Leveraging Generative AI Tools to Support the Development of Digital Solutions in Health Care Research: Case Study (Preprint)

Abstract

<sec> <title>BACKGROUND</title> Generative artificial intelligence has the potential to revolutionize health technology product development by improving coding quality, efficiency, documentation, quality assessment and review, and troubleshooting. </sec> <sec> <title>OBJECTIVE</title> This paper explores the application of a commercially available generative artificial intelligence tool (ChatGPT) to the development of a digital health behavior change intervention designed to support patient engagement in a commercial digital diabetes prevention program. </sec> <sec> <title>METHODS</title> We examined the capacity, advantages, and limitations of ChatGPT to support digital product idea conceptualization, intervention content development, and the software engineering process, including software requirement generation, software design, and code production. In total, 11 evaluators, each with at least 10 years of experience in fields of study ranging from medicine and implementation science to computer science, participated in the output review process (ChatGPT vs human-generated output). All had familiarity or prior exposure to the original personalized automatic messaging system intervention. The evaluators rated the ChatGPT-produced outputs in terms of understandability, usability, novelty, relevance, completeness, and efficiency. </sec> <sec> <title>RESULTS</title> Most metrics received positive scores. We identified that ChatGPT can (1) support developers to achieve high-quality products faster and (2) facilitate nontechnical communication and system understanding between technical and nontechnical team members around the development goal of rapid and easy-to-build computational solutions for medical technologies. </sec> <sec> <title>CONCLUSIONS</title> ChatGPT can serve as a usable facilitator for researchers engaging in the software development life cycle, from product conceptualization to feature identification and user story development to code generation. </sec> <sec> <title>CLINICALTRIAL</title> ClinicalTrials.gov NCT04049500; https://clinicaltrials.gov/ct2/show/NCT04049500 </sec>

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