Abstract

BackgroundStudies that examine the adoption of clinical decision support (CDS) by healthcare providers have generally lacked a theoretical underpinning. The Unified Theory of Acceptance and Use of Technology (UTAUT) model may provide such a theory-based explanation; however, it is unknown if the model can be applied to the CDS literature.ObjectiveOur overall goal was to develop a taxonomy based on UTAUT constructs that could reliably characterize CDS interventions.MethodsWe used a two-step process: (1) identified randomized controlled trials meeting comparative effectiveness criteria, e.g., evaluating the impact of CDS interventions with and without specific features or implementation strategies; (2) iteratively developed and validated a taxonomy for characterizing differential CDS features or implementation strategies using three raters.ResultsTwenty-five studies with 48 comparison arms were identified. We applied three constructs from the UTAUT model and added motivational control to characterize CDS interventions. Inter-rater reliability was as follows for model constructs: performance expectancy (κ = 0.79), effort expectancy (κ = 0.85), social influence (κ = 0.71), and motivational control (κ = 0.87).ConclusionWe found that constructs from the UTAUT model and motivational control can reliably characterize features and associated implementation strategies. Our next step is to examine the quantitative relationships between constructs and CDS adoption.

Highlights

  • Studies that examine the adoption of clinical decision support (CDS) by healthcare providers have generally lacked a theoretical underpinning

  • We found that constructs from the UTAUT model and motivational control can reliably characterize features and associated implementation strategies

  • Our goal is to explore the feasibility of using the UTAUT model to estimate the underlying mechanisms that might be manipulated in a head-to-head comparison of two forms of a CDS

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Summary

Introduction

Studies that examine the adoption of clinical decision support (CDS) by healthcare providers have generally lacked a theoretical underpinning. CDS has been defined as the provision of patient-specific knowledge, information, and recommendations to clinicians in order to support optimal healthcare decisions [3]. Some CDS features found to have a positive impact include automatic data gathering and presentation, triggering presentation during decision making, and the provision of actionable recommendations [5]. While these systematic reviews do provide insight regarding particular features that may help CDS adoption, they don’t directly address the theoretical mechanisms of action.

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