This paper aims to demonstrate the importance of mixture distribution modelling in analysing the characteristics of inpatient length of stay (LOS), which has direct implications on health planning and formation of payment policy. It is found that mixture distribution analysis can confirm the homogeneity of certain Diagnosis Related Groups (DRGs). It can also reveal the heterogeneous patterns of other DRGs. For those DRGs exhibiting heterogeneity in LOS, related socio-economic factors influencing LOS are compared and contrasted between components by Poisson mixture regressions. Such an analysis provides an integrated framework to link funding with relevant influencing factors of LOS. A Poisson mixture regression model can give useful insights for state health institutions to initiate efficient casemix payments. It also benefits hospital managers and clinicians to manage LOS more effectively.