ICD-11 for quality and safety: overview of the who quality and safety topic advisory group
Bibliographic record
Abstract
This paper outlines the approach that the WHO's Family of International Classifications (WHO-FIC) network is undertaking to create ICD-11. We also outline the more focused work of the Quality and Safety Topic Advisory Group, whose activities include the following: (i) cataloguing existing ICD-9 and ICD-10 quality and safety indicators; (ii) reviewing ICD morbidity coding rules for main condition, diagnosis timing, numbers of diagnosis fields and diagnosis clustering; (iii) substantial restructuring of the health-care related injury concepts coded in the ICD-10 chapters 19/20, (iv) mapping of ICD-11 quality and safety concepts to the information model of the WHO's International Classification for Patient Safety and the AHRQ Common Formats; (v) the review of vertical chapter content in all chapters of the ICD-11 beta version and (vi) downstream field testing of ICD-11 prior to its official 2015 release. The transition from ICD-10 to ICD-11 promises to produce an enhanced classification that will have better potential to capture important concepts relevant to measuring health system safety and quality-an important use case for the classification.
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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 | 0.077 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.021 | 0.016 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.011 | 0.010 |
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".