A framework for the oversight and local deployment of safe and high-quality prediction models.

Journal Article (Journal Article)

Artificial intelligence/machine learning models are being rapidly developed and used in clinical practice. However, many models are deployed without a clear understanding of clinical or operational impact and frequently lack monitoring plans that can detect potential safety signals. There is a lack of consensus in establishing governance to deploy, pilot, and monitor algorithms within operational healthcare delivery workflows. Here, we describe a governance framework that combines current regulatory best practices and lifecycle management of predictive models being used for clinical care. Since January 2021, we have successfully added models to our governance portfolio and are currently managing 52 models.

Full Text

Duke Authors

Cited Authors

  • Bedoya, AD; Economou-Zavlanos, NJ; Goldstein, BA; Young, A; Jelovsek, JE; O'Brien, C; Parrish, AB; Elengold, S; Lytle, K; Balu, S; Huang, E; Poon, EG; Pencina, MJ

Published Date

  • August 16, 2022

Published In

Volume / Issue

  • 29 / 9

Start / End Page

  • 1631 - 1636

PubMed ID

  • 35641123

Pubmed Central ID

  • PMC9382367

Electronic International Standard Serial Number (EISSN)

  • 1527-974X

Digital Object Identifier (DOI)

  • 10.1093/jamia/ocac078


  • eng

Conference Location

  • England