Developments of Risk-Adjusted Diagnostic Groups Based on All Diagnostic Information and Applications to Risk Adjustment Models

• Main Authors: Shu-Ting Chang, 張舒婷
• Other Authors: Reui-Yi Chang
• Format: Others
• Language: zh-TW
• Published: 2005
• Online Access: http://ndltd.ncl.edu.tw/handle/11913320855639745544

碩士 === 國立臺灣大學 === 醫療機構管理研究所 === 94 === In order to contain the escalation of health care expenditure, many countries have adopted prospective budgets paying health care expenditure. Under prospective payment system, introducing risk adjustment mechanism in health insurance is considered to be able to maintain equity and efficiency. According to previous studies, the development of risk adjustment models with diagnostic information is one of most prominent approach to prevent risk selection and possess good predictability. However, only diagnostic groups based on outpatient and principal inpatient diagnoses respectively have been developed in Taiwan, and no diagnostic groups combining outpatient and inpatient diagnostic information is under development in this country. This study intends to develop risk adjusters based on all diagnostic information and compare the predictabilities of various risk adjustment models. The result can provide a reference of assigning prospective budget in Taiwan’s National Health Insurance. 2002 and 2003 NHI panel data was used to analyze in this study. The diagnostic-based risk adjusters are HCCs, CCS hierarchy groups, and CCS case-mix groups, and the later two systems were developed based on Clinical Classification Software. The results show that the PR2 and PR values of risk adjustment models with HCCs and CCS hierarchy groups perform well. The PR2s are about 20% and most PR values are close to 1, except for few specific disease subgroups. It depicts that the models can predict the expenditure of different disease subgroups accurately. And the performance of subgroup predictability in CCS hierarchy groups developing by this study is better than others, so it is suitable for calculating the expected medical expenditure of disease subgroups. However, the PR2 of CCS hierarchy groups is less then HCCs slightly. The reason may be that the clinical characteristic of CCS hierarchy was not as strong as that of HCCs. It is encouraged that the clinical characteristic should be strengthened in future study, such as consulting physicians’ opinion. Besides, we can add prescription information in risk assessments to improve the defect of only using diagnostic information in metal and chronic disease.