Peter Martin1,2, Roger Davies3, Amy Macdougall1, Benjamin Ritchie1, Panos Vostanis4,5, Andy Whale1, Miranda Wolpert1. 1. Evidence Based Practice Unit, University College London and Anna Freud Centre for Children and Families, London, UK. 2. Department of Applied Health Research, University College London, London, UK. 3. City and Hackney CAMHS, East London Foundation Trust, London, UK. 4. Department of Neuroscience, Psychology and Behaviour, University of Leicester, Leicester, UK, and. 5. Department of Clinical, Educational and Health Psychology, University College London, London, UK.
Abstract
Background: Case-mix classification is a focus of international attention in considering how best to manage and fund services, by providing a basis for fairer comparison of resource utilization. Yet there is little evidence of the best ways to establish case mix for child and adolescent mental health services (CAMHS).Aim: To develop a case mix classification for CAMHS that is clinically meaningful and predictive of number of appointments attended and to investigate the influence of presenting problems, context and complexity factors and provider variation.Method: We analysed 4573 completed episodes of outpatient care from 11 English CAMHS. Cluster analysis, regression trees and a conceptual classification based on clinical best practice guidelines were compared regarding their ability to predict number of appointments, using mixed effects negative binomial regression. Results: The conceptual classification is clinically meaningful and did as well as data-driven classifications in accounting for number of appointments. There was little evidence for effects of complexity or context factors, with the possible exception of school attendance problems. Substantial variation in resource provision between providers was not explained well by case mix. Conclusion: The conceptually-derived classification merits further testing and development in the context of collaborative decision making.
Background: Case-mix classification is a focus of international attention in considering how best to manage and fund services, by providing a basis for fairer comparison of resource utilization. Yet there is little evidence of the best ways to establish case mix for child and adolescent mental health services (CAMHS).Aim: To develop a case mix classification for CAMHS that is clinically meaningful and predictive of number of appointments attended and to investigate the influence of presenting problems, context and complexity factors and provider variation.Method: We analysed 4573 completed episodes of outpatient care from 11 English CAMHS. Cluster analysis, regression trees and a conceptual classification based on clinical best practice guidelines were compared regarding their ability to predict number of appointments, using mixed effects negative binomial regression. Results: The conceptual classification is clinically meaningful and did as well as data-driven classifications in accounting for number of appointments. There was little evidence for effects of complexity or context factors, with the possible exception of school attendance problems. Substantial variation in resource provision between providers was not explained well by case mix. Conclusion: The conceptually-derived classification merits further testing and development in the context of collaborative decision making.
Entities:
Keywords:
case mix; child and adolescent mental health services; cluster analysis; outpatient care; payment systems; regression trees; resource use