Application of human reliability analysis to nursing errors in hospitals.

Area of Science:

• Healthcare Quality and Safety
• Medical Informatics
• Risk Management

Background:

• Adverse events in hospitals are a global concern, necessitating effective tools for analyzing medical incident reports.
• Current analytical tools lack the sophistication to handle large volumes of medical incident data.
• Understanding organizational factors is crucial for preventing medical errors.

Purpose of the Study:

• To develop and validate a novel model for analyzing medical incident reports.
• To facilitate quantitative risk assessment and identify underlying organizational factors in medical errors.
• To improve decision-making processes for necessary actions in healthcare settings.

Main Methods:

• Developed a unique coding system with seven vectors for classifying medical incidents.
• Defined medical tasks as module practices and calculated error rates and element weights mathematically.
• Applied the model to analyze 5,339 incident reports from nursing practices across six hospitals over one year.
• Ensured model legitimacy through quality assurance checks on practice quantities and analysis reproducibility.

Main Results:

• Error rates for module practices were consistently around 10⁻⁴ across all hospitals.
• Identified "violation of rules" (weight: 826 x 10⁻⁴) as the primary organizational factor.
• "Failure of labor management" (weight: 661 x 10⁻⁴) and "defects in the standardization of nursing practices" (weight: 495 x 10⁻⁴) were also significant factors.

Conclusions:

• The developed model effectively identifies organizational factors contributing to medical errors.
• The findings highlight critical areas for intervention to improve patient safety in hospitals.
• This quantitative approach provides a robust framework for risk assessment and quality improvement in healthcare.