Case Study: Large Payer Improves Patient Stratification using Unstructured Big Data

Situation: A large payer needed to extract member-related data to improve their analysis of Congestive Heart Failure (CHF) populations from a mixture of unstructured formats held in a data lake. Extracted data needed to be integrated with conventional data warehousing and analytics approaches to support improved patient stratification.

Solution: Linguamatics implemented their I2E NLP platform in combination with an automation workflow to ingest data from Hadoop, process it, and load it into a data warehouse for analysis.

Success: Linguamatics demonstrated that I2E could be integrated into existing Hadoop and Netezza systems to enable insights from unstructured data to be used as part of risk stratification analytics for CHF. The I2E infrastructure is easily extended to support other diseases areas and risk factors, for example diabetes and obesity.

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