Nudging within learning health systems: next generation decision support to improve cardiovascular care. (9th February 2022)

• Record Type: Journal Article
• Authors: Chen, Yang; Harris, Steve; Rogers, Yvonne; Ahmad, Tariq; Asselbergs, Folkert W.
• Is Part Of: European heart journal. Volume 43:Number 13(2022)
• Page Start: 1296
• Page End: 1306
• Publication Date: 2022-02-09
• Subjects: Nudge -- Nudge theory -- Clinical decision support system -- Electronic health record -- Learning health system; Cardiology -- Periodicals; Heart -- Diseases -- Periodicals; 616.12005
• DOI: 10.1093/eurheartj/ehac030
• Languages: English
• ISSNs: 0195-668X

Abstract

Graphical Abstract: Graphical Abstract Schematic of a future learning health system with nudge, clinical decision support systems (CDSS), and artificial intelligence-powered analyses forming core parts of the learning cycle. Nudges within the CDSS can be used to either promote better adherence of guideline-directed therapy or test new hypotheses whenever there is clinical equipoise, identified through variation in clinical practice owing to the lack of robust evidence in that context. Although a simple A vs. B randomized controlled trial choice has been presented (fluid restriction vs. standard of care), multiple arms and an adaptive design could be built. Clinical outcomes could include patient-reported outcome measures. The presentation of nudges can be adjusted as new clinical insights are generated either within the local system or externally through data linkage. ACEi, angiotensin-converting enzyme inhibitor; GDT, guideline-directed therapy. Abstract: The increasing volume and richness of healthcare data collected during routine clinical practice have not yet translated into significant numbers of actionable insights that have systematically improved patient outcomes. An evidence-practice gap continues to exist in healthcare. We contest that this gap can be reduced by assessing the use of nudge theory as part of clinical decision support systems (CDSS). Deploying nudges to modify clinician behaviour and improve adherence to guideline-directed therapy represents an underused tool in bridging the evidence-practice gap. In conjunction with electronic health records (EHRs) and newer devices including artificial intelligence algorithms that are increasingly integrated within learning health systems, nudges such as CDSS alerts should be iteratively tested for all stakeholders involved in health decision-making: clinicians, researchers, and patients alike. Not only could they improve the implementation of known evidence, but the true value of nudging could lie in areas where traditional randomized controlled trials are lacking, and where clinical equipoise and variation dominate. The opportunity to test CDSS nudge alerts and their ability to standardize behaviour in the face of uncertainty may generate novel insights and improve patient outcomes in areas of clinical practice currently without a robust evidence base.