Patterns of referral in a Canadian primary care electronic healthrecord database: retrospective cross-sectional analysis
Bibliographic record
Abstract
BACKGROUND: Databases derived from primary care electronic health records (EHRs) are ideally suited to study clinical influences on referral patterns. This is the first study outside the United Kingdom to use an EHR database to describe rates of referral per patient from family physicians to specialists. OBJECTIVE: To use a primary care EHR database to describe referrals to specialist physicians; to partition variance in referral rates between the practice and patient levels. METHODS: Retrospective cross-sectional analysis of de-identified EHRs of 33 998 patients from 10 primary care practices in Ontario, Canada. The study cohort included all patients who visited their family physician 1 April 2007 to 31 March 2008 (n ≥ 24856). Specialist referrals for each patient were counted for 12 months following their index visit. Rates of referral were compared by sex, age, number of office visits, practice location and specialist type using t-tests or Pearson's correlation. Variance partitioning determined the proportion of variance in the overall referral rate accounted for by the practice and patient levels. RESULTS: In total, 7771 patients (31.3%) had one or more referrals. The overall referral rate was 455/1000 patients/year (95% CI, 444-465). Rates were higher for females, older patients and rural practices. The referral rate correlated with the number of family physician office visits. Ninety-two percent of the total variance in referral rates was attributable to the patient (vs. practice) level. CONCLUSIONS: A Canadian primary care EHR database showed similar patterns of referral to those reported from administrative databases. Most variance in referral rates is explained at the patient level.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".