A cloud model and data envelopment analysis-enhanced FMEA framework for risk assessment in 3D-printed...

Area of Science:

• Radiotherapy and Medical Physics
• Risk Management and Quality Assurance
• Computational Modeling and Data Analysis

Background:

• Traditional Failure Mode and Effects Analysis (FMEA) is a common risk assessment tool in radiotherapy.
• The complexity of 3D-printed template-assisted intracavitary/interstitial brachytherapy (3DP-IC/IS) necessitates advanced risk management.
• Existing FMEA methods have mathematical and logical limitations that require addressing.

Purpose of the Study:

• To develop an advanced FMEA framework integrating cloud models and data envelopment analysis (DEA).
• To validate the performance of this enhanced FMEA framework against traditional FMEA in 3DP-IC/IS.
• To improve the identification and prioritization of failure modes in complex radiotherapy procedures.

Main Methods:

• A hybrid framework combining cloud models for linguistic assessment fuzziness and a modified DEA for multi-criteria decision-making was developed.
• The framework was applied to a dataset of 80 patients undergoing 3DP-IC/IS.
• Failure modes (FMs) were ranked using both the proposed and traditional FMEA methods for comparative analysis.

Main Results:

• The enhanced FMEA identified "Insufficient checking of needle labels" and "Excessive number of needles" as high-risk FMs.
• The framework improved risk-ranking discrimination, reducing score-based groupings.
• Four FMs were reclassified as high risk, indicating greater sensitivity to potentially underestimated risks compared to traditional FMEA.

Conclusions:

• A hybrid cloud model-DEA-FMEA framework effectively addresses limitations of traditional risk assessment.
• The framework is feasible and enhances the identification and prioritization of critical FMs in 3DP-IC/IS.
• This approach offers significant clinical value for quality assurance and patient safety in complex radiotherapy.