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

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Keim-Malpass, J; Kitzmiller, RR; Skeeles-Worley, A; Lindberg, C; Clark, MT; Tai, R; Calland, JF; Sullivan, K; Randall Moorman, J; Anderson, RA

Published Date

  • June 2018

Published In

Volume / Issue

  • 30 / 2

Start / End Page

  • 273 - 287

PubMed ID

  • 29724445

Pubmed Central ID

  • 29724445

Electronic International Standard Serial Number (EISSN)

  • 1558-3481

International Standard Serial Number (ISSN)

  • 0899-5885

Digital Object Identifier (DOI)

  • 10.1016/j.cnc.2018.02.009

Language

  • eng