Comparing traditional and AI-enhanced strategies for developing patient decision aids: a multiple case study.

OBJECTIVES: Our objective was to develop and test prompts designed to generate balanced, evidence-based information from artificial intelligence (AI) for the development of patient decision aid (DA) content. We compared the outputs of this AI-enhanced strategy with those produced by an experienced human team using a traditional development approach. METHODS: We conducted a comparative, mixed-methods, multiple-case study, with each case being a DA. Eight DAs were randomly selected from the Ottawa Inventory, stratified by author type (commercial, academic, public institution, professional association). We then followed a systematic process involving two researchers working independently. One researcher described the topics of the selected DAs and extracted their content by listing the available options with their benefits and harms. The other researcher-blind to the DA-used the topic description to generate AI-enhanced DA content by iteratively refining the prompt structures based on the International Patient Decision Aids Standards until the generated content stabilised. Quantitative analyses compared the number of options, benefits and harms generated by the traditional and AI-enhanced strategies, while qualitative analyses examined the differences in content. RESULTS: The selected DAs targeted different populations (older adults, women, the general population, children) and were produced in Canada, the UK, the USA or Australia. One type of DA (n=6) focused on a specific option (eg, whether to get vaccinated against COVID-19), the other (n=2) focused on improving an outcome (eg, treating attention-deficit/hyperactivity disorder symptoms). For option-focused DAs, 66% of the benefits/harms were generated by the AI-enhanced strategy only and 6.2% by the traditional strategy only. For outcome-focused DAs, 47% of the options were generated by the AI-enhanced strategy only, and 4% by the traditional strategy only. An evidence search confirmed that the options generated only by the AI-enhanced strategy were indeed beneficial, ruling out hallucinations. However, the AI-enhanced strategy did not suggest optimal combinations. Qualitative analysis showed that AI-enhanced content was generally richer. CONCLUSIONS: This study provides practical guidance on leveraging AI to improve the efficiency of DA development and improve their quality.