In this contribution, we propose a healthcare decision support system. Nowadays, it is commonly recognized that quantitative tools and decision support can increase the benefits and the performances of healthcare systems, and for this, different multiple criteria methods were proposed in many branches of medicine. The approach we propose is based on non-additive measures and the Choquet integral. This methodology has been intensively applied in many real-world applications, given its capability to represent interactions among criteria, and thus to model a wide range of preference structures. Considering that the diagnosis procedure needs also to take the clinical expertise into account, this method appears particularly tailored for a diagnosis support, mainly when statistical models cannot be applied and/or available data are scarce and knowledge can be inferred by physicians’ opinions. In particular, we propose a disease risk evaluation and compute some associated indicators. Furthermore, an error estimation is performed. As an application, a cardiovascular risk diagnosis model is presented. The proposed methodology, that allows to quantify the disease risk taking into account individual’s medical conditions, can be used for improving healthcare service quality or for pricing and reserving health insurance policies. An application to health insurance pricing is provided.

Multi-criteria and medical diagnosis for application to health insurance systems: a general approach through non-additive measures

Anzilli Luca
;
2020

Abstract

In this contribution, we propose a healthcare decision support system. Nowadays, it is commonly recognized that quantitative tools and decision support can increase the benefits and the performances of healthcare systems, and for this, different multiple criteria methods were proposed in many branches of medicine. The approach we propose is based on non-additive measures and the Choquet integral. This methodology has been intensively applied in many real-world applications, given its capability to represent interactions among criteria, and thus to model a wide range of preference structures. Considering that the diagnosis procedure needs also to take the clinical expertise into account, this method appears particularly tailored for a diagnosis support, mainly when statistical models cannot be applied and/or available data are scarce and knowledge can be inferred by physicians’ opinions. In particular, we propose a disease risk evaluation and compute some associated indicators. Furthermore, an error estimation is performed. As an application, a cardiovascular risk diagnosis model is presented. The proposed methodology, that allows to quantify the disease risk taking into account individual’s medical conditions, can be used for improving healthcare service quality or for pricing and reserving health insurance policies. An application to health insurance pricing is provided.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11587/441635
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