ArticleJournal of medical Internet research2021
The National COVID Cohort Collaborative: Analyses of Original and Computationally Derived Electronic Health Record Data.
Article in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Translation readiness of model-based synthetic tabular data in healthcare: a systematic review and governance audit.Journal of the American Medical Informatics Association : JAMIA · 2026Pooled it
- Privacy and Security Throughout the Health Data Life Cycle: From Primary Care to Research Networks.Annual review of biomedical data science · 2026Review
- Increased risk of knee osteoarthritis progressing to total knee arthroplasty following patella fractures: an age stratified population analysis.European journal of orthopaedic surgery & traumatology : orthopedie traumatologie · 2026Article
- Crossing borders securely: synthetic data and federated networks for privacy-preserving access to real-world data and emerging use cases.NPJ digital medicine · 2025Review
- A consensus privacy metrics framework for synthetic data.Patterns (New York, N.Y.) · 2025Article
- An evaluation of the replicability of analyses using synthetic health data.Scientific reports · 2024Article
- Synthetic data in cancer and cerebrovascular disease research: A novel approach to big data.PloS one · 2024Article
- SynTwin: A graph-based approach for predicting clinical outcomes using digital twins derived from synthetic patients.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2024Article
- Synthetic Health Data Can Augment Community Research Efforts to Better Inform the Public During Emerging Pandemics.medRxiv : the preprint server for health sciences · 2023Article
- Synthesize high-dimensional longitudinal electronic health records via hierarchical autoregressive language model.Nature communications · 2023Article
- Synthesize Extremely High-dimensional Longitudinal Electronic Health Records via Hierarchical Autoregressive Language Model.Research square · 2023Article
- Validating a membership disclosure metric for synthetic health data.JAMIA open · 2022Article
- Demonstrating an approach for evaluating synthetic geospatial and temporal epidemiologic data utility: results from analyzing >1.8 million SARS-CoV-2 tests in the United States National COVID Cohort Collaborative (N3C).Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Demonstrating an approach for evaluating synthetic geospatial and temporal epidemiologic data utility: Results from analyzing >1.8 million SARS-CoV-2 tests in the United States National COVID Cohort Collaborative (N3C).medRxiv : the preprint server for health sciences · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
backgroundComputationally derived ("synthetic") data can enable the creation and analysis of clinical, laboratory, and diagnostic data as if they were the original electronic health record data. Synthetic data can support data sharing to answer critical research questions to address the COVID-19 pandemic.
objectiveWe aim to compare the results from analyses of synthetic data to those from original data and assess the strengths and limitations of leveraging computationally derived data for research purposes.
methodsWe used the National COVID Cohort Collaborative's instance of MDClone, a big data platform with data-synthesizing capabilities (MDClone Ltd). We downloaded electronic health record data from 34 National COVID Cohort Collaborative institutional partners and tested three use cases, including (1) exploring the distributions of key features of the COVID-19-positive cohort; (2) training and testing predictive models for assessing the risk of admission among these patients; and (3) determining geospatial and temporal COVID-19-related measures and outcomes, and constructing their 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.
resultsFor 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. Although the synthetic and original data yielded overall nearly the same results, there were exceptions that included an odds ratio on either side of the null in multivariable analyses (0.97 vs 1.01) and differences in the magnitude of epidemic curves constructed for zip codes with low population counts.
conclusionsThis 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.