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.
≥18 years
Peer-review attendees at participating clinics
≥18 years
All patients with prostate cancer radiation therapy cases treated at participating sites (no intervention delivered to patients)
None
UNC Lineberger Comprehensive Cancer Center
Lead sponsor
Agency for Healthcare Research and Quality (AHRQ)
Collaborator