worley3
1Department of Acute and Specialty Care, School of Nursing, University of Virginia, PO Box 800782, Charlottesville, VA 22908, USA; Department of Medicine, School of Medicine, University of Virginia, 1215 Lee Street, Charlottesville, VA 22908, USA.
Abstract:
In the intensive care unit, clinicians monitor a diverse array of data inputs to detect early signs of impending clinical demise or improvement. Continuous predictive analytics monitoring synthesizes data from a variety of inputs into a risk estimate that clinicians can observe in a streaming environment. For this to be useful, clinicians must engage with the data in a way that makes sense for their clinical workflow in the context of a learning health system (LHS). This article describes the processes needed to evoke clinical action after initiation of continuous predictive analytics monitoring in an LHS.
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