Summary
Prescription drug errors are a leading source of harm in health care, resulting in substantial morbidity, mortality and healthcare costs estimated at more than $20 billion annually in the US.
Currently, clinical decision support (CDS) alerting tools – computerised alerts and reminders – 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 from Rozenblum et al., published in The Joint Commission Journal on Quality and Patient Safety, used retrospective data to evaluate the ability of a machine learning system – a platform that applies and automates advanced machine learning algorithms – to identify and prevent medication prescribing errors not previously identified by and programmed into the existing CDS system.
Content
The study analysed 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.
Findings showed a total of 10,668 alerts during the five-year period. Overall, 68.2% of the alerts would not have been generated by the existing CDS system. Ninety-two percent of a random sample of the chart-reviewed alerts were accurate based on structured data available in the record, and 80% were clinically valid. The estimated cost of adverse events potentially prevented in an outpatient setting was more than $60 per drug alert and $1.3 million when extrapolating the study’s findings to the full patient population.
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