Usability and Feasibility of a Mobile Application for Real-Time Trauma Care Guidance: Considerations for User Adoption
Notice bibliographique
Résumé
ABSTRACT Introduction Inadequate access to setting-relevant clinical guidance influences the implementation of evidence-based clinical practices in trauma care settings. This guidance is not optimally effective if it cannot be disseminated in settings where it is most needed, leading to substantial inequities in trauma care. To address this problem, this project developed a trauma clinical guidance repository and accompanying mobile application and sought to elicit concept feedback. Methods As part of year two of the Design for Implementation: The Future of Trauma Clinical Guidance and Research Conference Series, conference attendees participated in interactive breakout sessions to generate user feedback and beta-test the clinical guidance repository and mobile application that was created after the first annual conference. A mixed methods approach using interactive discussions and a post-conference survey was administered in-person and virtually to elicit feedback from a largely academic, urban audience. Results 56 post-conference survey responses were collected. Respondents provided detailed, in-depth feedback on the display and user features of the mobile application and gave input on what factors they would prioritize to maximize the tool’s adoption. Areas of positive feedback included the repository’s novel contribution as a clinical tool and its potential to aid clinicians in resource-constrained settings. Components of the tool that participants believed required further iteration included ensuring clear, concise language and making it more user-friendly to retrieve information during emergent situations efficiently. A prominent theme throughout the sessions and survey is the necessity of continuous opportunities for feedback from a wide range of stakeholders, both clinical and non-clinical. Discussion For novel information dissemination platforms to be effective, clinical guidance must be continuously updated and presented in a user-friendly, logical format that allows clinicians to find and integrate information into practice seamlessly. Conceptual feedback will contribute to a better understanding of clinician needs, further elucidating the opportunities to match technology with bedside utility. KEY POINTS Please include the key messages of your article after your abstract using the following headings. This section should be no more than 3-5 sentences and should be distinct from the abstract; be succinct, specific, and accurate. What is already known on this topic – Disparities in access to timely, resource-relevant, evidence-informed clinical guidance currently leads to inequitable outcomes in trauma care. What this study adds – Through interactive sessions, key partners provided input on the key factors that a novel clinical tool would need to address this gap in trauma care successfully. How this study might affect research, practice, or policy – This study further elucidates clinician needs, which will ideally inform ongoing innovation among potential technology partners, ensuring that resources are invested aligned with patient and clinician needs first and foremost.
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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 | 0,055 | 0,125 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».