Wolters Kluwer, Health has expanded its Health Language® Interoperability and Data Normalization Solutions to include a suite of services designed to help payers, providers and health IT vendors leverage exponentially increasing volumes of clinical data. Health Language Data Normalization Services draws on deep terminology management knowledge and clinical expertise that is now powered by machine learning to reduce the time, resources and costs associated with harmonising data for interoperability and analytics initiatives.
Today’s healthcare organisations increasingly acquire vast amounts of clinical data from disparate sources such as EHRs, practice management systems, laboratories, and pharmacies – each system encoding labs, drugs, and other clinically significant information in a different way. To overcome these widespread interoperability barriers, data must be mapped to industry standards such as LOINC®, RxNorm and SNOMED CT® to support industry initiatives like quality measures reporting, clinical decision support as well as care and disease management programs.
Health Language combines unmatched industry expertise with advanced terminology tools and finely-tuned matching algorithms powered by machine learning. Wolters Kluwer clinical experts—each offering an average of more than 25 years of healthcare terminology experience—partner with healthcare organisations to analyze use cases, create a strategic approach for their unique data normalization challenges and efficiently map disparate data across domains such as labs, medications, allergies, problems, diagnoses and procedures.
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