Evidence map›Paper›PMID 41826950›Full record

Trial reportBMC health services research2026

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

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

Abstract readRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in BMC health services research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Yuhua YuanDepartment of Infection Prevention and Control, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, No. 3 Qingchun East Road, Shangcheng District, Hangzhou, Zhejiang, 310016, China.
Bin LiuDepartment of Infection Prevention and Control, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, No. 3 Qingchun East Road, Shangcheng District, Hangzhou, Zhejiang, 310016, China.
Xiaoxia WeiDepartment of Infection Prevention and Control, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, No. 3 Qingchun East Road, Shangcheng District, Hangzhou, Zhejiang, 310016, China.
Lihong YeDepartment of Infection Prevention and Control, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, No. 3 Qingchun East Road, Shangcheng District, Hangzhou, Zhejiang, 310016, China.
Baihuan FengDepartment of Infection Prevention and Control, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, No. 3 Qingchun East Road, Shangcheng District, Hangzhou, Zhejiang, 310016, China. fengbaihuan@zju.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHealthcare-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.

methodsWe 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.

resultsMedian 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.

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

Indexed as

Artificial IntelligenceCross InfectionHousekeeping, HospitalChinaDisinfectionHumansIntelligent SystemsArtificial intelligenceCleaning competencyEnvironmental serviceHealthcare-associated infectionsHospital training

Identifiers

PMID41826950
PMCPMC13101272

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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 Socratic graph.