The Appeal and Real-Life Consequences of Applying Synthetic Data to Sensitive Clinical Data
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Jasmine Lee Jasmine Lee
The Appeal and Real-Life Consequences of Applying Synthetic Data to Sensitive Clinical Data
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Even before the COVID-19 crisis, health systems, medical researchers, and medical institutions grappled with efficient ways of gathering patient data while maintaining patient privacy.

When researching for health innovation or crisis management, healthcare institutions must extract data from a multitude of systems. Answering questions about trends in chronic conditions, the viability of a treatment in a community, the utilization rates of certain procedures, or the rising costs of health care-all of these scenarios require collecting, analyzing, and sharing patient and population data.

Unfortunately, that process is fraught with possible data breaches, navigation of industry privacy regulation, dependence on healthcare IT specialists, and precious time. On top of that, compiling and researching patient data requires navigation through massive troves of data that may exist in a variety of systems that are siloed or frustratingly dispersed across differing archives.

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