How does the mapped ICD data in an EHR system compare to the hospital DAD data in Alberta, Canada?
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Coding by human coders is burdensome to healthcare systems requiring advanced computerized techniques. Epic has collaborated with Intelligent Medical Objects (IMO) solution to integrate a solution where the clinical interface terminology is mapped to the International Classification of Diseases (ICD). This study assesses the agreement between the solution mapped ICD-10-CA codes in Alberta, Canada's EHR system (based on Epic) to the human coded ICD-10-CA codes in hospital Discharge Abstract Database (DAD). DESIGN AND SETTING: In this retrospective analysis conducted in Alberta, Canada, we linked records in the acute care hospital DAD with the province wide EHR system for admissions between April 2021 and March 2024. MAIN OUTCOME(S) AND MEASURE(S): The primary outcome was the level of agreement between the solution mapped ICD-10-CA codes from the 'hospital problem list' in the EHR and hospital DAD data. The analysis was conducted at 3-digit and 4-digit ICD code level for main diagnosis and any diagnosis, and further stratified by physician specialty, hospital type and location, and length-of-stay. RESULTS: A total of 603,437 unique hospital records were linked between hospital DAD and EHR. The average level of agreement at 3-digit level of ICD-10-CA code for main diagnosis was 47.5% and any diagnosis was 37.0%. The average number of diagnoses coded by human coders in hospital DAD was higher than the solution mapped data in EHR. The agreement varied by specialty and length-of-stay with specialties with more complex patients and longer stays showing the lowest levels of agreement. CONCLUSION: Level of agreement between solution mapped EHR and hospital DAD for ICD-10-CA data was low, indicating significant differences 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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".