Implementation of a prehospital-initiated redirection program for patients with low-acuity conditions: an 18-month retrospective cohort study
Bibliographic record
Abstract
Abstract Objectives: This study aims to describe the profile of patients and the barriers encountered in implementing an emergency medical services (EMS)-initiated redirection program to non-emergency department (ED) settings during the COVID-19 pandemic. Methods: This is a retrospective cohort study. All EMS interventions during which the coordinating redirection centre (Capitale-Nationale, Quebec, Canada) was called were reviewed. The centre was available weekdays during working hours. On a voluntary basis, the on-site emergency medical technician (EMT) had the opportunity to contact the redirection centre to initiate diversion of patients with non-urgent conditions to a non-ED setting. Results: Between April 27th, 2020, and October 26th, 2021, 2741 calls were received at the redirection centre, of which 1206 patients were finally diverted. Most redirected patients were female (62.0%) and 73.2% were aged ≥ 60 years old. The initial 911 Medical Dispatch Priority Systems (MDPS) codes of those redirected were sick person (37.3%) and falls (11.3%) while 58.9% were initially considered as low ambulance priority level (P4, P7). The main complaints of redirected patients were non-traumatic lower limb pain (17.8%), back pain (12.0%), neurologic disorder (8.3%), mental-health related condition (7.9%), skin disorder (6.7%) and fall (6.5%). Patients were most frequently redirected to a general practice clinic (45.1%), a community-based resource (20.1%) or their own family physician (11.9%). The main reasons for not redirecting, available for 1112 interventions, were the potential for deterioration (88.8%), patient not consenting to be redirected (7.2%) and no outpatient clinic availability (4.9%). Conclusions: A redirection program initiated by EMTs was implemented in the context of the COVID-19 pandemic. Older adults and those presenting with lower limb and back pain were the main populations redirected. Promoting the program to paramedics and leveraging this opportunity to compare different models of care are the upcoming steps.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".