A theory-based meta-regression of factors influencing clinical decision support adoption and implementation. (13th August 2021)

• Record Type: Journal Article
• Authors: Liu, Siru; Reese, Thomas J; Kawamoto, Kensaku; Del Fiol, Guilherme; Weir, Charlene
• Is Part Of: Journal of the American Medical Informatics Association. Volume 28:Number 11(2021)
• Page Start: 2514
• Page End: 2522
• Publication Date: 2021-08-13
• Subjects: clinical decision support -- unified theory of acceptance -- use of technology -- taxonomy -- meta-regression; 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/ocab160
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objective: The purpose of the study was to explore the theoretical underpinnings of effective clinical decision support (CDS) factors using the comparative effectiveness results. Materials and Methods: We leveraged search results from a previous systematic literature review and updated the search to screen articles published from January 2017 to January 2020. We included randomized controlled trials and cluster randomized controlled trials that compared a CDS intervention with and without specific factors. We used random effects meta-regression procedures to analyze clinician behavior for the aggregate effects. The theoretical model was the Unified Theory of Acceptance and Use of Technology (UTAUT) model with motivational control. Results: Thirty-four studies were included. The meta-regression models identified the importance of effort expectancy (estimated coefficient = −0.162; P = .0003); facilitating conditions (estimated coefficient = 0.094; P = .013); and performance expectancy with motivational control (estimated coefficient = 1.029; P = .022). Each of these factors created a significant impact on clinician behavior. The meta-regression model with the multivariate analysis explained a large amount of the heterogeneity across studies (R 2 = 88.32%). Discussion: Three positive factors were identified: low effort to use, low controllability, and providing more infrastructure and implementation strategies to support the CDS. The multivariate analysis suggests that passive CDS could be effective if users believe the CDS is useful and/or social expectations to use the CDS intervention exist. Conclusions: Overall, a modified UTAUT model that includes motivational control is an appropriate model to understand psychological factors associated with CDS effectiveness and to guide CDS design, implementation, and optimization.