Implementation of a strategy to manage EMS COVID-19 patient disposition with physician consultation: A retrospective descriptive study
Bibliographic record
Abstract
Objective: During the COVID-19 pandemic, there was concern that those accessing the 9-1-1 system could overwhelm hospitals. To alleviate this strain, emergency medical services (EMS) in Ontario, Canada, implemented a medical directive allowing paramedics to assess, treat and discharge low acuity patients with suspected COVID-19. Consultation with an EMS physician was required to ensure patient safety. The goal of the study was to measure compliance with and safety of this strategy. Methods: In this retrospective study, we assessed applications of the directive within the catchment of the Sunnybrook Centre for Prehospital Medicine from 1 January 2021, through 31 March 2022. We also monitored whether non-transported patients accessed the 9-1-1 system again within 7 days. Results: Overall, 122 patients were considered by paramedics to have met the COVID-19 medical directive, and, therefore, an EMS physician was contacted. Of these, 53.7% (65/122) were female, and mean age (SD) was 46.8 years (SD 17), range 5 to 96 years. In only 20% (25/122) of cases were all patient assessment findings reported to the EMS physician and within the appropriate ranges specified in the medical directive. The EMS physician approved non-transport in 83% (101/122) of cases. Of those not transported, 12% (12/99) accessed 9-1-1 again within 7 days. Conclusions: We present a non-transport option for patient management, a novel concept within Ontario's paramedic regulatory framework. Assessments provided by paramedics included telephone consultation with EMS physicians. There were no instances in which patient safety was known to have been impacted. Needed is further study of non-transport strategies for low acuity patients that ensure patient safety and study of training strategies to enhance compliance with decision-making tools and joint decision making between paramedics and physicians regarding patient disposition.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".