Prediction of Health Care Cost Using the Hierarchical Condition Category Risk Adjustment Model / 보건행정학회지

ABSTRACT

BACKGROUND: This study was conducted to evaluate the performance of the Hierarchical Condition Category (HCC) model, identify potentially high-cost patients, and examine the effects of adding prior utilization to the risk model using Korean claims data. METHODS: We incorporated 2 years of data from the National Health Insurance Services-National Sample Cohort. Five risk models were used to predict health expenditures model 1 (age/sex groups), model 2 (the Center for Medicare and Medicaid Services-HCC with age/sex groups), model 3 (selected 54 HCCs with age/sex groups), model 4 (bed-days of care plus model 3), and model 5 (medication- days plus model 3). We evaluated model performance using R² at individual level, predictive positive value (PPV) of the top 5% of high-cost patients, and predictive ratio (PR) within subgroups. RESULTS: The suitability of the model, including prior use, bed-days, and medication-days, was better than other models. R² values were 8%, 39%, 37%, 43%, and 57% with model 1, 2, 3, 4, and 5, respectively. After being removed the extreme values, the corresponding R² values were slightly improved in all models. PPVs were 16.4%, 25.2%, 25.1%, 33.8%, and 53.8%. Total expenditure was underpredicted for the highest expenditure group and overpredicted for the four other groups. PR had a tendency to decrease from younger group to older group in both female and male. CONCLUSION: The risk adjustment models are important in plan payment, reimbursement, profiling, and research. Combined prior use and diagnostic data are more powerful to predict health costs and to identify high-cost patients.