A performance and quality improvement intervention integrating molecular testing to support personalized cancer care in lung and thyroid cancers.
Bibliographic record
Abstract
260 Background: Molecular testing (MT) remains underutilized in the diagnosis and treatment of cancer patients (pts), despite its potential to significantly impact clinical decision-making and outcomes. Performance Improvement (PI) targets gaps with focused interventions to enhance process effectiveness, while a Quality Improvement (QI) aims to improve overall care quality, emphasizing pts outcomes. This project aimed to evaluate the process and impact of a PI/QI intervention to optimize integration of MT for pts with lung, especially non-small-cell lung cancer (NSCLC), or thyroid cancer (TC) within two MD Anderson Cancer Network sites (A and B). Methods: This PI/QI project engaged physicians, advanced practice providers, nurses and research teams for up to 18 months from the two targeted sites. After baseline evaluation of MT PI/QI needs, each site identified practice gaps, set objectives and developed their context-specific action plan to optimize integration of MT for NSCLC/TC. Participants then attended 4 faculty-led interactive webinars focused on evidence and best practice guidelines on MT in these cancers. A longitudinal, mixed-methods evaluation was performed, including a review of electronic medical records (EMR) of NSCLC/TC pts (pre, during and post), questionnaires and qualitative interviews with PI/QI participants (6 months post) as complementary sources of data for contextualization. Results: Site A EMR at pre (n=295 NSCLC; n=7 TC) showed that 84% (254/302) of NSCLC/TC pts had at least single-gene mutations or immunohistochemistry testing. However, only 59% (177/302) had comprehensive next generation sequencing (NGS) on a large gene panel. The primary objective of site A was to optimize NGS testing. Site B EMR (n=112 NSCLC; n=3 TC) showed that 75% (86/115) had MT ordered, and 33% (38/115) had NGS. Site B primary objective was to increase all types of MT. After 18 months, site A EMR showed an estimated increase in NGS tests ordered to 76 tests/100 pts (+17% absolute increase from pre). After 6 months, site B EMR showed an increase of all MT to 83 tests/100 pts (+8% absolute increase from pre), and an increase of NGS tests to 44 tests/100 pts (+12% absolute increase from pre). Questionnaire responses and qualitative interviews (n=8) indicated that sites focused on optimizing workflows to offer comprehensive MT for personalized treatment. They also articulated how limited resources and time constraints were experienced and considered, notably in the delegation of new roles and responsibilities for improved coordination of care. Conclusions: The evaluation suggests that the PI/QI intervention enabled both sites and their healthcare teams to achieve targeted changes in MT, while still facing the challenges of a busy clinical environment. Findings can inform other organizations on strategies to optimize MT for all eligible cancer patients.
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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 | 0.001 | 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 | 0.000 | 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".