The number needed to benefit: estimating the value of predictive analytics in healthcare. (13th June 2019)

• Record Type: Journal Article
• Authors: Liu, Vincent X; Bates, David W; Wiens, Jenna; Shah, Nigam H
• Is Part Of: Journal of the American Medical Informatics Association. Volume 26:Number 12(2019)
• Page Start: 1655
• Page End: 1659
• Publication Date: 2019-06-13
• Subjects: predictive analytics -- EHR -- implementation science -- cost-benefit analysis; 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/ocz088
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Predictive analytics in health care has generated increasing enthusiasm recently, as reflected in a rapidly growing body of predictive models reported in literature and in real-time embedded models using electronic health record data. However, estimating the benefit of applying any single model to a specific clinical problem remains challenging today. Developing a shared framework for estimating model value is therefore critical to facilitate the effective, safe, and sustainable use of predictive tools into the future. We highlight key concepts within the prediction-action dyad that together are expected to impact model benefit. These include factors relevant to model prediction (including the number needed to screen) as well as those relevant to the subsequent action (number needed to treat). In the simplest terms, a number needed to benefit contextualizes the numbers needed to screen and treat, offering an opportunity to estimate the value of a clinical predictive model in action.