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

Area of Science:

• Health Informatics
• Medication Safety
• Quality Improvement

Background:

• Barcode-assisted medication preparation systems generate numerous alerts.
• A high volume of alerts can be inaccurate, leading to alert fatigue and potentially impacting patient safety.

Purpose of the Study:

• To develop a data-driven process for analyzing barcode-assisted medication preparation alert data.
• To minimize inaccurate alerts and improve the overall efficiency of medication preparation systems.

Main Methods:

• Extracted three months of medication preparation alert data from an electronic health record system.
• Developed a dashboard to identify high-volume alerts and associated medication records.
• Implemented targeted interventions including informatics, workflow, and staff education based on root cause analysis.

Main Results:

• The institution averaged 31,000 alerts monthly, with "barcode not recognized" being the most frequent.
• Eighty-five medication records accounted for a significant portion of alerts, requiring various interventions.
• Targeted interventions reduced the "barcode not recognized" alert rate from 26.6% to 1.3% for polyethylene glycol and 48.7% to 0% for cyproheptadine.

Conclusions:

• A systematic, data-driven approach effectively identifies and reduces inaccurate medication preparation alerts.
• Optimizing medication purchasing, storage, and preparation processes through alert data analysis enhances medication safety.
• This quality improvement project demonstrates the value of analyzing alert data to minimize system noise and improve clinical workflows.