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Helping people with chronic diseases take better control of their health at home
The TREAT project addresses the growing global burden of non-communicable diseases, such as diabetes and heart disease, which currently account for the majority of annual deaths.
Modern healthcare systems are often siloed and system-centric, making it difficult for patients to effectively manage chronic conditions from home.
This lack of integration leads to fragmented medical advice, a heavy reliance on healthcare providers for data interpretation, and poor patient adherence to treatment plans.
To solve these challenges, the project aims to develop a patient-centric platform focused on semantic interoperability and self-efficacy.
This intelligent system will unify data from diverse sources, including wearables and medical records, to provide personalized monitoring.
By utilizing artificial intelligence and interactive interfaces, the TREAT project seeks to empower patients with the tools and knowledge needed to manage their own health.
Ultimately, the project aims to improve long-term health outcomes while reducing the overall burden on clinical staff and healthcare systems.
Almende will focus on supporting patiens with motivational support. We are responsible for the development of the sophisticated AI motivational engine to empower the patient in a health system.
Our work ensures that diverse data from wearables, medical records, and journals can be seamlessly integrated and understood in context. By utilizing advanced technologies like knowledge graphs and large language models, we create the intelligence needed to provide personalized health guidance.
We refer to this core intelligence as the motivational engine. This component provides the automated care feedback loops and actionable insights necessary to empower patients. Through these innovations, we aim to help individuals manage chronic conditions with greater confidence and self-efficacy.
The TREAT project will deliver a modular software platform that unifies diverse health data through semantic interoperability.
Key outputs include AI-driven “smart engines” that provide personalized health recommendations and interactive interfaces to increase patient adherence.
The result is a validated, patient-centric system that improves self-efficacy and long-term health outcomes.
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