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Developing a case mix classification for child and adolescent mental health services
2 September 2017
Case-mix classification puts the different types of patients attending services into groups. It is used to consider how best to manage and fund services and offers a fairer comparison of how resources are used. However, there is a little evidence of the best ways to establish case mix in child and adolescent mental health services (CAMHS).
The aim of this research was therefore to develop a case-mix classification for CAMHS that was clinically meaningful and could predict the number of appointments attended. To do this, 4573 closed cases from 11 English CAMHS were analysed.
Three methods of classification were compared: statistical methods – cluster analysis and regression analysis – and conceptual classification. However, the statistical methods used did not produce reliable or clinically meaningful categories. Because of this, the conceptual classification was examined more closely.
The conceptual grouping of presenting problems (such as ADHD, depression and OCD) is based on NICE guidelines and provides a clinically meaningful description of case mix. A service user’s group predicts to some extent how many appointments we can expect them to attend, yet the variation of appointments within groups is larger than the differences between them. The study found little evidence that complexity factors (e.g. parental health issues or experience of abuse) or context factors (e.g. issues at home) account for differences in resource use, once the conceptual grouping is taken into account. Differences in resource provision between providers are not well explained by case mix.
In its current form the case-mix classification makes only a modest contribution in accounting for the differences in resource provision in CAMHS. Further research using reliable and high quality data is required. However, the conceptual grouping is a useful starting point for the development of a case-mix classification which could be used to inform payment and quality monitoring in CAMHS.