ArticleTrauma surgery & acute care open2025
Better way: initial acceptability testing of using artificial intelligence tools to accelerate development of trauma clinical guidance.
Article in Trauma surgery & acute care open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Application of Evidence into Practice in Trauma: The Purpose to Practice (P2P) Approach to Consensus Generation.medRxiv : the preprint server for health sciences · 2025Article
- Application of evidence into practice in trauma: the Purpose-to-Practice (P2P) approach to consensus generation.Trauma surgery & acute care open · 2025Article
Corrections and comments
- Update of
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: 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 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.