L. Grigoletti, F. Amaddeo, A. Grassi, M. Boldrini, M. Chiappelli, M. Percudani, F. Catapano, A. Fiorillo, F. Perris, M. Bacigalupi, P. Albanese, Simona Simonetti, P. De Agostini, M. Tansella
{"title":"A predictive model to allocate frequent service users of community-based Mental Health Services to different packages of care","authors":"L. Grigoletti, F. Amaddeo, A. Grassi, M. Boldrini, M. Chiappelli, M. Percudani, F. Catapano, A. Fiorillo, F. Perris, M. Bacigalupi, P. Albanese, Simona Simonetti, P. De Agostini, M. Tansella","doi":"10.1017/S1121189X00000877","DOIUrl":null,"url":null,"abstract":"Summary 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, identifing 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.","PeriodicalId":72946,"journal":{"name":"Epidemiologia e psichiatria sociale","volume":"19 1","pages":"168 - 177"},"PeriodicalIF":0.0000,"publicationDate":"2010-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/S1121189X00000877","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Epidemiologia e psichiatria sociale","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1017/S1121189X00000877","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8
Abstract
Summary 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, identifing 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.