Physician level reporting of surgical and pathology performance indicators: a regional study to assess feasibility and impact on quality
Bibliographic record
Abstract
BACKGROUND: There is increased awareness that, to minimize variation in clinician practice and improve quality, performance reporting should be implemented at the provider level. This optimizes physician engagement and creates a sense of professional responsibility for quality and performance measurement at the individual and organizational levels. METHODS: Individual provider level reporting was implemented within a provincial health region involving 56 clinicians (general surgeons, surgical oncologists, urologists and pathologists). The 2 surgical pathology indicators chosen were colorectal cancer (CRC) lymph node retrieval rate and pT2 prostate cancer margin positivity rate. Surgical resections for all prostate and colorectal cancer performed between Jan. 1, 2011, and Mar. 30, 2012, were included. We used a pre- and postsurvey design to obtain physician perceptions and focus groups with program leadership to determine organizational impact. RESULTS: Survey results showed that respondents felt the data provided in the reports were valid (67%), consistent with expectations (70%), maintained confidentiality (80%) and were not used in a punitive manner (77%). During the study period the pT2 prostate margin positivity rate decreased from 57.1% to 27.5%. For the CRC lymph node retrieval rate indicator, high baseline performance was maintained. CONCLUSION: We developed a robust process for providing physicians with confidential, individualized surgical and pathology quality indicator reports. Our results reinforce the importance of individual physician feedback as a strategy for improving and sustaining quality in surgical and diagnostic oncology.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".