AI as an Aid for Weekly Symptom Intake in Radiotherapy

Trial Title: AI as an Aid for Weekly Symptom Intake in Radiotherapy

NCT ID: NCT06525181

Condition: Radiotherapy Side Effect
Pelvic Cancer
Patient

Conditions: Official terms:
Pelvic Neoplasms

Conditions: Keywords:
Artificial Intelligence
Patient-Reported Outcome

Study type: Interventional

Study phase: N/A

Overall status: Recruiting

Study design:

Allocation: Non-Randomized

Intervention model: Parallel Assignment

Primary purpose: Other

Masking: None (Open Label)

Intervention:

Intervention type: Other
Intervention name: Generative Artificial Intelligence
Description: Gen AI assisted symptom intake summarization
Arm group label: AI-assisted symptom intake

Intervention type: Other
Intervention name: Standard weekly symptom intake
Description: Standard weekly symptom intake performed by a physician
Arm group label: Standard weekly symptom assessment by physicians

Summary: 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.

Detailed description: 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.

Criteria for eligibility:
Criteria:
Inclusion Criteria: All patients undergoing radiotherapy in the pelvic region. Exclusion Criteria: Cases of pelvic reirradiation or operated cases.

Gender: All

Minimum age: 18 Years

Maximum age: N/A

Healthy volunteers: No

Locations:

Facility:
Name: Instituto Nacional de Câncer José Alencar Gomes da Silva - INCA

Address:
City: Rio De Janeiro
Country: Brazil

Status: Recruiting

Contact:
Last name: Rachele Rachele Grazziotin, MD

Start date: July 22, 2024

Completion date: December 15, 2024

Lead sponsor:
Agency: jaide
Agency class: Industry

Collaborator:
Agency: National Cancer Institute, Brazil
Agency class: Other

Source: jaide

Record processing date: ClinicalTrials.gov processed this data on November 12, 2024

Source: ClinicalTrials.gov page: https://clinicaltrials.gov/ct2/show/NCT06525181