Comparing terminology mappings to ICD-10 coded data in Discharge Abstract Database (DAD) in Alberta, Canada
Bibliographic record
Abstract
IntroductionCoding has become burdensome to healthcare systems due to patient complexity and resource requirements. In Alberta, Intelligent Medical Objects (IMO), an interface terminology mapping product, is integrated within the new province-wide Clinical Information System, named Connect Care (CC), to support documentation by clinicians and map clinical terminologies to ICD-10. This study evaluates comparability of terminologies mapped ICD-10 codes to the ICD-10 codes in DAD. ApproachWe conducted a retrospective analysis by linking acute care hospital DAD with CC between April 2021 and December 2023. The primary outcome was the level of agreement between ‘hospital problem list’ of CC and DAD for ICD-10-CA codes at a 3-digit level. The number of diagnoses and rate of unspecified codes were also compared. The outcome measures were stratified by physician specialty, hospital type and location, and length-of-stay (LOS). ResultsA total of 498,834 unique hospital records were linked. The average level of agreement between CC and DAD at 3-digit level of ICD-10-CA code was 43.4%. The average number of diagnoses captured in CC (3.91) was slightly lower than DAD (4.06), and the average rate of unspecified codes was higher in CC (26.4%) compared to DAD (23.0%). The level of agreement varied by specialty and length-of-stay with specialties with more complex patients and longer lengths-of-stay having the lowest agreement (43% for generalists and internal medicine and 33% for LOS >3 months). ConclusionLevel of agreement between CC and DAD for ICD-10 data was identified as low, indicating significant disparities between terminology mappings and the coding process.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".