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.
Health system reform in the United States
