Setting Quality Improvement Priorities for Women Receiving Systemic Therapy for Early-Stage Breast Cancer by Using Population-Level Administrative Data
Bibliographic record
Abstract
Purpose Routine evaluation of quality measures (QMs) can drive improvement in cancer systems by highlighting gaps in care. Targeting quality improvement at QMs that demonstrate substantial variation has the potential to make the largest impact at the population level. We developed an approach that uses both variation in performance and number of patients affected by the QM to set priorities for improving the quality of systemic therapy for women with early-stage breast cancer (EBC). Patients and Methods Patients with EBC diagnosed from 2006 to 2010 in Ontario, Canada, were identified in the Ontario Cancer Registry and linked deterministically to multiple health care databases. Individual QMs within a panel of 15 QMs previously developed to assess the quality of systemic therapy across four domains (access, treatment delivery, toxicity, and safety) were ranked on interinstitutional variation in performance (using interquartile range) and the number of patients who were affected; then the two rankings were averaged for a summative priority ranking. Results We identified 28,427 patients with EBC who were treated at 84 institutions. The use of computerized physician electronic order entry for chemotherapy, emergency room visits or hospitalizations during chemotherapy, and timely receipt of chemotherapy were identified as the QMs that had the largest potential to improve quality of care at a system level within this cohort. Conclusion A simple ranking system based on interinstitutional variation in performance and patient volume can be used to identify high-priority areas for quality improvement from a population perspective. This approach is generalizable to other health care systems that use QMs to drive improvement.
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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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".