Automated system for identifying potential dosage problems at a large university hospital

Area of Science:

• * Clinical Informatics
• * Pharmaceutical Sciences
• * Health Systems Engineering

Background:

• * Manual screening of drug orders for dosage appropriateness is time-consuming and prone to errors.
• * Ensuring correct drug dosages, especially for patients with impaired renal function, is critical for patient safety.
• * Developing automated systems can enhance the efficiency and accuracy of medication safety checks.

Purpose of the Study:

• * To describe the implementation and impact of an automated drug dosage screening system called DoseChecker.
• * To evaluate the system's effectiveness in identifying potential medication dosage errors.
• * To assess the rate of dosage adjustments made based on system alerts.

Main Methods:

• * Development of DoseChecker by integrating pharmacy, laboratory, and demographic data.
• * System rules identified targeted medications, evaluated dosages, and generated alerts for potential adjustments.
• * Pharmacists reviewed alerts and communicated recommendations to physicians for patient care.

Main Results:

• * DoseChecker screened 28,528 drug orders, detecting dosage problems in 10% (2859 cases).
• * 70% of alerts recommended lower doses, 30% recommended higher doses.
• * Physicians agreed to dosage adjustments in 75% (868 cases) of pharmacist-initiated interventions.

Conclusions:

• * The automated DoseChecker system efficiently identified inappropriate drug dosages in a large university hospital.
• * Failure to adjust dosages for renal function was the most common issue detected.
• * Automated screening significantly improved medication safety by facilitating timely dosage corrections.