Evidence map›Paper›PMID 40661700›Full record

ArticleRisk management and healthcare policy2025

Improving Pressure Steam Sterilization Quality Through Healthcare Failure Mode and Effects Analysis: A Pre-Post Intervention Study in Central Sterile Supply Departments.

Yanrong Zhang, Ruixue Hu, Yanhua Chen, Xiaoxiao Liu, Jin Wu, Liangying Yi

Abstract read
In one paragraph

Article in Risk management and healthcare policy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

6 authors.

Yanrong ZhangDepartment of Sterile Processing Nursing, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China.
Ruixue HuDepartment of Sterile Processing Nursing, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China.
Yanhua ChenDepartment of Sterile Processing Nursing, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China.
Xiaoxiao LiuDepartment of Sterile Processing Nursing, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China.
Jin WuDepartment of Sterile Processing Nursing, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China.
Liangying YiDepartment of Sterile Processing Nursing, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China.ORCID 0000-0001-9721-1194

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To evaluate the effectiveness of Healthcare Failure Mode and Effects Analysis (HFMEA) in reducing quality defects during pressure steam sterilization in the Central Sterile Supply Department (CSSD). Methods: The study followed a structured HFMEA framework: (1) A multidisciplinary team (n=7) with CSSD expertise was established to analyze sterilization workflows, including instrument scanning, sterilization verification, and post-sterilization cooling. (2) Process mapping and risk prioritization were conducted using a 4-level severity/occurrence matrix (adapted from Australian clinical risk criteria) to calculate Risk Priority Numbers (RPN=Severity×Occurrence). High-risk failure modes (RPN≥8 or severity=4) were identified, including unlabeled "non-sterilized" packages (due to incomplete scanning), wet packages (from insufficient cooling<30 minutes), and unverified sterilization information. (3) Root causes were analyzed via fishbone diagrams (human, machine, material, environment, method). Targeted interventions included: optimizing the traceability system with department-specific alerts, standardizing scanning protocols, staff retraining on verification procedures, increasing instrument inventory and sterilizer racks, and implementing performance monitoring with 5W1H checklists. Results: Pre-intervention, 87 defects were identified among 185,382 sterilization packages (32 unlabeled "non-sterilized", 10 wet packages). Post-intervention, defects decreased to 11/189,531 packages (χ²=115.556, P<0.001), including 4 unlabeled (χ²=374.951, P<0.001) and 2 wet packages (χ²=8.889, P=0.003). Conclusion: Systematic HFMEA application reduced sterilization defects by addressing critical workflow gaps, demonstrating its value in enhancing CSSD quality control and patient safety.

Indexed as

central sterile supply departmenthealthcare failure mode and effects analysispressure steam sterilizationquality defects

Identifiers

PMID40661700
PMCPMC12258196

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.