Research agenda
| Theme | Research point |
| Data sources | Leverage EULAR legacy initiatives around core datasets that should be collected in research (and usual care) as foundations for successful big data projects in the field of RMDs |
| Determine the optimal use of eHealth data through digital traces and patient-generated/patient-reported data | |
| Determine the potential use of database linkages, such as healthcare system claims databases | |
| Data access | Identify the mechanisms supporting and implications following open access to, and sharing of, big data |
| Assess positive and negative aspects of data sharing in terms of article impact (academic/social) and translational success | |
| Identify the challenges, opportunities and solutions for international data sharing | |
| Develop a repository of privacy rules in different European countries | |
| Identify public platforms for data and how the public can access their own data within big data sets for knowledge/education/self-management purposes | |
| Analyses | Evaluate and compare statistical methods and benchmarking of big data |
| Develop methods of assessment and minimisation of bias and of generalisation/reproducibility | |
| Determine the most appropriate open source tools to improve reproducibility of the results | |
| Perform a critical assessment of statistical significance vs clinical relevance of the results obtained from medical big data | |
| Reporting | Stimulate consistent reporting of big data studies using validated reporting guidelines |
| Stimulate and facilitate open sharing of codes/scripts | |
| Implementation | Determine the value of algorithms and big data findings in terms of quality of care and cost effectiveness |
| Assess levels of evidence in evidence-based medicine when based on big-data studies | |
| Manage the potential rapid and frequent changes of outcomes when implementing big data findings | |
| Training | Identify opportunities for training via the EULAR School of Rheumatology and other relevant organisations |
| Assess the importance of inter and cross-disciplinarity | |
| Assess the place of multidisciplinary training at specific stages of individual careers and/or at specific stages of specific projects | |
| Consider introducing a basic big data/systems biology/bioinformatic course at bachelors’ levels for healthcare professionals | |
| Collaborations | Stimulate national and international interest among the data scientist community in relation to RMDs |
| Promote the integration of RMD fluent ‘ethical experts’ in collaborative teams working on big data | |
| Ethics and roles | Stimulate ethical and moral discussions with patients and ‘data donors’ specifically in the context of big data, addressing topics such as informed consent/assent, confidentiality, anonymity and privacy concerns, particularly with regards to the re-use of the data |
| Discuss the roles and responsibilities of healthcare professionals, scientists/researchers and patients in relation to big data | |
| Assess issues pertaining to commercial use of big data, particularly involving public–private consortiums and the use of multiple datasets | |
| Assess the effects of big data results on use of drugs including in unauthorised/compassionate use cases | |
| Define the role, modalities and rules of patient engagement in the generation and exploitation of big data |
RMD, rheumatic and musculoskeletal disorder.