Usability and Feasibility of a Mobile Application for Real-Time Trauma Care Guidance: Considerations for User Adoption
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.055 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".