Leveraging Artificial Intelligence for Clinical Study Matching: Key Threads for Interweaving Data Science and Implementation Science

TL;DR

The paper discusses how artificial intelligence can improve clinical trial matching by integrating user-centered design and implementation science.

Contribution

It introduces key implementation themes for AI-based clinical trial screening tools identified by target users.

Findings

User-centered design and transparency are crucial for AI clinical trial tools.

Implementation science frameworks should be integrated early in AI tool development.

Collaboration with users improves trust and adoption of AI tools.

Abstract

Artificial intelligence holds the potential to enhance the efficiency of clinical research. Yet, like all innovations, its impact is dependent upon target user uptake and adoption. As efforts to leverage artificial intelligence for clinical trial screening become more widespread, it is imperative that implementation science principles be incorporated in both the design and roll-out of user-facing tools. We present and discuss implementation themes considered to be highly relevant by target users of artificial intelligence–enabled clinical trial screening platforms. The identified themes range from design features that optimize usability to collaboration with tool designers to improve transparency and trust. These themes were generally mapped to domains of existing implementation science frameworks such as the Consolidated Framework for Implementation Research. Designers should consider incorporating an implementation science framework early in the development process to not only ensure a user-centered design but to inform how tools are integrated into existing clinical research workflows.

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