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Predictive risk modelling in the Spanish population: a cross-sectional study
Juan F Orueta1, Roberto Nuño-Solinis, Maider Mateos
1Osakidetza, Basque Health Service, Bilbao, Spain.
BMC Health Services Research
|July 11, 2013
Summary
Risk adjustment models effectively identify high healthcare cost patients. Adding previous cost data significantly improves prediction accuracy across different systems.
Area of Science:
- Health Services Research
- Public Health
- Health Economics
Background:
- Rising chronic conditions pose a significant threat to healthcare systems.
- Risk adjustment models are crucial for population stratification and implementing new care models.
- Evaluating predictive capabilities of different risk adjustment models is essential for healthcare management.
Purpose of the Study:
- To assess the predictive accuracy of ACG-PM, DCG-HCC, and CRG-based models for healthcare costs.
- To identify high healthcare cost patients using these models.
- To analyze the impact of socio-economic variables on predictive capacity.
Main Methods:
- Cross-sectional study of 1,964,337 individuals in the Basque Country over two years.
- Used demographic, clinical, and cost data from the first 12 months to build predictive models.
- Assessed model performance using coefficient of determination and ROC curve analysis.
Main Results:
- Diagnosis-based models showed coefficients of determination from 0.18-0.24.
- ROC analysis demonstrated strong ability to identify high-cost patients (0.78-0.90 AUC).
- Previous cost data significantly reduced model differences; deprivation index had minor impact.
Conclusions:
- US-developed case-mix systems are valuable for universal healthcare systems.
- These systems can identify at-risk individuals for high resource consumption.
- Proactive interventions can potentially prevent high healthcare utilization.
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