There are currently 15 million patients living with Heart Failure (HF) in Europe. Annual mortal risk for these patients is between 10% to 20%, higher than various cancers. HF patients, in particular those with (multiple) comorbidities, currently do not receive optimal medical therapy, leading to potentially avoidable specialist-visits and frequent hospitalizations, impaired quality of life or even life-threatening complications. All this results in high costs for society.
15 million
people affected
Mortal risk
up to 20 %
Not optimal
medical therapy
Led by Erasmus Universitair Medisch Centrum Rotterdam, this pilot is focused on introducing personal healthcare concepts to the benefits of patients with HF. It will underline the impact of appropriated infrastructure to deploy eHealth applications for value-based healthcare. The goal is to reach optimal medical therapy in 100% of subjects.
In many hospitals treatment is organized per discipline, and the exchange of health data between disciplines is severely limited. This practical is not optimal in case of comorbidities, since a medical practitioner doesn’t have access to the full medical history of a given patient.
Through the combination of datasets from multiple stakeholders, existing HF models/interventions will be improved enabling optimal medical therapy.
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Gürgöze, M. T., van der Galiën, O. P., Limpens, M. A. M., Roest, S., Hoekstra, R. C., IJpma, A. S., Brugts, J. J., Manintveld, O. C., and Boersma, E. (2020) Impact of sex differences in co‐morbidities and medication adherence on outcome in 25 776 heart failure patients. ESC Heart Failure, https://doi.org/10.1002/ehf2.13113.
Poster at BigMedilytics event: “Big Data: Fueling the transformation of Europe’s Healthcare Sector”. September 4-5, 2019, Valencia, Spain
Poster at BigMedilytics event: “Big Data: Fueling the transformation of Europe’s Healthcare Sector”. September 4-5, 2019, Valencia, Spain
Veeningen M, Chatterjea S, Horváth AZ, Spindler G, Boersma E, van der Spek P, van der Galiën O, Gutteling J, Kraaij W, Veugen T. Enabling analytics on sensitive medical data with secure multi-party computation. Stud Health Technol Inform. 2018;247:76-80. doi:10.3233/978-1-61499-852-5-76