Emergency department interventions that could be conducted in subacute care settings for patients with nonemergent conditions transported by paramedics: a modified Delphi study
Bibliographic record
Abstract
BACKGROUND: As the number of patients with nonemergent conditions who are transported by paramedics continues to increase in Ontario, redirecting specific patients to subacute settings may be more beneficial and suitable for both patients and emergency departments. We aimed to evaluate whether emergency department interventions conducted on patients with nonemergent conditions who are transported by paramedics could be conducted in subacute health centres. METHODS: We conducted a RAND/UCLA modified Delphi study in Ontario between Oct. 13 and Dec. 19, 2020. We used purposive sampling to recruit practising emergency and primary care physicians for an expert panel. We abstracted interventions given to adult patients with nonemergent conditions (18 yr of age or older) who were transported by paramedics to an emergency department from the National Ambulatory Care Reporting System (NACRS) database (Jan. 1, 2014, to Mar. 31, 2018). Participants in the expert panel rated the suitability of the 150 most frequently recorded emergency department interventions from the NACRS database, for completion in subacute health care centres. We set consensus at 70% agreement. RESULTS: We invited 25 physician experts, 21 of whom consented to participate; 20 physicians completed round 1, and 18 physicians completed both rounds. After 2 rounds, consensus was reached on 146 (97.3%) interventions; 103 interventions (68.7%) were suitable for subacute centres, 43 (28.7%) for only the emergency department and 4 (2.6%) did not receive consensus. For subacute centres, all 103 interventions were rated for urgent care centres; walk-in medical centres were applicable for 46 (30.6%) interventions and clinics led by nurse practitioners for 47 (31.3%) interventions. INTERPRETATION: Most interventions provided to patients with nonemergent conditions transported by paramedics to emergency departments were identified as suitable for urgent care clinics, with one-third being suitable for either walk-in medical centres or clinics led by nurse practitioners. This study has potential to inform a patient classification model for paramedic-initiated redirection of patients from emergency departments, although further contextualization is required for this to be implemented in clinical practice. STUDY REGISTRATION: ID ISRCTN22901977.
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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 | 0.054 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 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".