About this trial
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.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
None
Disqualifiers
None
Trial design
Parallel
Treatments tested in this trial
Generative Artificial Intelligence
Other interventionGen AI assisted symptom intake summarization
Standard weekly symptom intake
Other interventionStandard weekly symptom intake performed by a physician
Treatment groups
Trial outcomes
Primary outcomes
The Physician Documentation Quality Instrument-9 (PDQI-9)
The Physician Documentation Quality Instrument-9 (PDQI-9) will be used to evaluate the quality of the documentation. The PDQI-9 is a validated questionnaire that assesses nine key elements of documentation quality: completeness, correctness, consistency, comprehensibility, relevance, organization, conciseness, formatting, and overall impression.
Secondary outcomes
Time tracking
The total time spent with each patient and on documentation will be recorded for both the standard and AI-enabled consultations.
Accuracy
A team of blinded specialist oncologists will compare the summaries generated by both methods. Accuracy ratings will be assigned based on tabulated data from the completed questionnaires. Patients will be shown both the AI-generated summary and the doctor's summary and will be asked to rate their accuracy on a scale from 1 to 10.
Physician satisfaction
Physician Satisfaction Assessment: Physician satisfaction with the AI-enabled symptom summary tool compared to traditional documentation methods will be evaluated. Measurement Tool: 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: Physicians will rate how easy they find the AI-enabled tool to use compared to traditional methods. Time Efficiency: Physicians will assess whether the AI tool saves time during patient consultations and documentation. Overall Satisfaction: An overall satisfaction score will be provided, reflecting the physician's general experience with the AI tool.
Patient satisfaction
Patient Satisfaction Assessment: Patient satisfaction with the AI-enabled symptom summary tool compared to traditional documentation methods will be evaluated as a secondary outcome of this trial. Measurement Tool: 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: Patients will rate how clear and understandable they find the AI-generated summaries compared to traditional documentation. Engagement and Interaction: Patients will evaluate their engagement and interaction experience during consultations using the AI tool versus traditional methods. Satisfaction: An overall satisfaction score will be provided, reflecting the patient's general experience with the AI tool.
Sponsors and contacts
Click on the lead sponsor to view all of their trials.
jaide
Lead sponsor
National Cancer Institute, Brazil
Collaborator