Application research of sensory control management in operating room based on failure mode and effect analysis
In brief
FMEA-based early warning lowers OR high-risk infection scores by up to 79%
In a single-center pre-post study, applying Failure Mode and Effects Analysis with monthly Risk Priority Number tracking reduced the RPN of identified high-risk items by 43-79% and boosted compliance with cleaning, hand hygiene and skin preparation to over 85%. The approach improved infection-control processes, but its long-term impact and generalizability need multicenter trials.
- Journal
- Frontiers in public health (Q1)
- Published
- 31 July 2026
- Study design
- Randomized controlled trial
- Evidence level
- Level 1, High (CEBM 1b)
- Authors
- Haijing Cai, Yuehong Ren, Man Xu, Feng Yan, Jiatang Guo, Xinying Liu, et al.
- PMID
- 42602106
- DOI
- 10.3389/fpubh.2026.1826841
Why clinicians should know about it
- Picked for Surgery (top studies of the week, 16 August 2026): Ranked by evidence level and journal quartile
Abstract
OBJECTIVE: To explore the application effect of Failure Mode and Effects Analysis (FMEA) combined with Risk Priority Number (RPN) dynamic early warning in reducing the risk of hospital-acquired infections in the operating room, and to provide evidence-based support for optimizing infection control management strategies in the operating room. METHODS: This study adopted an analytical and descriptive comparative research design (a prospective pre-post design) and was conducted in the clean operating room of a tertiary hospital from July to December 2023. A FMEA management team consisting of 10 members, including dedicated infection control personnel and members of the operating room infection control group, was formed. Risk identification was carried out using the brainstorming method from the aspects of personnel, equipment, material, method, environment, and monitor. A three-level scoring system (1-10 points) for the occurrence probability (O), severity (S), and detectability (D) of risks was developed based on literature review and clinical practice. The RPN score (RPN = O × S × D) was calculated monthly, and high-risk items were defined as those above the 80th percentile. Targeted interventions were implemented, and the infection control process in the operating room was continuously optimized to improve the quality of infection control management. RESULTS: A total of 49 risk points were identified, among which 10 were high-risk items. After the intervention, the RPN score of high-risk items decreased significantly, with an improvement rate ranging from 42.86 to 78.57%. The implementation rate of room cleaning and disinfection increased from 51.0 to 85.0% (P < 0.05), the correct rate of surgical hand disinfection increased from 66.7 to 90.7% (P < 0.05), the compliance rate of hand hygiene increased from 56.7 to 73.6% (P < 0.05), the standardization rate of skin disinfection in the surgical area increased from 62.5 to 90.6% (P < 0.05), and the standardization rate of skin preparation increased from 71.4 to 95.3% (P < 0.05). The pass rate of pre-treatment of surgical instruments increased from 70.8 to 87.5%, but the difference was not statistically significant (P > 0.05). CONCLUSION: The combination of FMEA and RPN dynamic early warning can effectively identify high-risk links of infection in the operating room. Through and improve the quality of infection control management. This study si a single-center pre-post comparison design. It is recommended that future multi-center randomized controlled trials be conducted to verify its long-term effect.
Abstract as published, via PubMed.
For healthcare professionals. The summary is generated by AI from the published abstract, and the evidence level is assigned automatically from the study design on the Oxford CEBM hierarchy. Neither is medical advice. Read the full paper before changing practice.