Opportunities and challenges for quality and safety applications in ICD-11: an international survey of users of coded health data
Bibliographic record
Abstract
OBJECTIVE: In 2018, the World Health Organization (WHO) plans to release the 11th revision of the International Classification of Diseases (ICD). The overall goal of the WHO is to produce a new disease classification that has an enhanced ability to capture health concepts in a manner that is compatible with contemporary information systems. Accordingly, our objective was to identify opportunities and challenges in improving the utility of ICD-11 for quality and safety applications. DESIGN: A survey study of international stakeholders with expertise in either the production or use of coded health data. SETTING: International producers or users of ICD-coded health care data. STUDY PARTICIPANTS: We used a snowball sampling approach to identify individuals with relevant expertise in 12 countries, mostly from North America, Europe, and Australasia. An 8-item online survey included questions on demographic characteristics, familiarity with ICD, experience using ICD-coded data on healthcare quality and safety, opinions regarding the use of ICD classification systems for quality and safety measurement, and current limitations and potential future improvements that would permit better coding of quality and safety concepts in ICD-11. RESULTS: Two-hundred fifty-eight unique individuals accessed the online survey; 246 provided complete responses. The respondents identified specific desires for the ICD revision: more code content for adverse events/complications; a desire for code clustering mechanisms; the need for diagnosis timing information; and the addition of better code definitions to reference materials. CONCLUSION: These findings reinforce the vision and existing work plan of the WHO's ICD revision process, because each of these desires is being addressed.
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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 | 0.021 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".