Wait times to rheumatology care for patients with rheumatic diseases: a data linkage study of primary care electronic medical records and administrative data
Bibliographic record
Abstract
BACKGROUND: The Wait Time Alliance recently established wait time benchmarks for rheumatology consultations in Canada. Our aim was to quantify wait times to primary and rheumatology care for patients with rheumatic diseases. METHODS: We identified patients from primary care practices in the Electronic Medical Record Administrative data Linked Database who had referrals to Ontario rheumatologists over the period 2000-2013. To assess the full care pathway, we identified dates of symptom onset, presentation in primary care and referral from electronic medical records. Dates of rheumatologist consultations were obtained by linking with physician service claims. We determined the duration of each phase of the care pathway (symptom onset to primary care encounter, primary care encounter to referral, and referral to rheumatologist consultation) and compared them with established benchmarks. RESULTS: Among 2430 referrals from 168 family physicians, 2015 patients (82.9%) were seen by 146 rheumatologists within 1 year of referral. Of the 2430 referrals, 2417 (99.5%) occurred between 2005 and 2013. The main reasons for referral were osteoarthritis (32.4%) and systemic inflammatory rheumatic diseases (30.6%). Wait times varied by diagnosis and geographic region. Overall, the median wait time from referral to rheumatologist consultation was 74 (interquartile range 27-101) days; it was 66 (interquartile range 18-84) days for systemic inflammatory rheumatic diseases. Wait time benchmarks were not achieved, even for the most urgent types of referral. For systemic inflammatory rheumatic diseases, most of the delays occurred before referral. INTERPRETATION: Rheumatology wait times exceeded established benchmarks. Targeted efforts are needed to promote more timely access to both primary and rheumatology care. Routine linkage of electronic medical records with administrative data may help fill important gaps in knowledge about waits to primary and specialty care.
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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 | 0.020 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| 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".