Optimizing the collection and reporting of wait time data.
Bibliographic record
Abstract
121 Background: Wait times for critical interventional radiology (IR) procedures have long been reported as barriers to access for cancer patients. Wait times have been difficult to measure in the province of Ontario as no mechanisms exist to capture IR data. Three individual programs at Cancer Care Ontario (CCO) began collection of CT-guided biopsies wait time data, with nuances dependent on program area of focus. The Cancer Imaging Program collects appointment availability for lung biopsies through a monthly email survey. The Diagnostic Assessment Program collects patient-level lung biopsy data at select facilities in the province. The Access to Care program collects patient-level data from facilities that perform CT biopsies, but the data is not specific to lung biopsies. To streamline data collection processes and provide a comprehensive report for the province, the three programs are collaborating to validate current data sets and optimize data collection. Methods: A four phase approach has been developed to prepare for the ongoing comprehensive report: Planning – identify target audience, timelines, and data; Data Validation – three step evaluation to compare data sets; Test Report – consultation of draft report with clinical experts; and Evaluation – lessons learned from steps one to three to prepare for ongoing report. Results: This initiative allows each program to: Critically assess the components of their respective datasets; Cross-validate data for consistency or alignment; and Frame the clinically relevant data components to support and inform decision making and reporting processes. Through this process, efficiencies are gained by streamlining data collection and ensuring alignment between program areas. The collaborative approach ensures the business needs of each program are protected in the final product. Furthermore, this process will allow decision makers at CCO to analyze various methods of wait time data collection and aid in methodology decisions of future collections. Conclusions: The final product of this initiative will provide physicians and decision makers throughout Ontario with robust data to understand what is happening in their hospitals. The report will also facilitate impact analysis of interventions to reduce wait times.
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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 | 0.231 | 0.372 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.009 | 0.017 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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; the direct Gemma label and the distilled Codex classifier 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".