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Making Large-Scale, Diverse Healthcare Data Clean, Actionable and Analyzable

The amount and variety of electronic healthcare data continue to grow at an exponential rate. This creates an expanded opportunity to leverage analytics, machine learning and artificial intelligence to harness this information and make it meaningful and actionable, bringing solutions to complex diagnostic, treatment, and delivery problems that were previously unapproachable. Optimal use of integrated data can also lead to solutions for improved care quality, health policy impact monitoring, supply chain, reportable condition handling, and better overall provider and patient experience. However, healthcare data comes from disparate sources, in different formats, using different value sets, and often containing missing, misplaced, duplicated, or otherwise incorrect information. Many industry articles note that healthcare data scientists spend more time cleaning up the data than they spend using it to gain new insights. Dr. Jonathan Teich speaks on producing large-scale clean data from diverse sources, and how it helps facilitate more usable and actionable analytics, case monitoring, event notification, and improved insight at the patient and population levels. Examples from public health, provider, payer and regulatory settings will be included.

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