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Leveraging artificial intelligence for clinical study matching: Key threads for interweaving data science and implementation science.
Andrew James Goodwin;
Sara Ann Armstrong;
David Ptak;
Kenneth Catchopole;
Jihad S Obeid;
Paul M Heider
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. Identified themes range from design features that optimize usability to collaboration with tool designers to improve transparency and trust. These themes generally mapped to domains of existing implantation 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.