A Patient-Centric AI System

The healthcare ecosystem is realizing the importance of AI-powered tools in the next-generation healthcare technology. It is believed that AI can bring improvements to any process within healthcare operation and delivery. For instance, the cost savings that AI can bring to the healthcare system is an important driver for implementation of AI applications. The use of AI methods (Big Data Analytics, Machine and Deep Learning) as predictive tools is particularly relevant for brain diseases as, in many cases, by the time all the clinical symptoms manifest, the outcomes are essentially irreversible. In this light, better tools for assisting the detection of early signs of brain disease are needed. Advances in machine intelligence have created powerful capabilities in algorithms that find hidden patterns in data, identify anomalies in “expected” patterns as well as common features and are able to highlight associations between patients, conditions and therapeutical options.

An holistic value-based approach

Value-based healthcare (VBH) is a healthcare delivery model in which care costs are calculated based on patient health outcomes. In the value-based approach, providers are rewarded for helping patients improve their health, reduce the effects and incidence of chronic disease, and live healthier lives in an evidence-based way.

This approach differs from the fee-for-service approach, in which providers are paid based on the amount of healthcare services they deliver. The “value” in VBH is derived from measuring health outcomes against the cost of delivering the outcomes. This approach is expected to lead to: (a) Patients spend less money to achieve better health; (b) Providers to achieve efficiencies and greater patient satisfaction; (c) Payers to control costs and reduce risk; (d) Suppliers to align prices with patient outcomes; (e) Society to become healthier while reducing overall healthcare spending.

In the ALAMEDA model, VBH is delivered via Shared Decision Making (SDM) methods specifically adapted to the needs of follow-up care and rehabilitation. Shared decision‐making (SDM) explicitly involves patients and carers in decision-making about diagnostic and treatment options, and it is known to increase patients’ awareness and understanding of available options and the quality of decision‐making.

ALAMEDA will evaluate the achievement of value-based objectives incorporating consideration regarding the economic and societal impact and the cost-variation between the actual costs and the cost of the proposed solution. The evidence generated will open new opportunities for innovation and growth both in the care of Parkinson’sMS and stroke care ecosystem and in general in similar applications of Big Data and AI for quality of life improvements of brain disease patients.


EU This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101017558.