Blueprint for harmonising unstandardised disease registries to allow federated data analysis: prepare for the future
Year: 2022
Type: review
Abstract: Real-world evidence from multinational disease registries is becoming increasingly important not only for confirming the results of randomised controlled trials, but also for identifying phenotypes, monitoring disease progression, predicting response to new drugs and early detection of rare side-effects. With new open-access technologies, it has become feasible to harmonise patient data from different disease registries and use it for data analysis without compromising privacy rules. Here, we provide a blueprint for how a clinical research collaboration can successfully use real-world data from existing disease registries to perform federated analyses. We describe how the European severe asthma clinical research collaboration SHARP (Severe Heterogeneous Asthma Research collaboration, Patient-centred) fulfilled the harmonisation process from nonstandardised clinical registry data to the Observational Medical Outcomes Partnership Common Data Model and built a strong network of collaborators from multiple disciplines and countries. The blueprint covers organisational, financial, conceptual, technical, analytical and research aspects, and discusses both the challenges and the lessons learned. All in all, setting up a federated data network is a complex process that requires thorough preparation, but above all, it is a worthwhile investment for all clinical research collaborations, especially in view of the emerging applications of artificial intelligence and federated learning.
Source: ERJ Open Research
Institutions: Medisch Centrum Leeuwarden, European Respiratory Society, NIHR Southampton Respiratory Biomedical Research Unit, University of Southampton, University of Copenhagen, Bispebjerg Hospital, European Lung Foundation, Amsterdam UMC Location University of Amsterdam, Amsterdam University Medical Centers, University of Amsterdam, Leiden University Medical Center
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $808
OpenAlex ID: W4288437344
