Monitoring obstetricians’ performance with statistical process control charts
Bibliographic record
Abstract
OBJECTIVE: The main objective of this study was to pave the way towards proactive, continuous assessment of individuals and hospitals by demonstrating the application of evidence-based competency standards in maternity care using statistical performance monitoring. DESIGN: Retrospective study using data routinely collected by a large maternity hospital. SETTING: A large teaching hospital. POPULATION: Clinicians who routinely perform either amniocentesis or ventouse deliveries. METHOD: As a 'proof of principle', we have used statistical process control (SPC) charts to compare the observed complication rates for amniocentesis and ventouse delivery with the expected complication rates based on published data. MAIN OUTCOME MEASURES: The recorded complication rates for amniocentesis and ventouse delivery. RESULTS: The SPC charts identified significant variation in complication rates within the team and showed the ways in which prospective data can be used to provide continuous feedback to individuals on their performance. CONCLUSION: The study shows that statistical performance monitoring and, in particular, the use of control charts can be a valuable tool in the continuous assessment of individuals and the healthcare service being provided. The control charts provide a more immediate indication of current performance and provide an alternative to performance-based league tables for the presentation of yearly performance data.
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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 | 0.062 | 0.176 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".