A Health Records Review of Outpatient Referrals from the Emergency Department
Bibliographic record
Abstract
OBJECTIVES: Many patients discharged home from the emergency department (ED) require urgent outpatient consultation with a specialty service. We sought to identify the best- and worst-performing services with regard to time to outpatient consultation, the proportion of patients lost to follow-up, the rate of related return ED visits prior to consultation, and common strategies used by our top-performing clinics. METHODS: We conducted a health records review of The Ottawa Hospital ED visits during four 1-week periods. All consecutive adult outpatient consultation requests were included for chart review and were followed up to 12 months. Outcome measures included demographics, referral attendance rates, incomplete referrals, return ED visits, and time intervals. Services with at least 15 consultation requests were included for data analysis and qualitative mapping of their referral processes. RESULTS: Of the 963 patients who met inclusion criteria, 803 (83.4%) attended their appointment, while 160 (16.6%) were lost to follow-up. The overall median time to successful consultation was 9 days (IQR = 2-27). 92 (9.6%) patients returned to the ED with a related complaint. The top-performing clinics included ophthalmology, orthopedics, and thrombosis (median = 1, 8, 1 days; incomplete consultation = 3%, 4%, 6%; return ED visits = 0%, 6%, 2% respectively). The bottom-performing clinics included otorhinolaryngology, neurology, and gynecology (median = 47, 39, 27 days; incomplete consultation = 50%, 41%, 37%; return ED visits = 11%, 15%, 26%, respectively). Processes incorporated by top-performing clinics included reserving appointment slots for emergency referrals, structured referral forms, and centralized booking. CONCLUSIONS: We found a substantial variability in both the waiting times and reliability of outpatient referrals from the ED. Top-performing clinics incorporate common referral processes.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".