ICD-11: towards better capture of quality and safety events in hospital
Bibliographic record
Abstract
ABSTRACT BackgroundThe Quality and Patient Safety TAG (Q&S TAG) is charged with reviewing ICD-10, ICD-10CM and progressive drafts of ICD-11 to inform the development of the ICD-11, focusing on identifying practical modifications for ICD 11 drafts that would enable better measurement of quality and safety. ApproachThe tasks of the Q&S TAG include: Horizontally crossing all ICD-11 chapters to advise on optimizing the entire classification’s content, structure and coding rules for enhanced application in both existing; Developing an inventory of existing quality of care and patient safety indicators and potentially novel quality and safety indicators; Assessing potential uses of ICD-11 for health services, quality and patient-centered outcomes research; Reviewing and critiquing the ICD-11 beta draft from the perspective of the quality and safety use case; Reviewing and critiquing Volume II work from the perspective of quality and safety use case and Designing field trials for the beta version of ICD-11. The Q&S TAG has held meetings in both New York, NY and Washington, D.C. where members have reviewed the status of discussions around coding rules (main condition, diagnosis timing, coding field), Chapter 19&20 content and associated clustering mechanisms and presented these concepts in emails to WHO and prepared to undertake a granular review of the content in chapters 1-20 and devised a committee work plan to do this. ResultsSix manuscripts have come from meetings. The QS-TAG has also devised a matrix model for considering potential ICD-11 field trials. The matrix categorizes cross-tabulates topic areas (e.g., validity of coded concepts, completeness of capture of critical patient safety and quality concepts, reliability and feasibility of various coding rules, opinions of stakeholders on various issues) against the methodologies that would be used for the field trials (i.e., code-recode studies using real medical records, coding studies assessing completeness of capture of key safety/quality concepts, surveys of stakeholders, heuristic evaluations of ICD-11 on various user interfaces, etc). Q&S TAG has completed 2 field trials, with 2 more in development phases. ConclusionUltimately, an enhanced classification system will permit expanded use of coded health data for large-scale quality and safety surveillance in health care systems internationally.
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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 | 0.074 | 0.292 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".