Diagnosis-based risk adjustment and Australian health system policy

Diagnosis-based risk adjustment is increasingly seen as an important tool for establishing capitation payments and evaluating appropriateness and efficiency of services provided and has become an important area of research for many countries contemplating health system reform. This paper examines the application of a risk-adjustment method, extensively validated in the United States, known as diagnostic cost groups (DCG), to a large Australian hospital inpatient data set. The data set encompassed hospital inpatient diagnoses and inpatient expenditure for the entire metropolitan population residing in the state of New South Wales. The DCG model was able to explain 34% of individual-level variation in concurrent expenditure and 5.2% in subsequent year expenditure, which is comparable to US studies using inpatient-only data. The degree of stability and internal consistency of the parameter estimates for both the concurrent and prospective models indicate the DCG methodology has face validity in its application to NSW health data sets. Modelling and simulations were conducted which demonstrate the policy applications and significance of risk adjustment model(s) in the Australian context. This study demonstrates the feasibility of using large individual-level data sets for diagnosis-based risk adjustment research in Australia. The results suggest that a research agenda should be established to broaden the options for health system reform.