Method for developing national quality indicators based on manual data extraction from medical records
Bibliographic record
Abstract
Developing quality indicators (QI) for national purposes (eg, public disclosure, paying-for-performance) highlights the need to find accessible and reliable data sources for collecting standardised data. The most accurate and reliable data source for collecting clinical and organisational information still remains the medical record. Data collection from electronic medical records (EMR) would be far less burdensome than from paper medical records (PMR). However, the development of EMRs is costly and has suffered from low rates of adoption and barriers of usability even in developed countries. Currently, methods for producing national QIs based on the medical record rely on manual extraction from PMRs. We propose and illustrate such a method. These QIs display feasibility, reliability and discriminative power, and can be used to compare hospitals. They have been implemented nationwide in France since 2006. The method used to develop these QIs could be adapted for use in large-scale programmes of hospital regulation in other, including developing, countries.
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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 | 0.051 | 0.093 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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; both teacher heads agree on what is shown here.
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".