Future research environments in the Healthcare domain

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Since the start of the EOSC initiative, the needs of potential stakeholders were put into focus – among them the wishes and requirements of researchers and research institutions. To understand better what the healthcare research community requires to perform cutting-edge research, an expert in open research, public health and computer sciences as well as a Nobel Laureate in the category Physiology or Medicine were invited to discuss their visions, requirements and needs for future research environments in the Healthcare domain.


Objectives & Challenges

The online session aimed at

  • bringing together a small, focused group of excellent researchers and research enablers who have a vision for and are interested in shaping the future of European research infrastructures for their domain 
  • obtain a better understanding on how research is changing
  • identify current barriers and services considered essential for a well-functioning EOSC 
  • elaborate visions on how research will be conducted in 5 to 15 years 
  • provide these as seeds for public comments to involve a large stakeholder community, thus ensuring many voices are being heard and that findings are considered in EOSC implementation processes, so that the EOSC is serving our needs as researchers

All findings were distributed among the EOSC governance bodies, the EOSC Working Groups and various stakeholders including researchers, members of university networks and funding bodies, thus providing input in the ongoing development processes of the EOSC. 

Main Recommendations

Within the online-session, a broad range of topics was covered during the discussions – among them reshuffling research environments, facilitating data intensive (health) systems, data and interdisciplinary research: 
The discussions on Reshuffling Research Environments addressed education and Data Literacy, support for the whole research cycle, support for highly focused studies, human expert infrastructure services, and enhancing collaboration. Among other things, it is recommended to: 

  • anchor data literacy basics in curricula at all levels of education.
  • support the whole research cycle by offering evaluation services to check the quality of data and research output and/or to enable researchers to put results under scrutiny.
  • supplement human peer review (e.g. running data, software checks, checking biases…) with AI approaches. In addition, machines could help overcome human biases and thus would be better at peer reviewing. Algorithms can be inspected and evaluated offering higher transparency.
  • establish support teams for scientific experts as part of a “human expert infrastructure to speed up scientific processes. Researchers could then contact experts in e.g. statistics, programming, data stewardship and many more, if questions and requirements outside their core domain expertise should arise. 

Topics such as connecting health systems, counteracting data colonialism, civic data cooperatives and avoiding vendor-lock in as well as vendor dominance were discussed in connection with Facilitating Data Intensive (Health) Systems. Many systems do not have the intelligence to take timely actions to e.g. control or contain a pandemic, or to reconfigure their systems to allow cross-agency communication and support actions across different agencies, which is crucial for public health. Thus, it was advocated to

  • connect various health systems with each other in order to understand them better. With regard to connecting such systems, national trusted nodes are a good starting point. National trusted nodes can work on specific topics, align with each other, agree flexibly on e.g. protocols on the technical, legal and trust level, which enables an organic growth of research institutions and health provider networks.
  • make counteracting data colonialism a political imperative. Solutions must involve society and be based on trust and mutual aid.
  • have citizens act as their own hub and as a trusted third party to optimize data uses and to increase societal benefits from the use of data. Such civic data cooperatives are about developing a culture that thinks as a group or as a system as well as about enabling a cooperative way of working between different players.

Debates surrounding Data covered the trustworthiness of data as well as reproducibility in research. In this context, two discussion points were particularly crucial:

  • The trustworthiness of data has to be ensured by all means. Thus, there needs to be a transparent framework to understand the data (depending on the type of data). Services to help with checking and verifying data quality need to be offered.
  • The reproducibility of research is still an issue. EOSC needs to support robust research by providing access to primary data (as it is crucial in reproducing results) and mechanisms to support reproducibility studies.

Focusing on Interdisciplinary Research led to the consideration of themes such as translation services and knowledge brokering. Services mentioned in this context are:

  • Translation services to help communication across disciplines. Key terms, scientific concepts and research outputs need to be communicated to policy makers and to the public efficiently.
  • Services facilitating two-way or multiway exchange of information. Knowledge needs to be brought together to create knowledge infrastructures and to bridge gaps between knowledge producers and knowledge consumers.
  • Reliable knowledge infrastructures in order to support researchers to coordinate research efforts so that they can look at different aspects of one problem.
  • Knowledge brokering as it helps with the distribution of knowledge to different audiences. Thus, it supports public outreach and research promotion.

For details on the key services, please see the report and/or the 2-pager on key takeaway messages. Both contain a collection of services as well as actions required.