Evidence map›Paper›PMID 42370061›Full record

ArticleTrauma surgery & acute care open2025

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 LaGrone, 2025 Design for Implementation (DFI) Authorship Group

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Gabriela Zavala WongUCHealth Medical Center of the Rockies, Loveland, Colorado, USA.ORCID https://orcid.org/0000-0003-3337-639X
Shannon RosenauerBoston Consulting Group (BCG), Denver, Colorado, USA.
Chelsea ChurchBoston Consulting Group (BCG), Denver, Colorado, USA.
Diana SherifaliMcMaster University, Hamilton, Ontario, Canada.
Megan RaceyMcMaster University, Hamilton, Ontario, Canada.ORCID https://orcid.org/0000-0003-4636-0397
Katheryn GriderADT, Whitefish, Montana, USA.
Ashley N MorenoCoalition for National Trauma Research, San Antonio, Texas, USA.ORCID https://orcid.org/0000-0002-8881-7597
Lacey LaGroneUCHealth, Medical Center of the Rockies, Loveland, Colorado, USA.ORCID https://orcid.org/0000-0003-4749-969X
2025 Design for Implementation (DFI) Authorship Group

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

guideline

Identifiers

PMID42370061
PMCPMC13296030

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.