Health Language Blog

Webinar Recap: Leveraging AI to Solve Common Healthcare Challenges: Hear from the Experts

Posted on 08/07/19 | Comments

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Topics: semantic interoperability, data normalization, interoperability, mapping, quality reporting, Natural Language Processing, Reference Data Management, Machine learning, clinical decision support, quality measure reporting, value-based care, enabling interoperability, clinical natural language processing, patient risk, chart review, cnlp

Webinar Recap: How Quality Data is Key to Delivering Value

Posted on 06/13/19 | Comments

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Topics: data normalization, interoperability, mapping, quality reporting, NLP, Natural Language Processing, Reference Data Management, artificial intelligence, Machine learning, clinical decision support, quality measure reporting, value-based care, clinical and claims data, enabling interoperability

Webinar Recap: Quality Data: Three Steps to Simplify Data Governance, Enable Semantic Interoperability, and Enhance Your Reporting and Analytics

Posted on 05/30/19 | Comments

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Topics: data normalization, interoperability, mapping, quality reporting, NLP, Natural Language Processing, Reference Data Management, artificial intelligence, Machine learning, clinical decision support, quality measure reporting, value-based care, clinical and claims data, enabling interoperability

Webinar Recap: Applying AI in healthcare: Challenges, opportunities, and emerging applications

Posted on 11/19/18 | Comments

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Topics: NLP, Natural Language Processing, Reference Data Management, artificial intelligence, Machine learning

Breaking Down the Data Silos: How reference data is the cornerstone of an overall data management strategy

Posted on 05/09/18 | Comments

Providers and payers face growing pressure to control costs, increase profitability, and succeed with value-based care. Healthcare executives must turn to data as their strongest ally to inform decision-making and drive performance improvement. Yet many organizations fail to extract the full value of their data assets due to fragmented operations, ineffective data governance, and IT structural limitations that create data silos across an enterprise.

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Topics: Reference Data Management