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

Jessica Keim-Malpass; Rebecca R Kitzmiller; Angela Skeeles-Worley; Curt Lindberg; Matthew T Clark; Robert Tai; James Forrest Calland; Kevin Sullivan; J Randall Moorman; Ruth A Anderson

doi:10.1016/j.cnc.2018.02.009

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

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

Affiliations

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.

PMID: 29724445
DOI: 10.1016/j.cnc.2018.02.009

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.

MeSH terms

Data Interpretation, Statistical*
Decision Support Systems, Clinical*
Evidence-Based Practice
Focus Groups
Humans
Intensive Care Units
Models, Statistical
Monitoring, Physiologic / statistics & numerical data
Monitoring, Physiologic / trends*