One of the exciting trends in healthcare data science is the ability for healthcare payers to use not only structured data to make decisions that impact population and individual member health, but also raw data from healthcare transactions, physicians’ notes, digital health applications, and more.
Not surprisingly, the concept of “data lakes” is attracting a lot of attention from payers, a group with massive amounts of raw data at its disposal. Data lakes store both structured and unstructured data without the aid of expensive computer infrastructure. Although most healthcare data flows are traditionally unidirectional, a true data lake receives data from transactional systems as well as returns data to those systems to support and enhance decision making.
Cotiviti’s Sumant Rao, senior vice president and business owner of performance analytics, explores the root causes of data lake failure among healthcare payers and offers seven major considerations for those considering a data lake approach.
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