Abstract
BackgroundPredictive modeling presents an opportunity to contain the expansion of medical expenditures by focusing on very few people. Evaluation of how risk adjustment models perform in predictive modeling in Taiwan or Asia has been rare. The aims of this study were to evaluate the performance of different risk adjustment models (the ACG risk adjustment system and prior expenditures) in predictive modeling, using Taiwan's National Health Insurance (NHI) claims data, and to compare characteristics of potentially high-expenditure subjects identified through different models.MethodsA random sample of NHI enrollees continuously enrolled in 2002 and 2003 (n = 164,562) was selected. Health status measures and total expenditures derived from 2002 NHI claims data were used to predict the possibility of becoming 2003 top users. Statistics-based indicators (C-statistics, sensitivity, & Predictive Positive Value) and characteristics of identified top groups by different models (expenditures and prevalence of manageable diseases) were presented.ResultsBoth diagnosis-based and prior expenditures models performed much better than the demographic model. Diagnosis-based models were better in identifying top users with manageable diseases; prior expenditures models were better in statistics-based indicators and identifying people with higher average expenditures. Prior expenditures status could correctly identify more actual top users than diagnosis-based or demographic models. The proportions of actual top users that could be identified by diagnosis-based models alone were much lower than that identified by prior expenditures status.ConclusionsPredicted top users identified by different models have different characteristics and there is little agreement between modes regarding which groups would be potentially top users; therefore, which model to use should depend on the purpose of predictive modeling. Prior expenditures are a more powerful tool than diagnosis-based risk adjusters in terms of correctly identifying more actual high expenditures users. There is still much room left for improvement of diagnosis-based models in predictive modeling.
Highlights
Predictive modeling presents an opportunity to contain the expansion of medical expenditures by focusing on very few people
The goal of this study is to evaluate the performance of the Adjusted Clinical Group (ACG) risk adjustment system in predictive modeling using Taiwan’s National Health Insurance claims data, and to compare characteristics of potentially high-expenditure subjects identified through different models
The results showed that both diagnosis-based and prior expenditures models performed much better than demographic models in predictive modeling, based on virtually all measures evaluated in the study
Summary
Predictive modeling presents an opportunity to contain the expansion of medical expenditures by focusing on very few people. The top 20% of the Predictive modeling in health care is generally defined as ‘a process of applying existing patient data to prospectively identify persons with high medical needs who are at risk for higher future medical utilization[7].’. Predictive modeling is important because early intervention can be delivered to persons identified as possibly having high medical needs. By helping these individuals manage their diseases effectively and providing coordinated medical care, their medical utilization can be reduced and the quality of care they receive can be maintained or improved[8]. Since high-expenditure users identified by diagnosis-based models have more ‘manageable’ diseases that are targets of disease management programs, it is the preferred model to use
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