TY - CONF
T1 - FAIR and GDPR Compliant Population Health Data Generation, Processing and Analytics
AU - Plug, Ruduan
AU - Liang, Yan
AU - Basajja, Mariam
AU - Aktau, Aliya
AU - Jati, Putu Hadi Purnama
AU - Amare, Samson Yohannes
AU - Taye, Getu
AU - Mpezamihigo, Mouhamed
AU - Oladipo, Fransisca
AU - van Reisen, Mirjam
PY - 2022/1
Y1 - 2022/1
N2 - Generating and analysing patient data in clinical settings is an inherently sensitive process, requiring collaborative effort between clinicians and informaticians to generate value from these data, while mitigating risks to the data subject. As a result, efforts in utilizing external patient data pose significant challenges. We propose a data-centric framework based on the FAIR principles and GDPR guidelines to enhance data management at the point of care. By using the process of data visiting, a cross-facility method for federated data analytics, we can automate generation of novel aggregate data which was previously not realizable. In two sequential studies we show that these techniques, supported by a data stewardship programme, increase community-wide involvement in data generation, improve transparency and trust, provide direct value and data ownership, and enable regulatory and ethically compliant, cross-national data visiting under curated accessibility patterns for federated analytics.
AB - Generating and analysing patient data in clinical settings is an inherently sensitive process, requiring collaborative effort between clinicians and informaticians to generate value from these data, while mitigating risks to the data subject. As a result, efforts in utilizing external patient data pose significant challenges. We propose a data-centric framework based on the FAIR principles and GDPR guidelines to enhance data management at the point of care. By using the process of data visiting, a cross-facility method for federated data analytics, we can automate generation of novel aggregate data which was previously not realizable. In two sequential studies we show that these techniques, supported by a data stewardship programme, increase community-wide involvement in data generation, improve transparency and trust, provide direct value and data ownership, and enable regulatory and ethically compliant, cross-national data visiting under curated accessibility patterns for federated analytics.
KW - FAIR Data
KW - GDPR
KW - Data management
KW - Data Stewardship
KW - Clinical Data
KW - Biomedical Ontologies
KW - Data Federation
KW - Data Visiting
M3 - Paper
ER -