The Electronic Trauma Health Record: Design and Usability of a Novel Tablet-Based Tool for Trauma Care and Injury Surveillance in Low Resource Settings
Bibliographic record
Abstract
BACKGROUND: Ninety percent of global trauma deaths occur in under-resourced or remote environments, with little or no capacity for injury surveillance. We hypothesized that emerging electronic and web-based technologies could enable design of a tablet-based application, the electronic Trauma Health Record (eTHR), used by front-line clinicians to inform trauma care and acquire injury surveillance data for injury control and health policy development. STUDY DESIGN: The study was conducted in 3 phases: 1. Design of an electronic application capable of supporting clinical care and injury surveillance; 2. Preliminary feasibility testing of eTHR in a low-resource, high-volume trauma center; and 3. Qualitative usability testing with 22 trauma clinicians from a spectrum of high- and low-resource and urban and remote settings including Vancouver General Hospital, Whitehorse General Hospital, British Columbia Mobile Medical Unit, and Groote Schuur Hospital in Cape Town, South Africa. RESULTS: The eTHR was designed with 3 key sections (admission note, operative note, discharge summary), and 3 key capabilities (clinical checklist creation, injury severity scoring, wireless data transfer to electronic registries). Clinician-driven registry data collection proved to be feasible, with some limitations, in a busy South African trauma center. In pilot testing at a level I trauma center in Cape Town, use of eTHR as a clinical tool allowed for creation of a real-time, self-populating trauma database. Usability assessments with traumatologists in various settings revealed the need for unique eTHR adaptations according to environments of intended use. In all settings, eTHR was found to be user-friendly and have ready appeal for frontline clinicians. CONCLUSIONS: The eTHR has potential to be used as an electronic medical record, guiding clinical care while providing data for injury surveillance, without significantly hindering hospital workflow in various health-care settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".