Artificial Intelligence (AI)-Enhanced Pretreatment Peer-review Process to Improve Patient Safety in Radiation Oncology

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.

In order to participate in this study a subject must meet all of the eligibility criteria outlined below. 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