A framework for the oversight and local deployment of safe and high-quality prediction models. (31st May 2022)

• Record Type: Journal Article
• Authors: Bedoya, Armando D; Economou-Zavlanos, Nicoleta J; Goldstein, Benjamin A; Young, Allison; Jelovsek, J Eric; O'Brien, Cara; Parrish, Amanda B; Elengold, Scott; Lytle, Kay; Balu, Suresh; Huang, Erich; Poon, Eric G; Pencina, Michael J
• Is Part Of: Journal of the American Medical Informatics Association. Volume 29:Number 9(2022)
• Page Start: 1631
• Page End: 1636
• Publication Date: 2022-05-31
• Subjects: artificial intelligence -- machine learning -- decision support systems—clinical -- Governance Models—organizational -- Health Information Management/Organization & Administration; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocac078
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: 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.