Primary care physician referral patterns in Ontario, Canada: a descriptive analysis of self-reported referral data
Bibliographic record
Abstract
BACKGROUND: In many countries, the referral-consultation process faces a number of challenges from inefficiencies and rising demand, resulting in excessive wait times for many specialties. We collected referral data from a sample of family doctors across the province of Ontario, Canada as part of a larger program of research. The purpose of this study is to describe referral patterns from primary care to specialist and allied health services from the primary care perspective. METHODS: We conducted a prospective study of patient referral data submitted by primary care providers (PCP) from 20 clinics across Ontario between June 2014 and January 2016. Monthly referral volumes expressed as a total number of referrals to all medical and allied health professionals per month. For each referral, we also collected data on the specialty type, reason for referral, and whether the referral was for a procedure. RESULTS: PCPs submitted a median of 26 referrals per month (interquartile range 11.5 to 31.8). Of 9509 referrals eligible for analysis, 97.8% were directed to medical professionals and 2.2% to allied health professionals. 55% of medical referrals were directed to non-surgical specialties and 44.8% to surgical specialties. Medical referrals were for procedures in 30.8% of cases and non-procedural in 40.9%. Gastroenterology received the largest share (11.2%) of medical referrals, of which 62.3% were for colonoscopies. Psychology received the largest share (28.3%) of referrals to allied health professionals. CONCLUSION: We described patterns of patient referral from primary care to specialist and allied health services for 30 PCPs in 20 clinics across Ontario. Gastroenterology received the largest share of referrals, nearly two-thirds of which were for colonoscopies. Future studies should explore the use of virtual care to help manage non-procedural referrals and examine the impact that procedural referrals have on wait times for gastroenterology.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".