Pilot #12 of INFINITECH is now collecting RWD

Pilot #12 of INFINITECH is now collecting RWD

INFINITECH’s Pilot 12 is aspiring to offer insurance companies the tools to assess the lifestyle of their clients, to facilitate personalized health insurance offerings.

The first step in understanding the lifestyle risks of clients is to measure their Real-world Data (RWD) client. Then machine learning systems can perform risk and fraud assessments, while insurance company personnel can verify these assessments and prepare the personalised offers to the clients.

Pilot 12 has taken this first step utilizing Healthentia for the collection of RWD. The RWD span across four categories (physiological, psychological, social and environmental) and are collected in two modes (measured using devices, reported by the clients via questionnaires). In doing so, we have identified an initial set of measurements and questionnaires, all in the physiological category, and are having a RWD collection dry run with volunteers already working in Pilot 12. Currently we have 15 users contributing:

  • Physiological data measurements (daily steps, energy burned, active minutes and their characterization, exercise sessions detected)
  • Reported symptoms (body temperature, blood pressure, fatigue, headache and pain perception)
  • Reported quality of life (emotional status, EQ5D5L for usual activities or mobility)
  • Sleep (reported as start and end time, or in the case of a user with Fitbit, measured sleep stages)
  • Reported initial demographic info (age, gender, medical history, education, occupation, hobbies)
  • Monitoring this dry run will allow us to understand our RWD and ways to augment them, get user experience feedback and compile a toy dataset to start the analysis tasks.

    Stay tuned as we monitor our 15 volunteers before opening up the study to a larger group, collecting a larger quantity of data and a variety of user profiles to draw insights from.


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    logo europe This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 856632.
    The content reflects only the authors’ views, and the European Commission is not responsible for any use that may be made of the information it contains.

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