DAY 2 | 29 November 2022 | 14:00 CET (GMT +01:00)
With the support of Clinerion, Switzerland.
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🕐 Session Programme
► 14:00 CET Welcome to Day 2
Randi Laukli, Norwegian Centre for E-health Research and Vice-President of EHTEL
► 14:15 CET A Data Culture for Society to support health data re-use at scale? An exploration
📝 Session abstract
🗣️ Speakers
The data relationship
Wannes Van Hoof, Sciensano, Belgium
Whenever personal data is used, a piece of someone’s identity and someone’s story is used. This means that every data user enters into a relationship with a person behind the data, at least to some extent. It is crucial to recognize this relationship, and to foster it into one based on trust, solidarity and shared values and principles. When citizens are involved in co-creating the ethical, legal and societal framework for health data use and reuse, rather than limiting their contribution to individual consent, it is possible to develop a responsible data culture for all.
Dynamic and ethical e-consent to free up health data
Nesrine Benyahia, Dr Data, France
Privacy is not only about encryption or strong passwords, it is also about individuals’ rights. Building a trustful health data world means involving citizens and give them a key role to free up health data.
Transparency through e-consent or ethical opt-out is necessary. Decentralized technologies can make this easy by combining privacy & innovation. It is time for data consumers to understand that there is an individual behind each line of data that needs privacy guarantees. It is time to make citizens understand that their health data are collectively powerful. It is time for ethics by design.
How can AI provide benefits to citizens and contribute to building the data culture for society?
Barış Erdoğan, Clinerion, Switzerland
The most important trend after the Covid pandemic is the increase in the development of prediction algorithms to help both the administrative and the clinical staff in Health Care Organisations. Predicting who are the high-risk patients before they reach a critical state both saves lives and enables efficient use of medical resources. However, training and validating algorithms requires various data sets for increased accuracy. Advanced technology and the use of data can enable the development of better algorithms: in the end, this will support physicians to provide preventive as well as personalized treatments.
First row panellists
Sara Mas, Salus Coop, Spain
Isabelle de Zegher, b!loba, Belgium
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