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Study on the effect of cleaning da Vinci patient side manipulator with steam cleaner

ObjectiveThis study compares the cleaning effect of patient side manipulators (PSMs) through manual cleaning and steam cleaning.MethodsA total of 2 400 contaminated da Vinci PSMs collected from September 2021 to September 2022 were randomly divided into experimental and control groups, each with 1 200 pieces. The control group adopted manual cleaning, and the steps were rinsing with running water, rinsing with a spray gun, bio-washing, ultrasonic cleaning, rinsing and repeating, drying, and sterilization. The experimental group added steam cleaning between the ultrasonic cleaning and the rinsing and repeating, and other steps remained unchanged. The qualification rates of the two groups were recorded and the damage rates of PSMs during the cleaning process were counted.ResultsThe results of visual inspection and adenosine triphosphate (ATP) bioluminescence assay showed that the qualification rate of the experimental group was higher than that of the control group (P<0.05), and the damage rate of PSM in the experimental group was lower than that in the control group (P<0.05).ConclusionCleaning PSM with steam cleaner significantly reduces the re-cleaning and damage rates and improves cleaning quality.

Xia Liu, Wei Liu, Fangfang Zhou, Chunyan Zhou, Hong Chen

article 6 Feb 2026

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Volume 5  Issue 2，2026 2026年第5卷第2 Issue

  • 标准指南解读

    China Health Inspection Association

    DOI：10.11910/j.issn.2791-2043.2026.2.01
      
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    Chinese Research Hospital Association

    DOI：10.11910/j.issn.2791-2043.2026.2.02
      
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  • 综述论著

    Huan Tan, Wenfang Zhou, Piao Tao, Lingyun Zhang

    DOI：10.11910/j.issn.2791-2043.2026.2.03
    Abstract：Luminal instruments, with their complex configurations, narrow lumens, and tendency to retain organic matter after use, are the most difficult category to clean in the Central Sterile Supply Department (CSSD) and have the highest re-cleaning rates. Their cleaning quality directly affects sterilization outcomes and patient safety. However, current technologies still face several persistent challenges: the absence of standardized pre-treatment protocols, marked variability in the efficacy of different moisturizing methods, and biofilm formation resulting from extended holding times of contaminated instruments; manual cleaning that depends heavily on operator technique, leading to inconsistent results; insufficient adaptability of mechanical cleaning equipment to complex luminal geometries, with steam penetration constrained by lumen length and diameter; and quality assessment that still relies primarily on visual inspection, while quantitative methods remain underutilized, making it difficult to accurately evaluate cleaning efficacy inside lumens. This article systematically reviews research progress on luminal instrument cleaning technologies in China from four perspectives: pre-treatment techniques, improvements in cleaning tools, upgrades to mechanical cleaning equipment, and quality assessment methods. For pre-treatment, this article evaluates the efficacy and operational convenience of various moisturizing agents and pre-cleaning approaches for biofilm disruption/loosening. For cleaning tools, this article describes the design rationale and clinical benefits of novel luminal cleaning racks, fixation devices, and internal illumination systems. For mechanical cleaning, this article details the operating principles and efficiency gains of pulsating vacuum washer-disinfectors and ultrasonic cleaning equipment. For quality assessment, this article compares the sensitivity and applicable contexts of visual inspection, Adenosine Triphosphate (ATP) bioluminescence assay, and protein residue detection. By examining the strengths and limitations of each technology, this article identifies current trends and suggests future research directions, with the aim of offering practical guidance for cleaning quality management of luminal instruments and informing subsequent investigations.  
    Keywords：Luminal instrument;Cleaning technology;Pre-treatment;Mechanical cleaning;Quality check   
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  • 技术创新

    Jing Che, Yan Zeng, Min Li, Guoping Xiong, Yunlei Li, Shuhui Li

    DOI：10.11910/j.issn.2791-2043.2026.2.04
    Abstract：ObjectiveThis study evaluated the effectiveness of an AI-assisted information traceability system in risk early warning for nursing quality and operational efficiency assessment in the Central Sterile Supply Department (CSSD), providing evidence-based support for the intelligent and refined management of the CSSD.MethodsA self-controlled study was conducted in the CSSD of a Grade A tertiary hospital. The control phase (January to June 2025) employed the conventional barcode-based traceability system, whereas the observation phase (July to December 2025) utilized the AI-assisted information traceability system. In the control group, the management model comprised process recording, manual spot-check quality control, and manual statistical efficiency management. For the observation group, AI modules were developed based on the existing traceability system, incorporating four core functional modules: full-process data collection, three-tier risk early warning, dynamic efficiency evaluation, and closed-loop rectification. The two groups were compared across nursing quality and risk control indicators, operational efficiency indicators, and nursing staff work experience indicators. All statistical analyses were performed using SPSS 26.0 software.ResultsIn the observation group, the qualified rates for instrument cleaning, packaging, sterilization, and risk early warning accuracy were 99.94%, 99.97%, 99.99%, and 98.62%, respectively, outperforming the control group (99.71%, 99.78%, 99.83%, and 82.15%). The incidence rates of quality defects, adverse events, and healthcare-associated infection (HAI) related events in the observation group were 0.07%, 0.03%, and 0, respectively, which were lower than controls (0.32%, 0.18%, and 0.11%) (all P<0.05). The average instrument turnaround time, emergency item processing time, and daily manual verification time in the observation group were 16.58 ± 2.37  h, 1.21 ± 0.32  h, and 0.85 ± 0.21  h, respectively, all significantly shorter than the corresponding values in the control group (22.43 ± 3.26  h, 2.64 ± 0.57  h, and 4.12 ± 0.75  h). The instrument reuse rate, equipment utilization rate, and completeness rate of traceability information were also significantly higher in the observation group than in the control group (all P<0.05). Furthermore, nursing staff in the observation group reported significantly higher satisfaction with work efficiency and system use, along with a significantly lower risk management pressure score, compared with the control group (all P<0.05).ConclusionThe AI-assisted information traceability system enables pre-event alerting, real-time interception, and post-event traceability for risks throughout the entire workflow in the CSSD. The AI-assisted system significantly enhanced nursing quality and safety, and reduced HAI risk. Furthermore, it facilitates dynamic and refined evaluation and optimization of departmental operational efficiency, alleviates the workload of nursing staff, and meets the requirements of smart hospital construction and high-quality nursing development. Collectively, these findings support the system’s substantial potential for clinical translation and broad applicability.  
    Keywords：Central Sterile Supply Department (CSSD);Artificial Intelligence (AI);Information traceability system;Risk Early Warning;Efficiency Evaluation;Nursing Quality   
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