Determinants of implementing artificial intelligence-based clinical decision support tools in healthcare: a scoping review protocol

Abstract

INTRODUCTION: Artificial intelligence (AI), the simulation of human intelligence processes by machines, is being increasingly leveraged to facilitate clinical decision-making. AI-based clinical decision support (CDS) tools can improve the quality of care and appropriate use of healthcare resources, and decrease healthcare provider burnout. Understanding the determinants of implementing AI-based CDS tools in healthcare delivery is vital to reap the benefits of these tools. The objective of this scoping review is to map and synthesise determinants (barriers and facilitators) to implementing AI-based CDS tools in healthcare. METHODS AND ANALYSIS: This scoping review will follow the Joanna Briggs Institute methodology and the Preferred Reporting Items for Systematic reviews and Meta-Analysis extension for Scoping Reviews checklist. The search terms will be tailored to each database, which includes MEDLINE, Embase, CINAHL, APA PsycINFO and the Cochrane Library. Grey literature and references of included studies will also be searched. The search will include studies published from database inception until 10 May 2022. We will not limit searches by study design or language. Studies that either report determinants or describe the implementation of AI-based CDS tools in clinical practice or/and healthcare settings will be included. The identified determinants (barriers and facilitators) will be described by synthesising the themes using the Theoretical Domains Framework. The outcome variables measured will be mapped and the measures of effectiveness will be summarised using descriptive statistics. ETHICS AND DISSEMINATION: Ethics approval is not required because all data for this study have been previously published. The findings of this review will be published in a peer-reviewed journal and presented at academic conferences. Importantly, the findings of this scoping review will be widely presented to decision-makers, health system administrators, healthcare providers, and patients and family/caregivers as part of an implementation study of an AI-based CDS for the treatment of coronary artery disease.

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