A comprehensive method for the quantification of medication error probability based on fuzzy SLIM

Area of Science:

• Healthcare Management
• Patient Safety
• Risk Assessment

Background:

• Medication errors pose significant risks to patient health and safety.
• Effective management strategies are crucial for mitigating these risks in hospital settings.
• Existing methods for assessing medication error probability are limited.

Purpose of the Study:

• To develop a comprehensive and novel methodology for estimating the probability of medication errors within hospitals.
• To identify and prioritize key factors contributing to medication errors.
• To provide a practical framework for risk assessment in clinical practice.

Main Methods:

• Conducted an extensive literature review to identify factors influencing medication errors.
• Employed the Success Likelihood Index Methodology (SLIM) for probability calculation.
• Utilized Fuzzy Multiple Attributive Group Decision Making and Fuzzy Analytical Hierarchical Process for factor weighting and rating.
• Validated the framework through a case study in an emergency department.

Main Results:

• Identified 17 distinct factors affecting medication errors.
• Determined workload, patient safety climate, and fatigue as the most significant factors.
• The case study highlighted that tasks involving deciphering handwritten notes increase human error risk.
• The developed framework proved easy to administer and useful for risk assessment.

Conclusions:

• The novel framework offers a systematic approach to estimating medication error probability.
• Prioritizing factors such as workload and fatigue can enhance patient safety protocols.
• The methodology provides a valuable tool for healthcare institutions to proactively manage medication risks.