Combining structured, unstructured EHR data improves medication reconciliation

Using structured and unstructured data in an EHR together can improve medication reconciliation and detect discrepancies between medication orders and patient adherence, according to a study published inBMC Medical Informatics and Decision Making.

Researchers built a computerized algorithm for medication reconciliation using machine learning and natural language processing. They developed a program that could compare medication information from patient discharge orders (structured data) to clinicians' notes (unstructured data). They used data on patients in the Complex Care Medical Home Program at Cincinnati' Children's Hospital Medical Center to assess the algorithm.

The algorithm was effective and achieved "much improved recall" on matched and discrepant medications and in detecting discrepant medications in the discharge summaries. There were some errors in detection and mismatching such as an abbreviation, a misspelling or used an uncommon medication name in the clinical notes that the algorithm did not match to the discharge summary.

"Even at this early stage of development, automated medication discrepancy detection shows a promising outcome in assisting medication reconciliation," the researchers wrote. "Consequently, 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."

Read the complete study.