Interfacility transport of trauma patients : the broken telephone
Bibliographic record
Abstract
Background: Interfacility transport (IFT) of trauma patients in British Columbia (BC) is hindered by fragmented systems of data-sharing and communication. These challenges can contribute to increased patient morbidity and mortality, poor resource allocation, and increased healthcare costs. Objective: This thesis examines the challenges to data-sharing and communication affecting IFT in BC using a systems-based approach. We also evaluate T6 - a digital platform designed for trauma care - and its potential to support these challenges through a proof-of-concept pilot study. Methods: 1) A process map (PM) of IFT of trauma patients in BC was developed and validated with subject matter experts (SMEs) from involved organizations. The PM was used to facilitate semi-structured interviews focused on identifying key communication and data-sharing issues. Thematic analysis was conducted to extract major challenges. 2) The identified challenges were translated into user requirements. Using these and the PM, we evaluated T6 as a potential tool to support improved data-sharing and communication. We then conducted a single-center, observational, prospective cohort study to assess the feasibility and completeness when using T6 to document and collect data during trauma activations at Vancouver General Hospital. Results: 1) Seventeen interviews were conducted, with representation for all major organizations. The PM was validated as accurate. Thematic analysis revealed eight key challenges: the transport call process, redundant communication systems, lack of resource situational awareness, out-of-hospital to hospital communication, limited transport resources, interoperability, lack of data for performance analysis, and repatriation delays. 2) Pilot testing of T6 demonstrated that it was feasible to use at our centre and achieved comparable data capture to existing paper record. Conclusion: IFT of trauma patients in BC relies on coordination across multiple organizations. We successfully developed and validated a PM that brings transparency to this complex system and supports a whole-system approach to process improvement. This process identified critical challenges in communication and data-sharing. Early testing of T6 suggests it may address several of these gaps, supporting its potential as an innovative tool in trauma care.
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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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".