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  • Title: A predictive model to allocate frequent service users of community-based mental health services to different packages of care.
    Author: Grigoletti L, Amaddeo F, Grassi A, Boldrini M, Chiappelli M, Percudani M, Catapano F, Fiorillo A, Perris F, Bacigalupi M, Albanese P, Simonetti S, De Agostini P, Tansella M, I-PSYCOST Group.
    Journal: Epidemiol Psichiatr Soc; 2010; 19(2):168-77. PubMed ID: 20815301.
    Abstract:
    AIM: To develop predictive models to allocate patients into frequent and low service users groups within the Italian Community-based Mental Health Services (CMHSs). To allocate frequent users to different packages of care, identifying the costs of these packages. METHODS: Socio-demographic and clinical data and GAF scores at baseline were collected for 1250 users attending five CMHSs. All psychiatric contacts made by these patients during six months were recorded. A logistic regression identified frequent service users predictive variables. Multinomial logistic regression identified variables able to predict the most appropriate package of care. A cost function was utilised to estimate costs. RESULTS: Frequent service users were 49%, using nearly 90% of all contacts. The model classified correctly 80% of users in the frequent and low users groups. Three packages of care were identified: Basic Community Treatment (4,133 Euro per six months); Intensive Community Treatment (6,180 Euro) and Rehabilitative Community Treatment (11,984 Euro) for 83%, 6% and 11% of frequent service users respectively. The model was found to be accurate for 85% of users. CONCLUSION: It is possible to develop predictive models to identify frequent service users and to assign them to pre-defined packages of care, and to use these models to inform the funding of psychiatric care.
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