Life sciences companies operate in one of the most strictly regulated industries and as such are pioneers in adopting new technologies. Real-world data plays a crucial role in the success of these technologies, however high quality data is either scarce, expensive, or restricted. The sheer volume of data and the complexity in ensuring that it is ethical and meets regulatory compliance for use in prediction models has been a major impediment for innovation.
The Indiana Bioscience Research Institute (IBRI), the Cardinal Health Fuse Innovation Center, and Virtusa formed a collaborative project in the fall of 2018 to address the woes in using real-world data. This paper discusses the synthesis of high-fidelity population-level electronic medical record (EMR) data using Proxi (a simulated population-level data generator) on vLife‚ (Virtusa's open innovation platform) to create prediction models around Type 2 Diabetes as defined by researchers in IBRI.
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