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Study on efficient reporting of protocol deviations in clinical trials using generative AI

Study on efficient reporting of protocol deviations in clinical trials using generative AI - Deviation Reporting AI Study

Status
Active, not recruiting
Phases
Unknown
Study type
Interventional
Source
JPRN
Registry ID
JPRN-UMIN000058692
Enrollment
40
Registered
2025-09-01
Start date
2025-09-01
Completion date
Unknown
Last updated
2026-09-14

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

Conditions

none

Interventions

First report created without AI support
second with AI support. First report created with AI support
second without AI support.

Sponsors

Okayama University
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: 1. Employees of Okayama University Hospital or SMO 2. Some experience in clinical trial or research-related duties (medical or administrative) 3. Able to operate a computer and create documents in Japanese 4. Capable of understanding the study purpose and providing written informed consent 5. Aged >=18 and <80 years at the time of consent

Exclusion criteria

Exclusion criteria: 1. Those with advanced experience using generative AI that may bias the evaluation 2. Those deemed unable to participate due to mental or physical burden (based on self-report)

Design outcomes

Primary

MeasureTime frame
Quality score of the reports based on blinded evaluation by three raters

Secondary

MeasureTime frame
- Time required for report preparation - Self-assessed task burden - Participant evaluation of similarity to real-world protocol deviation records - Free-text and multiple-choice responses on perceived usefulness and usability of generative AI

Countries

Japan

Contacts

Public ContactSatoshi Kuroda

Okayama University Hospital Department of Pharmacy / Center for Innovative Clinical Medicine

kuroda-s@cc.okayama-u.ac.jp086-235-6122

Outcome results

None listed

Source: JPRN (via WHO ICTRP) · Data processed: Sep 19, 2026