Performance measures of the medical priority dispatch system in an urban basic life support system
Bibliographic record
Abstract
BACKGROUND: Accurate dispatch prioritization for emergency medical services (EMS) is essential for optimizing resource allocation and ensuring timely emergency response. In the Province of Quebec, Canada, a locally adapted dispatch system was implemented using the standardized codes of the Medical Priority Dispatch System (MPDS) but with regional priority definitions. Despite periodic reviews, the system's performance has not been formally assessed. This study evaluates the effectiveness of this prioritization system by comparing priority levels assigned at call-taking with on-scene paramedic assessments and by examining how the system's performance has evolved over three years and across chief complaints. METHODS: In this retrospective observational study, we analyzed EMS dispatches in the Capitale-Nationale administrative region of the Province of Quebec, Canada, between July 15 and December 15 over three consecutive years (2021, 2022, and 2023). We assessed system performance using sensitivity, specificity, overtriage, undertriage, predictive values, and accuracy. Statistical analyses included chi-square tests for priority consistency and pairwise t-tests for performance changes over time. Additionally, we examined variations across chief complaints to identify high overtriage and undertriage medical conditions. RESULTS: This study analyzed 96,099 EMS dispatches over a three-year period. While 61.8% of these dispatches were classified as urgent at call-taking, paramedics later determined that 79.7% of all cases were stable and required non-urgent transport, indicating a high level of overtriage. Conditions such as abdominal pain, falls, and psychiatric issues were the chief complaints that showed high overtriage rates (> 90%), whereas allergic reactions, diabetic problems, and heart conditions had the highest undertriage rates (> 10%). Over the three-year period, priority modifications led to a 2.5% decrease in undertriage but a 3.7% increase in overtriage (p < 0.05), highlighting the ongoing challenge of balancing accuracy with an adequate response in dispatch prioritization. CONCLUSION: The studied prioritization system effectively identifies non-urgent dispatches but exhibits a high overtriage rate, which strains EMS resources. The recent priority modifications further increased overtriage, underscoring the challenge of balancing resource allocation with timely intervention. Refining dispatch criteria and integrating secondary triage or AI-based decision support could potentially improve accuracy and system efficiency.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".