Proposal ID: 965343
ICCS project ID: 63118000
Role: Coordinator
Topic: SC1-DTH-12-2020
Type of action: RIA
Call identifier: H2020-SC1-BHC-2018-2020

RETENTION: heaRt failurE paTient managEment and iNTerventIOns usiNg continuous patient monitoring outside hospitals and real world data

Duration in months: 48
Free keywords: Intelligent Interventions, Connected Health, Heart Failure, Big Data, Machine Learning, Real World Data (RWD), Internet of Medical Things, Internet of Things, Security, Privacy

Heart failure (HF) is a prominent chronic disease, despite efforts in improving its prevention, diagnosis and treatment, represents the leading cause of disability and premature death throughout the world, while being is a heavy burden on health systems due to the multiple, prolonged hospitalizations required for patients suffering from it and the related management costs. Nevertheless, studies have shown that routine patient-physician or patient-nurse communication following discharge can result in significant reduction of hospitalizations. The pertinent collected patient data may include simple yet important information (e.g. patient weight), as well as previously difficult to obtain information such as routine ECG, ICD/CRT-D interrogation and pulmonary arterial pressure monitoring, which are now within reach due to technological progress. Motivated by the above, RETENTION aims to develop and deliver an innovative platform supporting enhanced clinical monitoring and interventions aimed at improving the clinical management of patients with chronic HF, reducing their mortality and hospitalisation rates, and improving their quality of life, safety, and well-being. The RETENTION platform will support clinical decision making and evidenced based personalised interventions for HF patients by: (a) continually monitoring and collecting medical, clinical, physiological, behavioural, psychosocial, and real-world data for such patients, (b) analysing these data using innovative model-driven big data analytics, statistical, artificial intelligence and machine learning techniques, (c) detecting patterns in the HF disease progression and the quality of life of patients, (d) cross checking and validating them against the clinical literature, and (e) offering transparent, explainable and verifiable decision making capabilities that leverage the evidence produced by the underlying data analysis and augment clinical studies targeting HF and other CVDs.

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