A Better Way: Initial Acceptability Testing of Using Artificial Intelligence Tools to Accelerate Development of Trauma Clinical Guidance
Notice bibliographique
Résumé
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 (PRISM) framework, the investigators drafted a 12-step approach to clinical guidance development in 24 weeks. The process starts with the selection of 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 recommendations, and 3) user-friendly features (offline and multilingual access). On a post conference survey (n=56), 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 enhancement include promoting collaboration across multiple disciplines and organizations, developing recommendations that consider resource variations, and utilizing 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. Key Messages What is already known on this topic Current clinical guidance typically takes 1-2 years to develop. Moreover, clinical guidance may not be published until a year or more after its completion, long after some recommendations become outdated, contributing to lagged evidence-informed practice. What this study adds This study shares and tests the initial acceptability of a novel approach that aims to reduce the current clinical guidance timeline from 24 months to 24 weeks. It leverages existing artificial intelligence tools but with the critical input of subject matter experts (SMEs), ensuring ethical standards and collaborative design. SMEs shed light on critical steps and key areas that future clinical guidance needs to consider. How this study might affect research, practice or policy The current landscape of clinical guidance offers significant opportunities for improvement. Key areas for enhancement include promoting collaboration across multiple disciplines and organizations, developing recommendations that consider resource variations, and utilizing new technologies, such as artificial intelligence, to expedite the development process.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,118 | 0,297 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,005 | 0,003 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».