Opportunities and challenges for quality and safety applications in ICD-11: an international survey of users of coded health data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.004 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it