Reduced wait times for molecular-biomarker testing among patients with advanced lung cancer using parallel sample processing and closed-loop communication: the Continuous Oncopanel and ALK Status Tracking (COAST) Project
Bibliographic record
Abstract
BACKGROUND: Given how crucial molecular-biomarker testing is to treatment decisions for advanced non-small cell lung cancer (NSCLC), guidelines recommend a turnaround time (TAT) of 14 calendar days from receiving specimen to test result, and 3 days to receive a specimen from outside pathology lab. Current TAT for molecular-biomarker testing in British Columbia (BC), Canada frequently exceeds these recommendations. Thus, we launched a quality improvement (QI) initiative to improve TAT of molecular-biomarker testing. METHODS: We assembled a multidisciplinary team to identify areas contributing to delays in molecular-biomarker testing. We designed and conducted three Plan-Do-Study-Act (PDSA) cycles. Cycles 1 and 2 involved changing requisition delivery method from fax to email between BC Cancer Vancouver Centre (BCCV) and two outside hospitals with the highest volume of specimens. Cycle 3 introduced parallel processing of specimens at BCCV pathology lab and concurrent molecular-biomarker testing. TAT was evaluated before and after these process changes along with staff satisfaction via survey. RESULTS: The average TAT from test request to specimen received by BCCV was reduced from 6.4 days prechange to 4.6 days postchange. The TAT from specimen receipt by BCCV to report availability was reduced from a median of 22-16 days. Report availability for initial medical oncology consultation increased from a median of 13%-29%. The staff satisfaction survey revealed an enhanced experience with the new process, particularly for our nurse navigator. CONCLUSION: The TAT for molecular-biomarker testing is an ongoing and complex challenge for healthcare systems worldwide. This QI project is a step towards addressing province-wide reduction in wait times for molecular-biomarker testing results in BC. We continue to explore further ideas to improve workflow metrics and patient care among patients with advanced NSCLC.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".