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

Drugs making a mess of your data?

Posted on 07/31/19 | Comments

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Topics: semantic interoperability, data normalization, interoperability, mapping, quality reporting, Natural Language Processing, Machine learning, clinical decision support, quality measure reporting, value-based care, enabling interoperability

Lab Mapping...what the LOINC?!

Posted on 07/10/19 | Comments

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

Raising the Bar on Semantic Interoperability and Data Quality

Posted on 06/26/19 | Comments

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

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

Financial Stability in a Fluid Market

Posted on 11/01/17 | Comments

Four Ways to Address Data Management Challenges and Strengthen Your Revenue Cycle

Revenue cycle management can leverage systems and workflows that close gaps, tie up loose ends, and ensure submission of a clean claim. Reference data—representing the coded and uncoded data used across a health system—plays an all-important role in strategies that optimize revenue cycle processes and ensure compliance with industry licensing requirements.

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Topics: revenue cycle management

Three Reasons DSM-5 Mapping Strategies Matter

Posted on 09/07/17 | Comments

The introduction of industry standards such as SNOMED CT, ICD-10, LOINC, and RxNorm is an important step toward achieving the goals of interoperability and information sharing. Yet healthcare organizations still face notable challenges to laying the best frameworks for normalizing data to these standards. Since there is no one standard that addresses all healthcare information, clinical and financial data must be “cleaned” and appropriately mapped to a single source of truth to remove semantic ambiguity.

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Topics: Standard terminologies, Analytics, mapping, DSM-5