A computerized algorithm has achieved promising outcomes in reconciling medications between clinical notes (unstructured data) and discharge prescriptions (structured data).
Free-text medication data is inaccessible to computerized reconciliation applications that rely on structured medication information, according to researchers at the Cincinnati Childrens Hospital Medical Center. As such, accurate and timely reconciliation during care transitions poses significant challenges to clinical care providers.
The study assessed performance of a computerized algorithm on real-world medication reconciliation dataclinical notes and discharge prescription lists for 271 patients enrolled in the Complex Care Medical Home Program at Cincinnati Childrens Hospital Medical Center. Researchers developed state-of-the-art machine learning (ML) and natural language processing (NLP) technologies as well as a computerized algorithm for medication reconciliation.
Results of the study, published in BMC Medical Informatics and Decision Making, demonstrate that the hybrid algorithm showed good capability in medication entity detection, attribute linkage and medication matching. We hypothesize that the computerized algorithm, when transferred to the production environment, will have potential for significant impact in reduction of effort for conducting medication reconciliation in the clinical practice setting, conclude researchers.
The full article is available here.
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