Identifying client characteristics to predict homecare use more accurately: a Delphi-study involving nurses and homecare purchasing specialists.

Background: Case-mix based prospective payment of homecare is being implemented in several countries to work towards more efficient and client-centred homecare. However, existing models can only explain a limited part of variance in homecare use, due to their reliance on health- and function-related client data. It is unclear which predictors could improve predictive power of existing case-mix models.

Development of a casemix classification to predict costs of home care in the Netherlands: a study protocol.

Introduction: Compared with fee-for-service systems, prospective payment based on casemix classification is thought to promote more efficient, needs-based care provision. We aim to develop a casemix classification to predict the costs of home care in the Netherlands.

Methods And Analysis: The research is designed as a multicentre, cross-sectional cohort study using quantitative methods to identify the relative cost predictors of home care and combine these into a casemix classification, based on individual episodes of care.