A Data-Driven Approach to Evaluate Barcode-Assisted Medication Preparation Alerts at a Large Academic Medical Center

The purpose of this study was to develop a data-driven process to analyze barcode-assisted medication preparation alert data with a goal of minimizing inaccurate alerts.

Medication preparation data for the prior three-month period was obtained from an electronic health record system. A dashboard was developed to identify recurrent, high-volume alerts and associated medication records. A randomization tool was used to obtain a prespecified proportion of the alerts to review for appropriateness. Alert root causes were identified by chart review. Depending on the alert's cause(s), targeted informatics build changes, workflow and purchasing changes, and/or staff education were implemented. The rate of alerts was measured postintervention for select drugs.

The institution averaged 31,000 medication preparation alerts per month. The "barcode not recognized" alert (13,000) was the highest volume over the study period. Eighty-five medication records were identified as contributing to a high volume of alerts (5,200/31,000), representing 49 unique drugs. Of the 85 medication records triggering alerts, 36 required staff education, 22 required informatics build changes, and 8 required workflow changes. Targeted interventions for 2 medications, resulted in reducing the rate of the "barcode not recognized" alert from 26.6% to 1.3% for polyethylene glycol and from 48.7% to 0% for cyproheptadine.

This quality improvement project highlighted opportunities to improve medication purchasing, storage, and preparation through development of a standard process to evaluate barcode-assisted medication preparation alert data. A data-driven approach can help identify and minimize inaccurate alerts ("noise") and promote medication safety.