Theory-Based Failure Modes and Effect Analysis for Medication Errors.

Area of Science:

• Health Informatics
• Decision Science
• Risk Management

Background:

• Medication errors (MEs) pose significant challenges in healthcare, particularly in non-automated systems.
• Existing qualitative studies often lack robust methods for handling uncertainty in decision-making for ME analysis.
• Expert-based decisions in ME risk assessment can yield unrealistic results due to inherent uncertainties.

Purpose of the Study:

• To develop a hybrid decision-making approach for weighting risk factors and ranking medication errors (MEs) within the Failure Modes and Effect Analysis (FMEA) framework.
• To incorporate the concept of reliability and address uncertainty in the ME risk assessment process.
• To present a novel methodology combining Z-numbers with SWARA and WASPAS for enhanced ME analysis.

Main Methods:

• Identification of significant MEs by a panel of doctors, nurses, and pharmacists.
• Application of the Z-SWARA method to assign weights to risk factors, integrating reliability using Z-numbers.
• Ranking of identified MEs using the proposed Z-WASPAS method, which accounts for uncertainty.

Main Results:

• The study successfully demonstrates the applicability of the Z-SWARA and Z-WASPAS methods for ranking MEs.
• Improper medication administration was identified as the primary failure mode, posing risks of fatality or patient injury.
• The proposed approach provides a more reliable assessment compared to traditional methods like fuzzy-WASPAS and FMEA.

Conclusions:

• The integration of multiple-criteria decision-making methods with Z-numbers offers a powerful tool for healthcare management.
• This approach effectively addresses the challenges of reliability and uncertainty in analyzing complex healthcare risks like medication errors.
• The findings underscore the critical need for improved medication administration practices to enhance patient safety.