Radiotherapy Side EffectNCT06525181
AI as an Aid for Weekly Symptom Intake in Radiotherapy
A study testing Generative Artificial Intelligence and Standard weekly symptom intake in people with Radiotherapy Side Effect and Pelvic Cancer, run by jaide at 1 site in Brazil.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Radiotherapy Side Effect, Pelvic Cancer, Patient
- What is being tested?
- Generative Artificial Intelligence and Standard weekly symptom intake
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 200 participants
- Where
- 1 siteBrazil
- Study dates
- 22 Jul 2024to 15 Dec 2024
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- All patients undergoing radiotherapy in the pelvic region.
Who cannot join
- Cases of pelvic reirradiation or operated cases.
Trial locations and contacts
1 site in 1 country.
Brazil1 site, 1 recruiting
- Recruiting
Instituto Nacional de Câncer José Alencar Gomes da Silva - INCA
Rio de Janeiro
Rachele Rachele Grazziotin, MD
About this study
Official title: Evaluation of AI-Enhanced Symptom Summarization in Weekly Radiotherapy Consultations: A Comparative Study
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.
Read the 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.
- Conditions
- Radiotherapy Side Effect, Pelvic Cancer, Patient
- Being tested
- Generative Artificial Intelligence, Standard weekly symptom intake
- Keywords
- Artificial Intelligence, Patient-Reported Outcome
- Medical subject headings
- Radiation Injuries, Pelvic Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Generative Artificial IntelligenceOther
Gen AI assisted symptom intake summarization
Standard weekly symptom intakeOther
Standard weekly symptom intake performed by a physician
| Study group | Role | What they receive |
|---|---|---|
| Standard weekly symptom assessment by physicians | Active comparator | Not described Receives: Other: Standard weekly symptom intake |
| AI-assisted symptom intake | Experimental | Not described Receives: Other: Generative Artificial Intelligence |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 2 months
Secondary outcomes (4)
Time tracking
The total time spent with each patient and on documentation will be recorded for both the standard and AI-enabled consultations.
Measured: 2 months
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.
Measured: 2 months
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.
Measured: 2 months
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.
Measured: 2 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted29 Jul 2024
- Study start22 Jul 2024
- Primary completion1 Nov 2024 (estimated)
- Study completion15 Dec 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Radiotherapy Side EffectStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Other
- Allocation
- Non-randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Enrolment
- 200 participants (estimated)
Sponsor and investigators
- Lead sponsor
- jaide
- Sponsor type
- Industry (pharma, biotech, devices)
- Collaborators
- National Cancer Institute, Brazil
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06525181
- Sponsor's protocol ID
- BR-001
- Status last verified
- Oct 2024
- First submitted
- 18 Jul 2024
- Last updated
- 10 Oct 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 10 Oct 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Evaluation of AI-Enhanced Symptom Summarization in Weekly Radiotherapy Consultations: A Comparative Study
- ClinicalTrials.gov ID
- NCT06525181
- Acronym / sponsor ID
- BR-001
- Conditions
- Radiotherapy Side Effect, Pelvic Cancer, Patient
- Keywords
- Artificial Intelligence, Patient-Reported Outcome
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 200 participants (estimated)
- Lead sponsor
- jaideIndustry (pharma, biotech, devices)
- Collaborators
- National Cancer Institute, Brazil
- Study start
- 22 Jul 2024 (actual)
- Primary completion
- 1 Nov 2024 (estimated)
- Study completion
- 15 Dec 2024 (estimated)
- First posted
- 29 Jul 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Primary purpose
- Other
- Allocation
- Non-randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Standard weekly symptom assessment by physicians | Active comparator | Other: Standard weekly symptom intake |
| AI-assisted symptom intake | Experimental | Other: Generative Artificial Intelligence |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (1 listed)
- All patients undergoing radiotherapy in the pelvic region.
Key exclusion criteria (1 listed)
- Cases of pelvic reirradiation or operated cases.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Generative Artificial IntelligenceGen AI assisted symptom intake summarization | Other | AI-assisted symptom intake |
| Standard weekly symptom intakeStandard weekly symptom intake performed by a physician | Other | Standard weekly symptom assessment by physicians |
Primary outcome
The Physician Documentation Quality Instrument-9 (PDQI-9)
Time frame: 2 months
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.
4 secondary outcomes
Time tracking
Time frame: 2 months
Accuracy
Time frame: 2 months
Physician satisfaction
Time frame: 2 months
Patient satisfaction
Time frame: 2 months
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 10 Oct 2024.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Generative Artificial Intelligence and Standard weekly symptom intake in people with Radiotherapy Side Effect and Pelvic Cancer, run by jaide at 1 site in Brazil.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 10 Oct 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Brazil. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Rachele Grazziotin, MD ((21)3207-4550, cep@inca.gov.br), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The Physician Documentation Quality Instrument-9 (PDQI-9) (time frame: 2 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 10 Oct 2024.