As the healthcare industry undergoes rampant merger and acquisition (M&A) activity, health systems are faced with the challenge of rapidly integrating data silos across hospitals to ensure continued quality of care and avoid revenue disruptions. In Health, Wolters Kluwer is now harnessing AI to bridge data in electronic health records (EHRs) and disparate systems, such as lab results, to improve how organisations identify and report hospitals acquired infections.
Wolters Kluwer’s data science team worked with Health Language® to use AI and machine learning to enhance the process of mapping lab results to fully standardised LOINC codes (Logical Observation Identifiers Names and Codes), a common challenge among hospitals and payers post-mergers or when aggregating data from disparate EHRs or systems. Manually mapping lab results to LOINC is error-prone and can take months to complete. AI reduced the process considerably and enabled Wolters Kluwer’s Sentri7® customers to accurately and quickly comply with reporting HAIs to the Centers for Disease Control and Prevention and to state health departments.
Beyond increasing the speed and accuracy of how healthcare organisations report HAIs, Wolters Kluwer is also applying AI to several other areas supporting patient engagement and clinical effectiveness.
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.
