Semantic Data Normalization – A Foundational Component of Big Data Analytics and Population Health Management Initiatives

Complimentary Web Seminar
Available On-Demand
Brought to you by Health Data Management

Terminology is core to everything in healthcare—from procedures to results to diagnoses, healthcare IT systems (HIT) represent clinical concepts in various coded clinical terminologies or free text. Unfortunately, the explosive growth in HIT has resulted in patient data being scattered across an array of rapidly proliferating IT systems— each with their own way of representing clinical terms. This terminology barrier must be overcome if we are to recognize the national effort around increased interoperability, transparency, and collaboration within our healthcare system.

We will discuss why normalizing clinical and claims-based data into standard terminologies is critical in supporting forward-thinking initiatives such as big data analytics, population health management, and new delivery models that require semantic interoperability among systems.

Key Learning Objectives:

  • Map and translate localized content and unstructured text to standard terminologies.
  • Convert fragmented data that include labs, drugs and problems/diagnosis.
  • Explore potential benefits that include improving quality measure reporting, providing visibility on patients following treatment plans, improving care and disease management programs, and increase the accuracy of your decision support rules and real-time care alerts.


Jason D. Wolfson
VP of Product Management
Wolters Kluwer Health - Clinical Solutions

Brian Levy, MD
VP of Global Clinical Operations
Wolters Kluwer Health – Clinical Solutions

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