Access to Specialist Gastroenterology Care in Canada: Comparison of Wait Times and Consensus Targets
Bibliographic record
Abstract
BACKGROUND: Monitoring wait times and defining targets for care have been advocated to improve health care delivery related to cancer, heart, diagnostic imaging, joint replacements and sight restoration. There are few data on access to care for digestive diseases, although they pose a greater economic burden than cancer or heart disease in Canada. The present study compared wait times for specialist gastroenterology care with recent, evidence-based, consensus-defined benchmark wait times for a range of digestive diseases. METHODS: Total wait times from primary care referral to investigation were measured for seven digestive disease indications by using the Practice Audit in Gastroenterology program, and were benchmarked against consensus recommendations. RESULTS: Total wait times for 1903 patients who were undergoing investigation exceeded targets for those with probable cancer (median 26 days [25th to 75th percentiles eight to 56 days] versus target of two weeks); probable inflammatory bowel disease (101 days [35 to 209 days] versus two weeks); documented iron deficiency anemia (71 days [19 to 142 days] versus two months); positive fecal occult blood test (73 days [36 to 148 days] versus two months); dyspepsia with alarm symptoms (60 days [23 to 140 days] versus two months); refractory dyspepsia without alarm symptoms (126 days [42 to 225 days] versus two months); and chronic constipation and diarrhea (141 days [68 to 264 days] versus two months). A minority of patients were seen within target times: probable cancer (33% [95% CI 20% to 47%]); probable inflammatory bowel disease (12% [95% CI 1% to 23%]); iron deficiency anemia (46% [95% CI 37% to 55%]); positive occult blood test (41% [95% CI 28% to 54%]); dyspepsia with alarm symptoms (51% [95% CI 41% to 60%]); refractory dyspepsia without alarm symptoms (33% [95% CI 19% to 47%]); and chronic constipation and diarrhea (21% [95% CI 14% to 29%]). DISCUSSION: Total wait times for the seven indications exceeded the consensus targets; 51% to 88% of patients were not seen within the target wait time. Multiple interventions, including adoption of evidence-based management guidelines and provision of economic and human resources, are needed to ensure appropriate access to digestive health care in Canada. Outcomes can be evaluated by the 'point-of-care', practice audit methodology used for the present study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".