Validity of Procedure Codes in International Classification of Diseases, 9th revision, Clinical Modification Administrative Data
Bibliographic record
Abstract
BACKGROUND: Administrative hospital discharge data are widely used to assess quality of care in patients undergoing certain procedures. However, little is known about the validity of administrative coding of procedure data. We conducted a detailed chart review to evaluate the accuracy and completeness of information on procedures in administrative data. METHODS: We randomly selected 1200 hospital separations in the period April 1, 1996, to March 31, 1997, from administrative discharge data of 3 acute adult hospitals in Calgary, Alberta, Canada. Each separation record in administrative data contains up to 10 procedure coding fields. The corresponding medical charts were reviewed for recording presence or absence of procedures. We then determined sensitivity to quantify the accuracy of coding presence of procedures in administrative data when these are present in the chart data (criterion standard). RESULTS: The agreement between the 2 databases varied greatly across 35 procedures studied. The sensitivity ranged from 0% to 94%. Of 6 major procedures studied, validity of coding was generally good, with 5 procedures having coding sensitivity of 69% and over and only 1 (lysis of peritoneal adhesion) with a low sensitivity of 41%. In contrast, many minor procedures had low sensitivities. Of 29 minor procedures studied, sensitivity was lower than 50% for 15 procedures, between 50% and 79% for 10, and 80% and over for 4. CONCLUSION: Validity of information on procedures in administrative discharge data appears to be related to type of procedures. Major procedures that are usually performed in operating rooms are reasonably well-coded. Meanwhile, minor procedures that are routinely performed on wards or in radiology departments are generally undercoded.
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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 | 0.054 | 0.252 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".