P.032 Accessing ambulatory care in neurology: understanding and addressing demand in Calgary
Bibliographic record
Abstract
Background: Accessible ambulatory neurology care can reduce the need for inpatient evaluation. Aligning patient demand (service requests) with provider and space resources can optimize ambulatory clinic flow. In response to increasing referral volumes and wait times for neurologist access, a quality improvement initiative was undertaken to address demand. Methods: Process mapping and root cause analysis demonstrated access challenges and referral processing errors. Audit of 968 accepted referrals revealed variation in triage processes and decisions for referral questions. Neurologists defined inclusion criteria to specialty programs, based on referral questions. Referral management transitioned to a central intake model, reducing intra- and inter-clinic triage variability. Guidelines were established to prevent triage duplication and standardize appointment management. The primary outcome was accepted referrals per month. Secondary outcomes were referral rejection rate and neurology wait times. Results: Significantly more referrals were received per month post intervention (987 vs. 859, p<0.000). The number of accepted referrals did not change (p=0.147). Referral rejection rate increased from 21% to 31 % (p<0.000). Wait times increased by 16% (p=0.003). Conclusions: Referral management helped respond to increased referral requests. Despite no change in accepted referrals, wait times increased, suggesting a significant capacity problem and focus for further work.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".