D.6.10. Use Cases Implementation and Experimentation v1

The goal of this document is to describe the first activities performed to demonstrate the implementation and experimentations with the CrowdHEALTH platform by the use case partners of the consortium. These are part of Task 6.3 and two further demonstration activities are planned to be reported in months 24 and 36.

The main demonstration activities of the first version of the CrowdHEALTH system consisted of the following aspects:

1. Data Import from multiple sources: At the project meeting in Rome (13-14. March 2018) a live demonstration is given of how data from use case partners is imported into the CrowdHEALTH platform; this was data provided in the specific format of the use case partners, but not data from real persons, but synthetic data or pseudonymised data, in order to avoid any privacy issues during the demonstration at the meeting. The synthetic data is provided by the use case partners BioAssist (BIO, Greece), Hospital La Fe (HULAFE) and SLOfit (JSI, Slovenia).
2. Analytics capabilities: At the meeting in Rome again the partners developing analytics component demonstrate the status of analytics and visualization components. These demonstrations are based on the partners own data and are specifically the analysis and predictions algorithms developed by JSI on data from the SLOfit use case.
3. Installation and Integration with existing UC partner infrastructures and foreseen exploitation by policy makers: All use case partners described whether they have (resp. plan to have) an on-site or assume a central installation of the CrowdHEALTH platform, how the integration with their current infrastructure is (resp. is planned) and how policy makers will be able to use the CrowdHEALTH analytics.
4. Requirements by UC partners to foster acceptance: In order to encourage adoption of the system use case partners foresee different additional functionalities. These consist in the SLOfit use case of providing access for teachers to collected data of individual scholars and of the usage of prediction or forecasting analytics. In the joint HULAFE-DFKI use case these include providing access for nutritionists coaching obesity patients to the tracked nutritional and activity data, and comparing it with recommended nutritional behaviour. Similarly, in the BioAssist use case these consist of allowing doctors to use CrowdHEALTH’s forecasting and risk assessment tools on data of individual patients, and of comparing the results with expected values or outcomes for groups of patients.