Aaltodoc publication archive (Aalto University institutional repository)
School of Business | Department of Information and Service Economy | MSc program in Information and Service Management | 2016
Thesis number: 14718
High-Cost Users in Social and Health Care - Mental Health and Substance Abuse Customers: Case City of Tampere
|Title:||High-Cost Users in Social and Health Care - Mental Health and Substance Abuse Customers: Case City of Tampere|
|Year:||2016 Language: eng|
|Department:||Department of Information and Service Economy|
|Academic subject:||MSc program in Information and Service Management|
|Index terms:||palvelut; terveystalous; asiakkaat; mielenterveys; päihteet; terveydenhuolto; sosiaalipolitiikka|
» hse_ethesis_14718.pdf size:2 MB (1600265)
|Key terms:||social care; health care; mental health; substance abuse; disease management; care management; cost analysis; high-cost users; service use; predictive modeling; logistic regression; cluster analysis|
Social and health care costs accumulate to a small number of people, called high-cost users. High-cost use correlates with poor health status and complex service needs and thus causes significantly higher expenditures than the rest of the population. It is not uncommon that the costliest 10 % causes 80 % of annual social and health care expenditures.
This study analyses the high-cost users of the City of Tampere, and in more detail, mental health and substance abuse customers. The aim of this study is to gather understanding of these high-cost users, their demographics, service use and the persistence of the high-cost use.
The gathered understanding is then used to segment the population into distinct groups in order to create a framework of potential methods to manage these segments, and to achieve cost savings, better services, effectiveness, productivity, and better quality.
In addition to managing the current high-cost users of mental health and substance abuse services, this study tackles the topic of predicting future high-cost users by exploring the possibilities in predictive modeling in social and health care.
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