MS DataConnect

MS DataConnect

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We aim to transform the mostly population based management of Multiple Sclerosis (MS) of today into an individualized, personalized and precision level management. We believe the key to achieve this next level of MS management is “F.A.I.R.” data. F.A.I.R. stand for Findable, Accessible, Interoperable and Re-usable. Currently, data is used rather superficially whereas we assume many new insight are to be discovered using the data that is already there. However, two main hurdles obstruct us from reaching these insights. First, most data is not findable, not accessible, not interoperable and not re-usable. Secondly, we lack proper analytical tools for optimal data mining. With this project, we aim to overcome these two obstructions.

Imagine any type of data being ‘Findable, Accessible, Interoperable and Reusable’ by both humans and machines. Important to note here is that “findable” does not mean “for everyone to find”, “Accessible” does not mean “open access”, “interoperable” does not mean “for everyone to operate on” and “re-usable” for everyone to use”. However, it creates the possibility to find, access, interoperate, and re-use data when necessary. In other words, it gives data the opportunity to have maximal impact. The possibilities to discover new insights multiplies manifold. We may finally be able to make far greater strides forward than ever before in the challenging MS management.

But before we get there, many hurdles have to be overcome. There is an urgent need for data collection procedures and tools that create F.A.I.R. data. Data isn’t FAIR because you open it up to others. We should all become more disciplined in collecting and storing data the correct way, i.e. the F.A.I.R way. This project investigated these hurdles, opportunities and challenges in the concept of MS.

We aim to develop 1° data collection procedures and tools to create data that is F.A.I.R. (=findable, accessible, interoperable and re-usable), 2° IT solutions to allow (temporarily) pooling and linking of F.A.I.R dataset, 3° statistical methods to define minimal requirements for datasets and 4° new analytical methods for optimal mining of connected and pooled F.A.I.R datasets. Two pilot studies are performed. Pilot study 1 develops F.A.I.R. data collection procedures for a local consortium involved in care, rehabilitation and research and connects the datasets involved. Pilot study 2 develops a statistical method to evaluate the relative importance of prognostic risk factors.

Pilot study 1 aims to develop user-friendly and sustainable data collection procedures and tools that create F.A.I.R. data. We focus on data collection procedures and tools that permit visit-entry or automation. Next to this, all IT codes are open source. Working with open source IT codes enables a low priced implementation of IT platforms for MS data entry collection. In addition, it creates the possibility to adapt the IT platform meeting local needs. These characteristics facilitate the use and implementation of these tools by other national and international partners and is in line with our vision and future perspectives.

Next to this, this pilot study gives us the opportunity to investigate legal, ethical and practical consequences of sharing initiatives using a relative simplified set-up. Currently, there are a lot of insecurities around extensive data sharing initiatives (e.g. How does the General Data Protection Regulation effect sharing possibilities? What about informed consents? How should we hand pseudo-anonymization, how can data be shared respecting security and privacy? …) . Investigating ethical, legal and practical solutions using a clearly defined “test-case” that operates in a community of trust and an alliance of like-minded partners will result in concrete answers that can be used for more complex consortia or situations.

The following values provide our framework for decisions.

SECURITY We prioritize safe storage and sharing of data
We respect ethical and legal restrictions of data sharing
We value the involvement and respect the rights of the people with MS
QUALITY We prioritize meaningful multi-and interdisciplinary research
We secure the quality of the data
TRANSPARENCY We are transparent in our research and activities
We consult our partners for all major decisions and provide them with all findings and reports
USER FRIENDLY We provide an IT platform that enables visit entry and encourages automation of data-collection
SUSTAINABILITY We strive to constantly improve the scope and methodology of our data collection procedures