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

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)

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

JPRN

Registry ID

JPRN-jRCT1070250070

Enrollment

Registered

2025-09-17

Start date

2024-07-08

Completion date

Unknown

Last updated

2026-06-29

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Nurses' workload, workload and perioperative nursing tasks Operating room nurses

Interventions

None listed

Eligibility

Sex/Gender

All

Inclusion criteria

Inclusion criteria: 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

Exclusion criteria

Exclusion criteria: 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

Design outcomes

Primary

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

Secondary

Measure	Time frame
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	—

Contacts

Public ContactAyano Ishibashi

Clinical Research Center, NHO Nagasaki Medical Center, Nagasaki

ishibashi.ayano.uk@mail.hosp.go.jp+81-957523121

Outcome results