Unfortunately, we had to cancel the physical event as a consequence of the Covid-19 crisis that had gained control of Austria and most other home countries of our international authors and visitors. Nevertheless, we decided to go ahead with publishing the proceedings. On the one hand side, to keep the momentum and to avoid an empty space in the rather long row of proceedings of this conference, which spans 14 years already. On the other hand, we think that this year’s special topic: “Biomedical Informatics for Health and Care” is also very timely. We chose it to indicate that, already today, but even more in the future, ICT systems in healthcare and biomedical systems and devices will increasingly be intertwined. Both scientific disciplines, i.e. biomedical engineering and health informatics, are obviously closely related to each other and it is often difficult to delineate where the one ends and the other begins.
A special topic for dHealth 2019 was “From eHealth to dHealth”.
According to the motto “from eHealth to dHealth”, we renamed the conference previously known as eHealth, stressing that healthcare will be more and more data-driven in the future. While eHealth predominately concerns IT solutions at the level of healthcare providers, dHealth addresses additional fields along the data path “from sensors to decisions”. Both of the conference programme tracks, i.e. the scientific track and the applied dHealth Summit track, were designed as an interdisciplinary approach and to spark a transdisciplinary dialogue to the various aspects of digital health.
A special topic for eHealth2018 was “Biomedical meets eHealth - From Sensors to Decisions”.
Future ICT systems and biomedical systems and devices will increasingly be intertwined and share a common entity which is data. This completes the chain and flow of information from the sensor via the processing to the actuator, which can be anything from a human healthcare professional to a Robot. Along this pathway, methods for automating the information processing like machine learning and predictive analytics will play an increasing role in order to provide actionable information and to support preventive health care concepts, in both biomedical and digital healthcare systems and applications.