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Demystifying NLP: Successful Use Cases in Healthcare

November 7, 2017 RISE Webinar Recording

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60+ Sponsors. 32+ Sessions. 4 Tracks. 3 Days.

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A Successful Conference Starts with Us

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$229

Incremental Revenue Per Member

4X

Coder Productivity

Download the Case Study

Peer Perspective: UPMC Case Study

Mary Beth Jenkins, SVP & COO at UPMC HEALTH PLAN and Dr. Adele Towers, MPH at UPMC Enterprises will explain the challenges their plan was facing in risk adjustment for their MA and Exchange populations. Together, they'll discuss how they overcame several challenges and improved plan performance.

Leave with key takeaways and lessons learned that will enable your organization to succeed in similar circumstances.

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Leave with key takeaways and lessons learned that will enable your organization to succeed in similar circumstances.

Webinar Highlights

Listen as Mary Beth Jenkins, SVP & COO at UPMC Health Plan & Dr. Adele Towers, MPH at UPMC Enterprises explain the risk adjustment challenges their plan was facing in their MA and Exchange populations. Together, they discuss how applying new technology improved performance.

Key Presentation Points:

How UPMC Health Plan addressed their MA and ACA challenges with Natural Language Processing (NLP) technology
Pre- and post-technology enabled coding workflow comparisons, NLP parsing example and data ingestion process that transformed risk adjustment operations
Health plan results, including:
- Substantial increase in RAF capture
- Visibility into potential compliance issues/audit risks
- 4X gain in coder productivity

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Webinar Highlights

Join Health Fidelity Founder, Anand Shroff, as he discusses how organizations that take advantage of advanced technologies such as natural language processing (NLP) and machine learning are successfully managing the transition to value-based care.

Learn how these technologies have been proven to accurately automate the analysis of clinical data and advance your understanding of how to evaluate the applicability of different engines to your specific use case.

Key Takeaways

How natural language processing technology is used to extract valuable insights from unstructured clinical narratives
Key criteria for evaluating different natural language processing engines offered in the market
Common misconceptions about cognitive computing technologies
Examples of how health care organizations have taken advantage of natural language processing and machine learning technology to improve clinical, financial, and operational outcomes

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