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