Spot the difference: comparing results of analyses from real patient data and synthetic derivatives
Randi E. Foraker, Sean Yu, Aditi Gupta, Andrew P. Michelson, José A. Soto, Ryan Colvin, Francis Loh, Marin H. Kollef, Thomas M. Maddox, Bradley Evanoff, Hovav Dror, Noa Zamstein, Albert M. Lai, Philip Payne
Abstract
BACKGROUND: Synthetic data may provide a solution to researchers who wish to generate and share data in support of precision healthcare. Recent advances in data synthesis enable the creation and analysis of synthetic derivatives as if they were the original data; this process has significant advantages over data deidentification. OBJECTIVES: To assess a big-data platform with data-synthesizing capabilities (MDClone Ltd., Beer Sheva, Israel) for its ability to produce data that can be used for research purposes while obviating privacy and confidentiality concerns. METHODS: We explored three use cases and tested the robustness of synthetic data by comparing the results of analyses using synthetic derivatives to analyses using the original data using traditional statistics, machine learning approaches, and spatial representations of the data. We designed these use cases with the purpose of conducting analyses at the observation level (Use Case 1), patient cohorts (Use Case 2), and population-level data (Use Case 3). RESULTS: > 0.05) between the synthetic derivative and the real data to draw the same conclusions. DISCUSSION AND CONCLUSION: This article presents the results of each use case and outlines key considerations for the use of synthetic data, examining their role in clinical research for faster insights and improved data sharing in support of precision healthcare.