Analyzing Partitioned FAIR Health Data Responsibly
Chang Sun, Lianne Ippel, Birgit Wouters, Johan van Soest, Alexander, Malic, Onaopepo Adekunle, Bob van den Berg, Marco Puts, Ole Mussmann,, Annemarie Koster, Carla van der Kallen, David Townend, Andre Dekker, Michel, Dumontier
TL;DR
This paper discusses the challenges and progress in responsibly analyzing partitioned FAIR health data from clinical and statistical sources to understand diabetes and socio-economic factors.
Contribution
It highlights the social, legal, technical, and scientific issues in analyzing sensitive health data and presents progress in addressing these challenges.
Findings
Identification of key challenges in data sharing and analysis
Development of methods to address legal and privacy issues
Progress in integrating clinical and socio-economic data
Abstract
It is widely anticipated that the use of health-related big data will enable further understanding and improvements in human health and wellbeing. Our current project, funded through the Dutch National Research Agenda, aims to explore the relationship between the development of diabetes and socio-economic factors such as lifestyle and health care utilization. The analysis involves combining data from the Maastricht Study (DMS), a prospective clinical study, and data collected by Statistics Netherlands (CBS) as part of its routine operations. However, a wide array of social, legal, technical, and scientific issues hinder the analysis. In this paper, we describe these challenges and our progress towards addressing them.
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Taxonomy
TopicsData Quality and Management · Data-Driven Disease Surveillance · Privacy-Preserving Technologies in Data