EDITORIAL
Introduction to the Special Issue: Data Intelligence on
Patient Health Records
Mirjam van Reisen1†, Barend Mons2
1Leiden University Medical Centre (LUMC), Leiden University, 1310 Leiden, the Netherlands
2Leiden University Medical Centre, Poortgebouw N-01, Rijnsburgerweg 10 2333 AA Leiden, the Netherlands
Citation: van Reisen, M., Mons, B.: Introduction to the Special Issue: Data Intelligence on patient health records. Data
Intelligence 4(4), 671–672 (2022). doi: 10.1162/dint_e_00165
Submitted: March 10, 2021; Revised: June 10, 2022; Accepted: July 15, 2022
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Data Intelligence is the ultimate purpose of FAIR data management. FAIR as in data that is Findable,
Accessible (under well defined conditions), Interoperable and Reusable. FAIR also as in ethical data; data
that fulfils the requirements of Personal Data Protection, is collected for well defined purposes and is held
and curated within ownership of the location where the data is produced.
In this Special Issue, we ask how more data intelligence can be derived from Medical Patient Records.
All health facilities associated with the reported studies produce Medical Patient Records, and these records
are highly structured and employing vocabularies that are internationally understood and used. The
researchers of the Virus Outbreak Data Network-Africa were interested to understand whether these data
would be a good source for FAIRification and, once FAIRified, would provide a data pipeline that can be
the future of data intelligence with a global scope and at a scale that is currently lacking. The programme
therefore focused on Africa, a continent with a minimum of legacy in digital health records data intelligence
and therefore an excellent place to build a pioneer, novel FAIR system.
The Special Issue introduces the preparatory work to establishing a workable Minimal Viable Product
based on FAIR machine-actionable patient health records. The work comprised the following steps. First an
exploration of the regulatory frameworks internationally and in each of the participating countries. This
exploration was positive, in all of the countries the policy-framework was positively geared towards the
FAIR principles.
The second step was focused on the FAIRification process of the data as well as the localisation of data
repositories. The outcome of this phase was positive, a data-visiting exercise across countries and even
across continents, worked out. This demonstrated that the basic principles of the FAIRification process were
realistic and operational.
†
Corresponding author: Mirjam van Reisen (Email: mirjamvanreisen@gmail.com; ORCID: 0000-0003-0627-8014).
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© 2022 Chinese Academy of Sciences. Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
Introduction to the Special Issue: Data Intelligence on Patient Health Records
In the third step the research team set out to understand the different conditions for deployment in
different places. For this purpose the team identified places that varied widely in terms of remoteness,
connectedness, health services provided and conditions for operations. The result was a list of specifications
and requirements providing the basis for engineering software, that could be installed and serviced locally
in these widely different conditions in an African context.
In a final stage the working conditions as pertaining to digital patient health records in facilities were
studied, with a view to understanding the usefulness in the daily practice of the health facilities. Four
parallel use-cases were identified and these were integrated in a Virtual Image that was deployed in 88
health facilities in 8 countries.
The result is a data intelligent system that relates to four purposes: (i) monitoring by Ministry of Health;
(ii) monitoring of data generated and stored in each of the facilities (iii) visualisation of common statistics
derived from data-visited by algorithms in the health facilities and (iv) a FAIR-store of data that can be used
for dynamic use-cases, based on permission granted by the facility.
FAIR works in Africa and this test-case forms an excellent basis for further development and deployment
—it combines data ownership with a data-visiting capacity, and this can form an ethical pipeline for
innovation of ML and AI supported health systems. The lessons learned in the African setting can be of high
value for implementation in other continents as well, including regions where health information systems
and ICT infrastructure might be more advanced, but nevertheless not enabling FAIR based distributed
analytics and learning by visiting local data. The research demonstrated that it is possible to include data
from areas that are poorly connected and that quality data for ML and AI pipelines can and should include
diverse data. This Special Issue documents that this is an achievable goal: the development of an inclusive,
high quality, and ethical pipeline based on stewardship of data according to FAIR Guidelines of medical
patient records.
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AUTHOR CONTRIBUTION STATEMENT
Mirjam Van Reisen (0000-0003-0627-8014, mirjamvanreisen@gmail.com); conceptualization, methodology,
formal analysis, visualization, writing original draft preparation, writing—review and editing, supervision,
project administration, funding acquisition Barend Mons (0000-0003-3934-0072, barendmons@gmail.com)
conceptualization, writing—review and editing.
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672
Data Intelligence