Risky business: a scoping review for communicating results of predictive models between providers and patients. Issue 4 (12th November 2021)

• Record Type: Journal Article
• Authors: Walsh, Colin G; McKillop, Mollie M; Lee, Patricia; Harris, Joyce W; Simpson, Christopher; Novak, Laurie Lovett
• Is Part Of: JAMIA open. Volume 4:Issue 4(2021)
• Publication Date: 2021-11-12
• Subjects: predictive analytics -- predictive algorithms -- patient communication -- risk communication -- shared decision-making; Medical informatics -- Periodicals; 610.285
• DOI: 10.1093/jamiaopen/ooab092
• Languages: English
• ISSNs: 2574-2531

Abstract

Abstract: Objective: Given widespread excitement around predictive analytics and the proliferation of machine learning algorithms that predict outcomes, a key next step is understanding how this information is—or should be—communicated with patients. Materials and Methods: We conducted a scoping review informed by PRISMA-ScR guidelines to identify current knowledge and gaps in this domain. Results: Ten studies met inclusion criteria for full text review. The following topics were represented in the studies, some of which involved more than 1 topic: disease prevention ( N = 5/10, 50%), treatment decisions ( N = 5/10, 50%), medication harms reduction ( N = 1/10, 10%), and presentation of cardiovascular risk information ( N = 5/10, 50%). A single study included 6- and 12-month clinical outcome metrics. Discussion: As predictive models are increasingly published, marketed by industry, and implemented, this paucity of relevant research poses important gaps. Published studies identified the importance of (1) identifying the most effective source of information for patient communications; (2) contextualizing risk information and associated design elements based on users' needs and problem areas; and (3) understanding potential impacts on risk factor modification and behavior change dependent on risk presentation. Conclusion: An opportunity remains for researchers and practitioners to share strategies for effective selection of predictive algorithms for clinical practice, approaches for educating clinicians and patients in effectively using predictive data, and new approaches for framing patient-provider communication in the era of artificial intelligence.