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Crit Care Nurs Clin North Am. 2018 Jun;30(2):273-287. doi: 10.1016/j.cnc.2018.02.009.

Advancing Continuous Predictive Analytics Monitoring: Moving from Implementation to Clinical Action in a Learning Health System.

Author information

1
Department 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. Electronic address: Jlk2t@virginia.edu.
2
School of Nursing, University of North Carolina, Carrington Hall, South Columbia Street, Chapel Hill, NC 27599, USA.
3
School of Education, University of Virginia, 405 Emmet Street South, Charlottesville, VA 22903, USA.
4
Billings Clinic, 801 North 29th Street, Billings, MT 59101, USA.
5
Advanced Medical Predictive Devices, Diagnostics, Displays, Charlottesville, VA 22903, USA.
6
Department of Medicine, School of Medicine, University of Virginia, 1215 Lee Street, Charlottesville, VA 22908, USA.
7
Department of Computer Science, School of Engineering, University of Virginia, Engineer's Way, Charlottesville, VA 22903, USA.
8
Department of Medicine, School of Medicine, University of Virginia, 1215 Lee Street, Charlottesville, VA 22908, USA; Advanced Medical Predictive Devices, Diagnostics, Displays, Charlottesville, VA 22903, 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.

KEYWORDS:

Implementation science; Learning health system; Predictive analytics monitoring; Stakeholder driven design; Streaming design

PMID:
29724445
DOI:
10.1016/j.cnc.2018.02.009
[Indexed for MEDLINE]

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