Health Language Blog

Are You Allergic to Messy Data?

Posted on 08/21/19 | 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

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

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

Meaningful Analytics: Improving Data Integration and Quality by Leveraging the HIE Webinar Recap

Posted on 11/29/16 | Comments

Healthcare organizations must achieve mastery of high-quality data and analytics to thrive within value-based care models. Today’s IT professionals are challenged to design systems that improve data exchange with industry stakeholders as well as acquire more complete and accurate patient information for quality measures reporting. Without a strategy in place that addresses each of these key areas, hospitals and health systems face significant barriers to achieving their overall population health or financial goals.

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Topics: Analytics, HIE, cohort rules management, mapping, CMS, data integration, data quality, APM, VITL

Data Normalization: Mapping to Existing Standards vs Creating Local Standards

Posted on 05/11/16 | Comments

If you’ve followed the previous steps outlined in this blog series, you should have a good idea of how to pursue data normalization within your healthcare organization. But challenges will continue to arise once you get underway. One of the decisions a healthcare provider or payer will face in the course of a project is whether to use an existing industry standard or create a local terminology. As it turns out, the answer isn’t cut and dried, and will depend to a large degree on the use case involved.

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Topics: data normalization, selecting standards, mapping, local standards