Advancing data collection of hospital-related harms: Validity of the new ICD-11 Quality & Safety Use Case
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
IntroductionThe beta version of the new ICD-11 includes a 3-part model for coding hospital acquired conditions (harms) to enhance adverse event descriptions. This method includes code clusters for detail each condition/event (e.g. bleed), cause (e.g. anticoagulant drug), and mode (over-dose).
 Objectives and ApproachTo compare the proportion of adverse events captured in ICD-11 to clinical chart review. A large field trial of 3000 inpatient charts are being coded with ICD-11 and chart review. Hospital admissions were randomly selected between January- June 2015 for adults at 3 Calgary hospitals. Chart reviewers were nurses trained to identify 11 categories of harms. Six coding specialists were trained to code with the ICD-11 3-part model for harm description. Coding decision trees and case examples of hospital-related harms were reviewed extensively by both teams. Coding training focused on new codes, code clustering, and extension codes for cause and mode of the harm.
 ResultsOf the 1,009 records reviewed and coded using ICD-11 to date, chart reviewers and coding specialists accurately identified 49 (37%) of the same charts with documented hospital harms. Both correctly identified 797 (91\%) of cases with no harm. Detailed analysis will follow. Study case examples will demonstrate advanced features of ICD-11 and the coding rules being collaboratively developed by our team, CIHI, and and WHO representatives.
 Conclusion/ImplicationsIdentification of hospital-related harms was consistent between coding specialists using ICD-11 principles and clinical chart reviewers. Variation existed in determining the cause and the mode of the harm. Case examples exemplify the new 3-part model for ICD-11 description of hospital-related harms.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.002 | 0.001 |
| 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