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Artificial intelligence supported patient self-care in chronic heart failure
- Source :
- The EPMA Journal. 10(4):445-464
- Publication Year :
- 2019
- Publisher :
- Springer, 2019.
-
Abstract
- Heart failure (HF) is one of the most complex chronic disorders with high prevalence, mainly due to the ageing population and better treatment of underlying diseases. Prevalence will continue to rise and is estimated to reach 3% of the population in Western countries by 2025. It is the most important cause of hospitalisation in subjects aged 65 years or more, resulting in high costs and major social impact. The current "one-size-fits-all" approach in the treatment of HF does not result in best outcome for all patients. These facts are an imminent threat to good quality management of patients with HF. An unorthodox approach from a new vision on care is required. We propose a novel predictive, preventive and personalised medicine approach where patients are truly leading their management, supported by an easily accessible online application that takes advantage of artificial intelligence. This strategy paper describes the needs in HF care, the needed paradigm shift and the elements that are required to achieve this shift. Through the inspiring collaboration of clinical and high-tech partners from North-West Europe combining state of the art HF care, artificial intelligence, serious gaming and patient coaching, a virtual doctor is being created. The results are expected to advance and personalise self-care, where standard care tasks are performed by the patients themselves, in principle without involvement of healthcare professionals, the latter being able to focus on complex conditions. This new vision on care will significantly reduce costs per patient while improving outcomes to enable long-term sustainability of top-level HF care.
- Subjects :
- DECISION-SUPPORT
Patient stratification
Patient engagement
Heart failure
Medical ethics
Information and communications technology
CLASSIFICATION
Comorbidities
EJECTION FRACTION
Professional interactome
Artificial Intelligence
Healthcare economy
MANAGEMENT
Individualised patient profile
Societal impact
OUTCOMES
Therapy monitoring
Diabetes
Predictive preventive personalised participatory medicine
Integrated care
Healthcare digitalisation
TRENDS
TIME
2016 ESC GUIDELINES
Disease modelling
HOSPITALIZATION
Multi-level diagnostics
HEALTH-CARE
BURDEN
Subjects
Details
- Language :
- English
- ISSN :
- 18785085 and 18785077
- Volume :
- 10
- Issue :
- 4
- Database :
- OpenAIRE
- Journal :
- The EPMA Journal
- Accession number :
- edsair.od........83..5f20076174e92ed461f668caf3f06f98