Coding reliability and agreement of international classification of disease, 10th revision (ICD-10) codes in emergency department data
Bibliographic record
Abstract
INTRODUCTION: Administrative health data from emergency departments play important roles in understanding health needs of the public and reasons for health care resource use. International Classification of Disease (ICD) diagnostic codes have been widely used to code reasons of clinical encounters for administrative purposes in emergency departments. OBJECTIVE: The purpose of the study is to examine the coding agreement and reliability of ICD diagnosis codes in emergency department records through auditing the routinely collected data. METHODS: We randomly sampled 1 percent of records (n=1636) between October and December 2013 from 11 emergency departments in Alberta, Canada. Auditors were employed to review the same chart and independently assign main diagnosis codes. We assessed coding agreement and reliability through comparison of codes assigned by auditors and hospital coders using proportion of agreement and Cohen's kappa. Error analysis was conducted to review diagnosis codes with disagreement and categorized them into six groups. RESULTS: Overall, the agreement was 86.5% and 82.2% at 3 and 4 digits levels respectively, and reliability was 0.86 and 0.82 respectively. Variations of agreement and reliability were identified across different emergency departments. The major two categories of coding discrepancy were the use of different codes for same condition (23.6%) and the use of codes at different levels of specificity (20.9%). CONCLUSIONS: Diagnosis codes in emergency departments show high agreement and reliability, although there are variations of coding quality across different hospitals. Stricter coding guidelines regarding the use of unspecified codes are needed to enhance coding consistency.
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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 | 0.092 | 0.233 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".