High-fidelity synthetic patient data applications and privacy considerations

Puja Myles, Colin Mitchell, Elizabeth Redrup Hill, Luca Foschini and Zhenchen Wang
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Puja Myles: Director, Clinical Practice Research Datalink, Medicines and Healthcare Products Regulatory Agency, UK
Colin Mitchell: Head of Humanities, PHG Foundation, University of Cambridge, UK
Elizabeth Redrup Hill: Senior Policy Analyst (Law and Regulation), PHG Foundation, UK
Luca Foschini: President, Sage Bionetworks, USA
Zhenchen Wang: Head of Data Analytics and Machine Learning, Scientific Data and Insights, Medicines and Healthcare products Regulatory Agency (MHRA), UK

Journal of Data Protection & Privacy, 2024, vol. 6, issue 4, 344-354

Abstract: This paper explores the potential applications of high-fidelity synthetic patient data in the context of healthcare research, including challenges and benefits. The paper starts by defining synthetic data, types of synthetic data and approaches to generating synthetic data. It then discusses the potential applications of synthetic data in addition to as a privacy enhancing technology and current debates around whether synthetic data should be considered personal data and,therefore, should be subjected to privacy controls to minimise reidentification risks. This will be followed by a discussion of privacy preservation approaches and privacy metrics that can be applied in the context of synthetic data. The paper includes a case study based on synthetic electronic healthcare record data from the Clinical Practice Research Datalink on how privacy concerns due to reidentification have been addressed in order to make this data available for research purposes. The authors conclude that synthetic data, particularly high-fidelity synthetic patient data, has the potential to add value over and above real data for public health and that it is possible to address privacy concerns to make synthetic data available via a combination of privacy measures applied during the synthetic data generation process and post-generation reidentification risk assessments as part of data protection impact assessments.

Keywords: synthetic patient data; re-identification risk; privacy metrics; CPRD; data governance; differential privacy (search for similar items in EconPapers)
JEL-codes: K2 (search for similar items in EconPapers)
Date: 2024
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