Journal study evaluates success of automated machine learning system to prevent medication prescribing errors

OAKBROOK TERRACE, Ill., Dec. 27, 2019 (GLOBE NEWSWIRE) -- Prescription drug errors are a leading source of harm in health care, resulting in substantial morbidity, mortality and health care costs estimated at more than $20 billion annually in the United States.1
Currently, clinical decision support (CDS) alerting tools a?? computerized alerts and reminders a??A are widely used to identify and reduce medication errors. However, CDS systems have a variety of limitations, including that they are rule based and can identify only medication errors that have been previously identified and programmed into the alerting logic.A new study published in the January 2020 issue of The Joint Commission Journal on Quality and Patient Safety used retrospective data to evaluate the ability of a machine learning system a?? a platform that applies and automates advanced machine learning algorithms a?? to identify and prevent medication prescribing errors not previously identified by and programmed into the existing CDS system.In the study, a??Using a Machine Learning System to Identify and Prevent Medication Prescribing Errors: A Clinical and Cost Analysis Evaluation,a?? alerts were generated retrospectively by a machine learning system using existing outpatient data from Brigham and Womena??s Hospital and Massachusetts General Hospital in Boston from 2009 through 2013.The study analyzed whether the system generated clinically valid alerts and its estimated cost savings associated with potentially prevented adverse events. These alerts were compared to alerts in the CDS system, using a random sample of 300 alerts selected for medical record review.
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