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Wolters Kluwer partners with SAS to accelerate real-world evidence analytics in life sciences with health language data quality solutions -

Wolters Kluwer, Health has announced a partnership with SAS to leverage its Health Language portfolio of data quality solutions to power real-world evidence (RWE) analytics within SAS® Health, a leading industry analytics solution. The integration of Wolters Kluwer Health Language data quality solutions in SAS Health allows the harmonisation of information aggregated from an array of real-world sources and establishes a foundation of regulatory-grade data quality to drive the use of RWE in life sciences.

According to Research and Markets, the RWE market is expected to reach $1.64 billion by 2024. This is due to its potential for improving the understanding of treatment effects, disease burden, patient safety and indications for use, as well as accelerating and optimizing clinical trials. Derived from real-world data sources such as electronic health records (EHRs), patient disease registries, wearables, genomic datasets, medical claims registries and social determinants of health (SDOH), RWE depends on clean data codified to industry standards for analytics.

Wolters Kluwer’s Health Language solution provides a comprehensive approach to improving the quality of real-world data for the life sciences sector. Through a disease-centric approach, Health Language will enable SAS Health’s clients to more effectively manage reference data, such as ICD-10, CPT®, and SNOMED CT, establish relationships between clinical and claims data and extract value from unstructured text within the medical record–providing a comprehensive view of a patient’s clinical journey. The Health Language team combines unmatched industry expertise with advanced terminology tools and proven results, helping clients accelerate the time-to-insights by transforming real-world data into evidence.

Brought to you by Scope e-Knowledge Center, a trusted global partner for digital content transformation solutions - Abstracting & Indexing (A&I), Knowledge Modeling (Taxonomies, Thesauri and Ontologies), and Metadata Enrichment & Entity Extraction.

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