Journal
CRITICAL CARE NURSING CLINICS OF NORTH AMERICA
Volume 30, Issue 2, Pages 273-+Publisher
W B SAUNDERS CO-ELSEVIER INC
DOI: 10.1016/j.cnc.2018.02.009
Keywords
Predictive analytics monitoring; Implementation science; Stakeholder driven design; Learning health system; Streaming design
Categories
Funding
- University of Virginia Translational Health Research Institute of Virginia (THRIV) Scholars award
- National Center for Translational Sciences and National Institutes of Health (NIH) [KL2TR001109]
- MITRE [19140, 11348]
- MITRE Corporation
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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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