A22 ROUTINE GASTROINTESTINAL REFERRAL WAIT LIST REDUCTION VIA AN ENHANCED PRIMARY CARE PATHWAY
Bibliographic record
Abstract
High referral volumes to gastroenterologists in Canada highlight the ubiquity of gastrointestinal (GI) disorders. Yet, due to demand-supply mismatch, wait times for specialist consultation continue to grow, often exceeding consensus targets. Within the Calgary Health Zone, a single point of entry model is used to centralize GI referral intake (GI-CAT). Recently, a set of Enhanced Primary Care Pathways (EPCPs)—a collection of best practice evidence-based guidelines—were co-developed to identify certain low-risk GI referrals that may be best managed within the primary care medical home. These guidelines encompass common GI conditions, including gastroesophageal reflux disease, dyspepsia, irritable bowel syndrome, chronic constipation, and resistant H. pylori infection. We have previously demonstrated the interim safety of this triage strategy. In this study, we evaluated the effect of EPCP implementation on existing clinic wait list volumes. All referrals to GI CAT from October 2016–September 2018 were captured. Real-time monthly wait lists were generated for patients triaged to non-urgent and routine clinic visits. EPCP criteria were applied to close both new referrals and existing wait-listed routine clinic referrals over this time period. Wait list volumes were compared pre- and post-EPCP implementation. During the 24-month study period, a total of 41,774 unique referrals to GI CAT were captured, with an average monthly referral volume of 1816 (± 123) cases. A total of 1911 new referrals were closed using EPCPs, averaging 87 (± 22) cases per month. EPCP criteria were also applied to close existing referrals. At the start of the study period, 2000 patients were waiting for routine clinic consultation. Within the first 12 months of EPCP implementation, routine wait list volume was reduced by an average of 165 cases per month, to a total of 24 remaining cases (99% reduction). This effect was maintained during the subsequent 12-month interval, representing a significant wait list reduction (ANOVA, p = 0.002). During the same period, the non-urgent wait list marginally increased, from 1654 (±103) to 1868 (±78) cases (ANOVA, p < 0.001). Total referral volumes remained unchanged (ANOVA, p = 0.107). Not all patients referred for GI consultation require specialist care. Through careful patient selection, an EPCP provides a means to identify low-risk patients who are more appropriately managed within a primary care medical home. In Calgary, this process has successfully been used to drastically reduce the numbers of waitlisted patients, allowing for the redirection of strained GI consultative resources to more high-risk patients. Routine and non-urgent clinic monthly wait list volumes and total monthly referrals during the 24-month study period after implementation of EPCP criteria None
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".