Waiting to see the specialist: patient and provider characteristics of wait times from primary to specialty care
Bibliographic record
Abstract
BACKGROUND: Wait times are an important measure of access to various health care sectors and from a patient's perspective include several stages in their care. While mechanisms to improve wait times from specialty care have been developed across Canada, little is known about wait times from primary to specialty care. Our objectives were to calculate the wait times from when a referral is made by a family physician (FP) to when a patient sees a specialist physician and examine patient and provider factors related to these wait times. METHODS: Our study used the Electronic Medical Record Administrative data Linked Database (EMRALD) which is a linkage of FP electronic medical record (EMR) data to the Ontario, Canada administrative data. The EMR referral date was linked to the administrative physician claims date to calculate the wait times. Patient age, sex, socioeconomic status, comorbidity and FP continuity of care and physician age, sex, practice location, practice size and participation in a primary care delivery model were examined with respect to wait times. RESULTS: The median waits from medical specialists ranged from 39 to 76 days and for surgical specialists from 33 days to 66 days. With a few exceptions, patient factors were not associated with wait times from primary care to specialty care. Similarly physician factors were not consistently associated with wait times, except for FP practice location and size. CONCLUSIONS: Actual wait times for a referral from a FP to seeing a specialist physician are longer than those reported by physician surveys. Wait times from primary to specialty care need to be included in the calculation of surgical and diagnostic wait time benchmarks in Canada.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".