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Making sense of Big Data

By Claire Thayer, August 5, 2013

A recent article on big data published McKinsey & Company, The big-data revolution in US health care: Accelerating value and innovation, finds that many innovative US health-care data applications are moving beyond retroactive reporting to interventions and predictive capabilities.  Since 2010, more than 200 new businesses have developed innovative health-care applications. About 40 percent of these were aimed at direct health interventions or predictive capabilities.

Last week, Optum published a two-part series on big data.  Don James, Director of Product Management, Risk Adjustment Solutions at Optum, suggests that a comprehensive analytics approach presents a framework for helping health plans to clinically evaluate members, collaborate with providers more strategically, and apply models to help gauge the impact of efforts to improve clinical effectiveness.  In this series,  Optum identifies three steps in developing an analytics’ approach: (1) access data sources; (2) aggregate and stratify the data; and (3) model the data.  Step 1, access data sources, is addressed in Living the Promise of Big Data Part I while steps two and three are specifically addressed in Living the Promise of Big Data Part II

Cutting edge predictive analytics and health care trends are highlighted each month in Predictive Modeling News, published by Health Policy Publishing.  The August 2013 edition, released today, features a lead article addressing predictive analytics in health care -- view page one of this issue here.  Additionally, here are a few big-data in health care related videos that are available for free on MCOL’s HealthShareTV site:

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