Comparing the coding of complications in Queensland and Victorian admitted patient data
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Notice bibliographique
Résumé
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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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle