What is the impact of primary care model type on specialist referral rates? A cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Several new primary care models have been implemented in Ontario, Canada over the past two decades. These practice models differ in team structure, physician remuneration, and group size. Few studies have examined the impact of these models on specialist referrals. We compared specialist referral rates amongst three primary care models: 1) Enhanced Fee-for-service, 2) Capitation- Non-Interdisciplinary (CAP-NI), 3) Capitation - Interdisciplinary (CAP-I). METHODS: We conducted a cross-sectional study using health administrative data from primary care practices in Ontario from April 1st, 2008 to March 31st, 2010. The analysis included all family physicians providing comprehensive care in one of the three models, had at least 100 patients, and did not have a prolonged absence (eight consecutive weeks). The primary outcome was referral rate (# of referrals to all medical specialties/1000 patients/year). A multivariable clustered Poisson regression analysis was used to compare referral rates between models while adjusting for provider (sex, years since graduation, foreign trained, time in current model) and patient (age, sex, income, rurality, health status) characteristics. RESULTS: Fee-for-service had a significantly lower adjusted referral rate (676, 95% CI: 666-687) than the CAP-NI (719, 95% confidence interval (CI): 705-734) and CAP-I (694, 95% CI: 681-707) models and the interdisciplinary CAP-I group had a 3.5% lower referral rate than the CAP-NI group (RR = 0.965, 95% CI: 0.943-0.987, p = 0.002). Female and Canadian-trained physicians referred more often, while female, older, sicker and urban patients were more likely to be referred. CONCLUSIONS: Primary care model is significantly associated with referral rate. On a study population level, these differences equate to 111,059 and 37,391 fewer referrals by fee-for-service versus CAP-NI and CAP-I, respectively - a difference of $22.3 million in initial referral appointment costs. Whether a lower rate of referral is more appropriate or not is not known and requires further investigation. Physician remuneration and team structure likely account for the differences; however, further investigation is also required to better understand whether other organizational factors associated with primary care model also impact referral.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".