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Background <p>Healthcare-associated infections remain a major patient-safety threat, and environmental contamination is a key driver. Hospital cleaning staff are essential for prevention, yet conventional training lacks standardization and real-time feedback. We evaluated whether an artificial intelligence (AI) system with live monitoring and voice prompts improves terminal cleaning competency.</p> Methods <p>We randomly assigned 60 environmental service staff from Sir Run Run Shaw Hospital, Hangzhou, China, to either conventional instruction or AI-assisted training. The AI platform used computer vision and deep-learning algorithms to track cleaning tasks in real time against a predefined nine-item procedure and provided instant voice corrections when deviations occurred. Competency was assessed post-training under identical, feedback-free conditions, and a score of ≥ 7 was required to pass.</p> Results <p>Median competency scores were 7.8 (IQR: 7.4–8.4) in the AI group versus 5.8 (IQR: 5.4–7.4) in controls. All 30 AI-trained staff passed, compared with 9 (30%) controls. Largest gains occurred in edge cleaning, S-pattern mopping and high-touch surface disinfection.</p> Conclusions <p>Real-time AI supervision significantly enhances cleaning competency and procedure adherence, offering a scalable and standardized approach to improve environmental hygiene and infection prevention.</p>

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Effectiveness of an artificial intelligence-assisted training program on cleaning competency among hospital environmental service staff

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月14日

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  • Yuhua Yuan,
  • Bin Liu,
  • Xiaoxia Wei,
  • Lihong Ye,
  • Baihuan Feng

摘要

Background

Healthcare-associated infections remain a major patient-safety threat, and environmental contamination is a key driver. Hospital cleaning staff are essential for prevention, yet conventional training lacks standardization and real-time feedback. We evaluated whether an artificial intelligence (AI) system with live monitoring and voice prompts improves terminal cleaning competency.

Methods

We randomly assigned 60 environmental service staff from Sir Run Run Shaw Hospital, Hangzhou, China, to either conventional instruction or AI-assisted training. The AI platform used computer vision and deep-learning algorithms to track cleaning tasks in real time against a predefined nine-item procedure and provided instant voice corrections when deviations occurred. Competency was assessed post-training under identical, feedback-free conditions, and a score of ≥ 7 was required to pass.

Results

Median competency scores were 7.8 (IQR: 7.4–8.4) in the AI group versus 5.8 (IQR: 5.4–7.4) in controls. All 30 AI-trained staff passed, compared with 9 (30%) controls. Largest gains occurred in edge cleaning, S-pattern mopping and high-touch surface disinfection.

Conclusions

Real-time AI supervision significantly enhances cleaning competency and procedure adherence, offering a scalable and standardized approach to improve environmental hygiene and infection prevention.