Analyses of Original and Computationally-Derived Electronic Health Record Data: The National COVID Cohort Collaborative.

Foraker, Randi; Guo, Aixia; Thomas, Jason; Zamstein, Noa; Payne, Philip R O; Wilcox, Adam

BACKGROUND Computationally-derived ("synthetic") data can enable the creation and analysis of clinical, laboratory, and diagnostic data as if they were the original electronic health record (EHR) data. Synthetic data can support data sharing to answer critical research questions to address the COVID-19 pandemic. OBJECTIVE We aimed to (1) compare the results from analyses of synthetic data to those from original data, and (2) assess the strengths and limitations of leveraging computationally-derived data for research purposes. METHODS We used the National COVID Cohort Collaborative's (N3C) instance of MDClone, a big-data platform with data-synthesizing capabilities (MDClone Ltd., Beer Sheva, Israel). We downloaded EHR data from 34 N3C institutional partners, and tested three use cases, including (1) exploring the distributions of key features of the COVID-positive cohort; (2) training and testing predictive models for assessing the risk of admission among these patients; and (3) determining geospatial and temporal COVID-related measures and outcomes, and constructing their respective epidemic curves. We compared the results from synthetic data to those from original data using traditional statistics, machine learning approaches, and temporal and spatial representations of the data. RESULTS For each use case, the results of the synthetic data analyses successfully mimicked those of the original data such that the distributions of the data were similar and the predictive models demonstrated comparable performance. While the synthetic and original data yielded overall nearly the same results, there were exceptions which included an odds ratio on either side of the null in multivariable analyses (0.97 versus 1.01) and differences in the magnitude of epidemic curves constructed for zip codes with low population counts. CONCLUSIONS This paper presents the results of each use case and outlines key considerations for the use of synthetic data, examining their role in collaborative research for faster insights. CLINICALTRIAL