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Contemporary Heart Failure Treatment Based on Improved Knowledge and Personalized Care of Comorbidities
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Author(s): Kostas Giokas (National Technical University of Athens, Greece), Charalampos Tsirmpas (AiM Research Team, Biomedical Engineering Laboratory, National Technical University of Athens, Greece), Athanasios Anastasiou (AiM Research Team, Biomedical Engineering Laboratory, National Technical University of Athens, Greece), Dimitra Iliopoulou (AiM Research Team, Biomedical Engineering Laboratory, National Technical University of Athens, Greece), Vassilia Costarides (AiM Research Team, Biomedical Engineering Laboratory, National Technical University of Athens, Greece)and Dimitris Koutsouris (National Technical University of Athens, Greece)
Copyright: 2019
Pages: 15
Source title:
Healthcare Policy and Reform: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-5225-6915-2.ch071
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Abstract
Chronic diseases are the leading cause of mortality and morbidity. A significant contribution to the burden of chronic diseases is the concurrence of co-morbidities. Heart failure (HF) is a complex, chronic medical condition frequently associated with co-morbidities. The current care approach for HF patients with co-morbidities is neither capable to deliver personalised care nor to halt the on-going increase of its socio-economic burden. Our approach aims to improve the complete care process for HF patients and related co-morbidities to improve outcome and quality of life. This will be achieved by the proposed standardised yet personalised patient-oriented ICT system that supports evidence-based clinical decision making as well as interaction and communication between all stakeholders with focus on the patients and their relatives to improve self-management. We propose that such a system should be build upon a novel European-wide data standard for clinical input and outcome and that it should facilitate decision making and outcome tracking by new collective intelligence algorithms.
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