Application of Nudges to Design Clinical Decision Support Tools: Systematic Approach Guided by Implementation Science.

Background: Clinical decision support (CDS) is one strategy to increase evidence-based practices by clinicians. Despite its potential, CDS tools produce mixed results and are often disliked by clinicians. Principles from behavioral economics such as "nudges" may improve the effectiveness and clinician satisfaction of CDS tools. This paper outlines a pragmatic approach grounded in implementation science to identify and prioritize how to incorporate different types of nudges into CDS tools.

Objective: The purpose of this paper is to describe a systematic and pragmatic approach grounded in implementation science to identify and prioritize how best to incorporate different types of nudges into CDS tools. We provide a case example of how this systematic approach was applied to design a CDS tool to improve guideline-concordant prescribing of mineralocorticoid receptor antagonists for patients with heart failure and reduced ejection fraction.

Methods: We applied the Messenger, Incentives, Norms, Defaults, Salience, Priming, Affect, Commitments, and Ego nudge framework and the Practical, Robust Implementation and Sustainability Model implementation science framework to systematically and pragmatically identify and prioritize different types of nudges for CDS tools. To illustrate how these frameworks can be applied in a real-life scenario, we use a case example of a CDS tool to improve guideline-concordant prescribing for patients with heart failure. We describe a process of how these frameworks can be used pragmatically by clinicians and informaticists or more technical CDS builders to apply nudge theory to CDS tools.

Results: We defined four iterative steps guided by the Practical, Robust Implementation and Sustainability Model: (1) engage partners for user-centered design, (2) develop a shared understanding of the nudge types, (3) determine the overarching CDS format, and (4) brainstorm and prioritize nudge types to address each modifiable contextual issue. These steps are iterative and intended to be adapted to align with the local resources and needs of various clinical scenarios and settings. We provide illustrative examples of how this approach was applied to the case example, including who we engaged, details of nudge design decisions, and lessons learned.

Conclusions: We present a pragmatic approach to guide the selection and prioritization of nudges, informed by implementation science. This approach can be used to comprehensively and systematically consider key issues when designing CDS to optimize clinician satisfaction, effectiveness, equity, and sustainability while minimizing the potential for unintended consequences. This approach can be adapted and generalized to other health settings and clinical situations, advancing the goals of learning health systems to expedite the translation of evidence into practice.