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Japanese

Sept. 17, 2025

Sept. 17, 2025

jRCT1070250070

Visualizing and Optimizing Operating Room Nurses Workload Using Generative Artificial Intelligence: A Cross-Sectional Study (VONO AI Study(Visualizing Operating room Nurses workload with Optimization by AI))

A Study on Visualizing and Optimizing Operating Room Nurses Workload Using Artificial Intelligence: A Cross-Sectional Study (VONO AI)

Hara Kentaro

Department of Fundamental Nursing, Faculty of Life Sciences, Kumamoto University

4-24-1 Kuhonji, Chuo-ku, Kumamoto-shi

+81-963735474

kentaro-hara@kumamoto-u.ac.jp

Ishibashi Ayano

Clinical Research Center, NHO Nagasaki Medical Center, Nagasaki

2-1001-1, Kubara, Omura

+81-957523121

ishibashi.ayano.uk@mail.hosp.go.jp

Not Recruiting

July. 08, 2024

July. 08, 2024
38

Observational

single arm study

open(masking not used)

uncontrolled control

single assignment

other

Nurses working in the operating theatre at Nagasaki Medical Centre
Persons who were engaged in work during the study period
Persons who gave written consent to participate in the study

Persons in managerial positions who do not perform actual direct nursing duties
Persons who are unable to participate in the survey for reasons such as cognitive function

20age old over
No limit

Both

Nurses' workload, workload and perioperative nursing tasks

Operating room nurses; workload; perioperative nursing; occupational stress; task analysis

None

Quantification of workload (hours, task type, number of tasks)
Visualisation of work distribution (by weekday/holiday, day shift/night shift)

AI-based work optimisation simulation results
Relationship between workload and working pattern (working days and hours)
Relationship between nurse attributes (years of experience, role) and workload

None
Ethics Review Committee, NHO Nagasaki Medical Center
2-1001-1, Kubara, Omura, Nagasaki

+81-957-52-3121

611-kenkyu@mail.hosp.go.jp
Approval

May. 14, 2024

No

none