Better way: initial acceptability testing of using artificial intelligence tools to accelerate development of trauma clinical guidance
Gabriela Zavala Wong, Shannon Rosenauer, Chelsea Church, Diana Sherifali, Megan Racey, Katheryn Grider, Ashley N Moreno, Lacey LaGroneIntroduction
Representatives of the trauma community have voiced a need for a new approach to developing clinical guidance. In this study, we test the initial acceptability of a proposed 12-step approach that aims to reduce the current clinical guidance timeline from more than 24 months to 24 weeks.
Methods
Investigators hypothesized that artificial intelligence (AI) tools could be leveraged to improve and make the process of clinical guidance development more efficient, facilitating AI initial output that could later be reviewed by subject matter experts (SMEs), ensuring ethical standards and a collaborative design. Following the agile methodology, emphasizing continuous delivery and improvement, and the Practical, Robust Implementation and Sustainability Model framework, the investigators drafted a 12-step approach to clinical guidance development in 24 weeks. The process begins with selecting a clinical topic and culminates in a bedside-ready clinical decision tree.
Results
The 2025 Design for Implementation: The Future of Trauma Research & Clinical Guidance conference participants were invited to reflect on this new 12-step approach during two breakout sessions. Participants included a broad range of trauma providers, methodologists, patient representatives, technology, and marketing experts. Their recommendations highlighted: (1) multidisciplinary involvement, (2) need for resource-stratified guidance, and (3) user-friendly features (offline and multilingual access). On a postconference survey (n=56 total respondents), 64% were confident in AI accelerating the current development process.
Conclusions
The current landscape of clinical guidance offers significant opportunities for improvement. Key areas for improvement include promoting collaboration across multiple disciplines and organizations, developing recommendations that consider resource variations, and leveraging new technologies, such as AI, to expedite the development process. This is crucial because ongoing delays lead to practices lagging behind current evidence. Further research is needed to rigorously test and refine how responsible use of AI can be integrated into expediting evidence integration into clinical guidance.