Real-Time Triage, Position, and Documentation (TriPoD) During Medical Response to Major Incidents: Protocol for an Action Research Study
Bibliographic record
Abstract
BACKGROUND: There is a need to address the implementation of technological innovation into emergency medical services to facilitate and improve information exchange between prehospital emergency care providers, command centers, and hospitals during major incidents to enable better allocation of resources and minimize loss of life. At present, there is a lack of technology supporting real-time information sharing in managing major incidents to optimize the use of resources available. OBJECTIVE: The aim of this protocol is to develop, design, and evaluate information technology innovations for use in medical response to major incidents. METHODS: This study has a qualitative action research design. This research approach is suitable for developing and changing practice in health care settings since it is cyclical in nature and involves development, evaluation, redevelopment, and replanning. The qualitative data collection will include workshops, structured meetings, semistructured interviews, questionnaires, observations, and focus group interviews. This study assesses the use of a digital solution for real-time information sharing by involving 3 groups of indented users: prehospital emergency care personnel, hospital personnel, and designated duty officers with experience and specific knowledge in managing major incidents. This study will explore end users' experiences and needs, and a digital solution for prehospital and hospital settings will be developed in collaboration with technology producers. RESULTS: The trial implementation and evaluation phase for this study is from April 2024 to May 2026. Interviews and questionnaires with end users were conducted during the planning phase. We have performed observations in connection with 2 major exercises in April 2024 and November 2024. The outcome of this analysis will form the basis for the design and development of a new information technology system. We aim to complete the observations in training sessions and exercises (phase 3) by September 2025, followed by modification of the technology solutions tested (phase 4) before dissemination in a scientific journal. CONCLUSIONS: This protocol includes several methods for data collection that will form the basis for the design and development process of a digital solution for real-time information sharing to support efficient management in major incidents based on the experiences and requirements of end users. The findings from this study will contribute to the limited research on users' perspectives and the development of digital solutions for real-time information during major incidents. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/57819.
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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 | 0.162 | 0.117 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.045 | 0.013 |
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".