Reducing Wait Times: A Comparative Analysis of CT Scan Efficiency in Alberta and Ontario
Bibliographic record
Abstract
Jessica, a retired teacher in Edmonton, waited nearly three weeks in pain before finally receiving a CT scan to rule out kidney stones. In Ontario, a similar patient, Elena, was scanned, diagnosed, and treated within one week. These contrasting stories illustrate a persistent gap: thousands of Albertans face prolonged uncertainty and complications from long CT scan wait times. These delays lead to unnecessary hospital admissions, higher healthcare costs, and growing public frustration. This highlights the important question: What specific diagnostic imaging policies, practices, and system-level interventions have allowed Ontario to sustain consistently shorter CT scan wait times, and under what conditions can these policies be effectively adapted and implemented within Alberta’s healthcare context? Alberta’s average median CT scan wait time has fluctuated around 18 days, while Ontario has stayed below the 7-day benchmarks since 2008, all while having similar per-person CT scanners and similar infrastructure investments. The explanation is not about equipment or funding but about policy and system design. This project is one of the first to compare diagnostic imaging policy over fifteen years across two provinces, using a rapid review of fifty-eight sources, policy mapping, and a combination of comparative and statistical analysis. Ontario’s success came from adopting ten key policy levers together, while Alberta introduced some of these measures, but did so slowly and in isolation. The evidence is clear: no single reform or partial bundle produced more than minor gains, and wait times only dropped when at least eight of these levers were in place at once. Statistical modelling predicts that if Alberta adopts the full Ontario-style policy bundle, median CT wait times could drop by almost eight days within one year. With sustained action, Alberta could approach the national benchmark of a seven-day median. Alberta has a unique chance to act now with the following phased plan: • Phase 1: Implement province-wide eReferral and transparent dashboards. • Phase 2: Link funding to performance and expand navigation teams. • Phase 3: Transition to single central intake and upgrade scanners. This is a rare, actionable policy window. The need is urgent, the evidence is strong, and the tools are at hand. By making these coordinated reforms, Alberta can sharply reduce CT wait times, improve patient care, and restore public confidence in its health system.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".