Improving Timeliness of Oncology Assessment and Cancer Treatment Through Implementation of a Multidisciplinary Lung Cancer Clinic
Bibliographic record
Abstract
PURPOSE: Timely lung cancer care has been associated with improved clinical outcomes and patient satisfaction. We identified improvement opportunities in lung cancer management pathways at Kingston Health Sciences Centre. Quality improvement strategies led to the implementation of a multidisciplinary lung cancer clinic (MDC). METHODS: We set an outcome measure of decreasing the time from diagnosis to first cancer treatment by 10 days within 6 months of clinic implementation. We implemented a weekly MDC that involved respirologists, medical oncologists, and radiation oncologists at which patients with new lung cancer diagnoses were offered concurrent oncology consultation. We used Plan-Do-Study-Act cycles to guide our improvement initiatives. A total of five Plan-Do-Study-Act cycles spanned 14 months and consisted of an MDC pilot clinic, large-scale MDC launching, debriefing meetings, and clinic expansion. Pre-MDC data were analyzed retrospectively to establish baseline and prospectively for improvement. Statistical Process Control XmR(i) charts were used to report data. RESULTS: Since MDC initiation, 128 patients have been seen in 34 MDC clinics (3.8 patients per clinic). Mean days from diagnosis to first oncology assessment decreased from 12.4 days to 3.9 days, and mean days from diagnosis to first cancer treatment decreased from 39.5 to 15.0 days, both of which demonstrated special cause variation. Time to assessment and treatment improved for patients with every stage of lung cancer and for both small-cell and non-small-cell subtypes. CONCLUSION: MDC shortens the time from lung cancer diagnosis to oncology assessment and treatment. Time to treatment improved more than time to oncology assessment, which suggests the improvement is related to benefits beyond faster oncology assessment.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".