AI as an Aid for Weekly Symptom Intake in Radiotherapy

The study investigates the use of artificial intelligence (AI) and large language models (LLMs) to enhance the efficiency and accuracy of weekly treatment consultations (OTVs) in radiotherapy. It hypothesizes that an AI-enabled symptom summary tool will match traditional medical review methods in accuracy while saving time. The study includes patients undergoing pelvic radiotherapy and excludes those with pelvic reirradiation or who have undergone surgery. Patients will receive both standard and AI-assisted weekly consultations, with AI summaries generated using the OpenAI GPT-4 API. Blinded oncologists will compare the accuracy and quality of the AI-generated and doctor-generated summaries, while patients and doctors will rate these summaries. The primary objective is to evaluate the accuracy and time efficiency of AI-assisted symptom summaries compared to traditional methods.

This clinical trial is a comparative study designed to evaluate the accuracy and time efficiency of an AI-enabled symptom summary tool in comparison to traditional medical review methods in patients undergoing radiotherapy in the pelvic region.

Hypothesis:

The AI-enabled symptom summary tool is hypothesized to be non-inferior in accuracy to traditional medical review methods and to save time in the process.

Primary Outcome:

Accuracy of Documentation: The quality of the documentation will be evaluated using the Physician Documentation Quality Instrument-9 (PDQI-9), a validated questionnaire that assesses nine key elements of documentation quality: completeness, correctness, consistency, comprehensibility, relevance, organization, conciseness, formatting, and overall impression. Blinded specialist doctors will use the PDQI-9 to evaluate both AI-generated and traditional summaries, assigning scores from 1 to 10.

Secondary Outcomes:

Time Efficiency: The time required to complete the AI-enabled and traditional consultations will be recorded and compared.

Physician Satisfaction: A custom-designed satisfaction questionnaire will be administered to the physicians participating in the study. This questionnaire will include Likert-scale questions to rate various aspects of satisfaction, including ease of use, time efficiency, accuracy perception, and overall satisfaction.

Patient Satisfaction: A custom-designed satisfaction questionnaire will be administered to the patients participating in the study. This questionnaire will include Likert-scale questions to rate various aspects of satisfaction, including clarity and understanding, perceived accuracy, engagement and interaction, and overall satisfaction.

Methodology:

Patient Selection: Patients meeting the inclusion criteria will be selected for participation. Exclusion criteria will be applied to eliminate cases of pelvic reirradiation or prior operations in the pelvic region.

Consultation Process: Patients will undergo a standard weekly consultation with a doctor. In the same week, each patient will also have a separate consultation with a different doctor. During this second consultation, a symptom questionnaire will be completed under medical supervision. The resulting summary from this questionnaire will be generated using the OpenAI GPT-4 API.

Eligibility Criteria:
Inclusion Criteria:

All patients undergoing radiotherapy in the pelvic region.

Exclusion Criteria:

Cases of pelvic reirradiation or operated cases.