A cloud model and data envelopment analysis‐enhanced FMEA framework for risk assessment in 3D‐printed template‐assisted brachytherapy

Background Failure mode and effects analysis (FMEA) is a widely used proactive tool for risk assessment in radiotherapy. With the introduction of 3D‐printed template–assisted intracavitary/interstitial brachytherapy (3DP‐IC/IS), the increasing procedural complexity and newly introduced steps highlight the need for systematic risk management. However, the traditional FMEA approach has been criticized for its mathematical and logical limitations. Purpose To develop an enhanced FMEA framework using a cloud model and data envelopment analysis (DEA), and to validate its performance through a comparative risk assessment against the traditional FMEA method in 3DP‐IC/IS. Methods The analysis was performed on a dataset from 80 patients who underwent 3DP‐IC/IS. The proposed framework incorporates a cloud model to manage the fuzziness and randomness of linguistic risk assessments and a modified DEA model for multi‐criteria decision‐making. Both the proposed and traditional FMEA methods were used to rank failure modes (FMs) in the 3DP‐IC/IS workflow. Risk priority numbers and model‐derived efficiency scores were calculated to identify high‐priority FMs, and the ranking results from both methods were compared to evaluate performance. Results A total of 66 FMs were analyzed. The average rank shift between the traditional and introduced FMEA was 4.39 (max: 17, min: 0). Among the top 20 FMs, 18 were consistent between the two methods. “The introduced FMEA uniquely identified “Insufficient checking of needle labels” and “Excessive number of needles” as high risk.” The proposed framework also increased risk‐ranking discrimination, reducing the number of FMs with identical scores from 12 groups to a single group. The two highest risk FMs—“Unreasonable needle track design” and “Error in identifying non‐coplanar needle number”—were consistently ranked first by both methods. Notably, the proposed framework reclassified four FMs as high risk, which demonstrated improved sensitivity to risks potentially underestimated by traditional FMEA. Conclusion We developed a hybrid cloud model‐DEA‐FMEA framework that mitigates key limitations of traditional risk assessment. When applied to 3DP‐IC/IS, this framework demonstrated both feasibility and an enhanced ability to identify and prioritize critical FMs. Our findings highlight its clinical value for improving quality assurance and patient safety, especially in complex, high risk radiotherapy procedures.