How many diagnosis fields are needed to capture safety events in administrative data? Findings and recommendations from the WHO ICD-11 Topic Advisory Group on Quality and Safety
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: As part of the WHO ICD-11 development initiative, the Topic Advisory Group on Quality and Safety explores meta-features of morbidity data sets, such as the optimal number of secondary diagnosis fields. DESIGN: The Health Care Quality Indicators Project of the Organization for Economic Co-Operation and Development collected Patient Safety Indicator (PSI) information from administrative hospital data of 19-20 countries in 2009 and 2011. We investigated whether three countries that expanded their data systems to include more secondary diagnosis fields showed increased PSI rates compared with six countries that did not. Furthermore, administrative hospital data from six of these countries and two American states, California (2011) and Florida (2010), were analysed for distributions of coded patient safety events across diagnosis fields. RESULTS: Among the participating countries, increasing the number of diagnosis fields was not associated with any overall increase in PSI rates. However, high proportions of PSI-related diagnoses appeared beyond the sixth secondary diagnosis field. The distribution of three PSI-related ICD codes was similar in California and Florida: 89-90% of central venous catheter infections and 97-99% of retained foreign bodies and accidental punctures or lacerations were captured within 15 secondary diagnosis fields. CONCLUSIONS: Six to nine secondary diagnosis fields are inadequate for comparing complication rates using hospital administrative data; at least 15 (and perhaps more with ICD-11) are recommended to fully characterize clinical outcomes. Increasing the number of fields should improve the international and intra-national comparability of data for epidemiologic and health services research, utilization analyses and quality of care assessment.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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