The availability of real-world data (RWD) has undoubtedly revolutionized the time, cost, and utility of analytics in healthcare. However, the insight we expect to be inherently available from RWD remains elusive. Why might this be? Well, by “big data” in healthcare, what most people mean is “transactional” data that is generated from individual healthcare encounters like filling a prescription or visiting a doctor.
And while this transactional RWD has become critical in letting us know “what” is happening to patients, AI and other advanced analytics are going to struggle to answer “why” patients have different experiences and outcomes because we are missing a key ingredient: contextual data.
I lay out our argument for what is required to fill this gap in a Medium post I published today, Healthcare AI Needs Contextual Data.
And thank you to the folks who provided thoughtful commentary in creating this piece, including the Veritas Data Research team, Kevin McCurry, Leonid Flek, Auren Hoffman, Gillian Cannon, Gaurav Singal, MD, Vera Mucaj, Helen Moran, Andrew Kress, Robert Nagel, Bobby Samuels, Travis May, and Shahir Kassam-Adams.
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