Comparing the coding of complications in Queensland and Victorian admitted patient data
Bibliographic record
Abstract
Objective. To examine differences between Queensland and Victorian coding of hospital-acquired conditions and suggest ways to improve the usefulness of these data in the monitoring of patient safety events. Design. Secondary analysis of admitted patient episode data collected in Queensland and Victoria. Methods. Comparison of depth of coding, and patterns in the coding of ten commonly coded complications of five elective procedures. Results. Comparison of the mean complication codes assigned per episode revealed Victoria assigns more valid codes than Queensland for all procedures, with the difference between the states being significantly different in all cases. The proportion of the codes flagged as complications was consistently lower for Queensland when comparing 10 common complications for each of the five selected elective procedures. The estimated complication rates for the five procedures showed Victoria to have an apparently higher complication rate than Queensland for 35 of the 50 complications examined. Conclusion. Our findings demonstrate that the coding of complications is more comprehensive in Victoria than in Queensland. It is known that inconsistencies exist between states in routine hospital data quality. Comparative use of patient safety indicators should be viewed with caution until standards are improved across Australia. More exploration of data quality issues is needed to identify areas for improvement. What is known about the topic? Routine data are low cost, accessible and timely but the quality is often questioned. This deters researchers and clinicians from using the data to monitor aspects of quality improvement. Previous studies have reported on the quality of diagnosis coding in Australia but not specifically on the quality of use of the condition-onset flag denoting hospital-acquired conditions. What does this paper add? Few studies have tested the consistency of the data between Australian states. No previous studies have evaluated the comprehensiveness of the coding of hospital-acquired conditions using routine data. This paper compares two states to highlight the differences in the coding of complications, with the aim of improving routine data to support patient safety. What are the implications for practitioners? The results imply more work needs to be done to improve the coding and flagging of complications so the data are valid and comprehensive. Further research should identify problem areas responsible for differences in the data so that training and audit strategies can be developed to improve the collection of this information. Practitioners may then be more confident in using routine coded inpatient data as part of the process of monitoring patient safety.
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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 | 0.026 | 0.136 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".