Artificial Intelligence (AI)-Enhanced Pretreatment Peer-review Process to Improve Patient Safety in Radiation Oncology
Recruiting · NCT07463833 · Interventional (participants receive a specific treatment) · Lead sponsor: UNC Lineberger Comprehensive Cancer Center
View the official record on ClinicalTrials.gov →Interventions studied
What this trial is about
This prospective study will test artificial intelligence (AI) and machine learning (ML) decision support tools. This tool is designed to help doctors, physicists and other staff during pre-treatment peer review, a step where treatment plans are checked before a patient begins care. The system highlights summaries showing how different providers may vary in their treatment planning (provider-variability summaries) and points out the best signals or warning signs to look for (optimal cues). By drawing attention to these patterns and cues, the tool aims to help reviewers spot possible treatment-planning mistakes earlier, reduce the chance of errors, and improve overall patient safety.
Who can take part
Inclusion criteria
- Providers only
- ≥18 years
- Peer-review attendees at participating clinics
- Patients only
- ≥18 years
- All patients with prostate cancer radiation therapy cases treated at participating sites (no intervention delivered to patients)
Exclusion criteria
- Providers only
- Providers unwilling/unable to comply with study procedures; sites unable to implement the workflow or provide required outcomes.
- Patients and Providers
- Has dementia, altered mental status, or any psychiatric or co-morbid condition prohibiting the understanding or rendering of informed consent
Where it is running
1 location listed across 1 US state.
- University of North Carolina at Chapel Hill, Department of Radiation Oncology - Chapel Hill, North Carolina, United States