The implementation of a streamlined process for biomarker testing in medical oncology.
Bibliographic record
Abstract
179 Background: Biomarker testing is increasingly becoming an essential part of standard care. At Sunnybrook Health Sciences Centre (SHSC) in Toronto, Canada, our Department of Molecular Services handles some internal biomarker tests, while some tests are referred to citywide labs. The Odette Cancer Centre (OCC) of SHSC is one of the largest cancer centres in Canada, serving over 12,000 new patients per year, of whom many rely on biomarker tests for personalized treatment. We aimed to describe the work systems at the OCC for biomarker testing and reporting, and to initiate system improvements. Methods: This quality improvement initiative occurred in three phases: qualitative descriptive analysis of the current process of biomarker testing, exploration of future state process using LEAN, and implementation of a streamlined process. In phase one, ten medical oncologists, two administrative assistants, and one pathologist were interviewed. A multidisciplinary team was then assembled to investigate and initiate improvements. Results: Tracking results from external labs was managed by individual physicians and hard copy results were submitted to medical records for filing in patient paper charts, compared to internal tests which are posted on the electronic record, making outside tests harder to retrieve later. The current process involved more than 150 different tests with only 44% of results appearing in the hospital electronic record. In June 2016, a standardized process was implemented where a designated laboratory assistant managed requisition forms, sent corresponding specimens to qualified labs, ensured the receipt of results through various electronic tracking tools and validated the subsequent upload to the electronic medical system. Over a four-month implementation period, there were 364 cases/patients with 467 tests requested; 100% of these test results are stored in the electronic record. Conclusions: The lack of standardization of biomarker testing and reporting can have negative implications on quality of care and patient safety. Therefore, streamlining this process and incorporating electronic tracking tools can improve the accessibility of test results to improve the quality of oncology care.
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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 | 0.117 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".