Harvard study of almost 800k lives shows MedAware technology reduces medication error

By Heather Mack
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Researchers at Harvard Medical School have demonstrated a new technology can help reduce a widespread problem that harms some 1.5 million people every year – medication error.

In a study published in the Journal of American Medical Informatics Association, researchers looked at five years of EHR-provided clinical data for 747,985 lives at the Brigham and Women’s Hospital and the Center for Patient Safety and Practice, plus retrospective data from Partners HealthCare BWH and Massachusetts General Hospital. Using the algorithmic software from Israel-based startup MedAware, researchers screened the data to detect outliers suggestive of potential medication errors and compared with an existing clinical decision support screening system.

Existing CDS screening systems can only detect a small number of actual errors, the researchers stated, because they are not patient-specific or sufficiently adaptable. Plus they often result in high false alarm rates. Since doctors get so many fake alerts, they begin disregarding them altogether, and the result is more than $20 billion in healthcare losses every year in the United States.

Using MedAware, researchers got alerts for over 15,000 charts, and they then looked at a sample of 300 charts, finding that 75 percent of alerts were valid in identifying potential medication errors. Unlike existing CDS systems, MedAware generates alerts based on screening for outliers.

“It has been hard to find medication errors which come completely out of the blue -- like a medication used only in pregnant women which is ordered for an elderly male -- but this approach detects orders which appear to be anomalous in some way, and it represents a very exciting new way to pick these errors up before they get to the patient,” Dr. David Bates, a professor at Harvard and expert on patient safety, said in a statement.

Of the valid alerts, 75 percent of them were for potentially life-threatening prescription errors, giving researchers a validation of of MedAware’s probabilistic, machine-learning approach (provided it is based on high-quality, complete underlying data).

“There is a need for innovative solutions to address the current prescription errors that result in substantial morbidity, mortality, and wasteful healthcare cost,” Dr. Ronen Rozenblum, a professor at Harvard Medical and a co-investigator on the study, said in a statement. “MedAware’s medication error detection system appears to have the ability to generate novel alerts that might otherwise be missed with existing clinical decision support systems.”