From code to care: AI and human collaboration in crafting Veteran-centric health technology
Abstract: INTRODUCTION: The healthcare industry faces dual revolutions with the widespread adoption of mobile healthcare applications and the emergence of generative artificial intelligence (Gen AI). The Veterans Administration and other military healthcare providers particularly stand to benefit from these technologies given their unique challenges serving veterans. This case study explored how Gen AI might help bridge the historical gap between healthcare providers and software developers in creating more effective healthcare applications for veterans. MATERIALS AND METHODS: The study utilized Anthropic's Claude 3.5 Sonnet, a large language model, to assist in developing requirements for a hypothetical healthcare application, Annie Pro. The process included uploading relevant documentation into the AI's context window and conducting an unstructured interview with the AI over an approximately 6-hour period, generating 80 pages of conversational text and 23 multi-page artifacts. Eight software developers were consulted to provide informal qualitative feedback on the resulting 26-page requirements document. RESULTS: The Gen AI demonstrated utility in requirements gathering, technical specification development, project planning, user flow mapping, and interface design. The AI showed particular strength in rapidly incorporating new requirements and explaining technical concepts to nontechnical stakeholders. Software developers reviewing the final product universally praised its value as a starting point for development, although some expressed concern about overly prescriptive technical specifications. CONCLUSION: This study suggests that Gen AI can effectively support healthcare providers in developing software requirements. While the technology shows promise in improving provider-developer communication, careful attention must be paid to avoid false confidence and over-specification. Future studies should look to replicate these results across different healthcare contexts and with different AI models as the technology continues to evolve rapidly.