Introduction

Existing initiatives on early detection and intervention on health and social risks are demonstrated only in clinical trials. Several healthcare systems in Europe – including many recruited in this project – have put in place strategies to stratify populations at risk based on the level of complexity. In some of these cases such stratifications is based on digitalized health records and in few of them the health records are integrating information from primary and secondary care settings. However, risk prevention and management is not implemented proactively.

GATEKEEPER Large Scale Pilots (LSP) will establish and consolidate the different Use Cases through Europe enabling the deployment of digital solutions for early detection and intervention and support the risk stratification models. They ensure that GATEKEEPER users’ and medical requirements for early detection and intervention are correctly deployed in a coordinated way in all pilot sites.

The ambition of carrying out LSP across Europe is to cooperate together among the pilot sites and with the involvement of a large number of users in order to assess and contribute to the understanding of differentiating factors of successful solutions in the area of health and social risks prevention for understanding AHA. 

The Use Cases that will be implemented in the different Pilot Sites will cover:

Lifestyle-related early detection and interventions

Big data Analytics techniques will be exploited to address risk stratification and early detection.

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COPD exacerbations management

Machine learning methods to implement apps that predict exacerbations and avoid hospitalizations.

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Diabetes: predictive modeling of glycemic status

Short-term prediction of glycemic dynamic is essential to improve Diabetes self-management.

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Parkinson’s disease treatment DSS

Key enabling technologies to continuously or periodically measure motor and non-motor symptoms.

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Predictive readmissions and decompensations in HF

Telemonitoring services and to implement an advanced model for predicting acute Heart Failure decompensations

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Primary and secondary stroke prevention

Image recognition to detect signs for active early warning alarms to target secondary stroke prevention

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Multi-chronic elderly patient management including polimedication

Sensing technologies to monitor parameters in Chronic Care Models for multi-morbid subjects.

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