Primary Care PatientsAI-CARENCT06997107
AI to Create Accessible & Reliable Patient Education Materials
A study testing Health promotional messages generated by Artificial Intelligence and Health promotional messages generated by humans in people with Primary Care Patients, run by Hopital Montfort at 1 site in Canada.
Trial summary
This study investigates the use of Generative AI (GAI) to support primary care practices in delivering accurate, accessible patient education. With the rise of health misinformation, increasingly complex patient needs, and a strained healthcare workforce, primary care must find new ways to communicate trusted health… Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Primary Care Patients
- What is being tested?
- Health promotional messages generated by Artificial Intelligence and Health promotional messages generated by humans
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 56 participants
- Where
- 1 siteCanada
- Study dates
- 16 Jan 2025to 12 Jun 2026
- 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
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- Content team members: Content team members must have an expertise in primary care, public health, or health communication. The investigators aim to recruit at least two or three members whose mother tongue is French.
- Providers' review panel: The investigators will recruit a diverse group of primary care providers. All providers who provide comprehensive care in Canada and will be eligible to participate. They must be proficient in either written English or French. They must be able to consent to participate in this study.
- Patients' review panel: The investigators will recruit a diverse group of patients. All must be aged 18 years or older and be proficient in either written English or French. They must be able to consent to participate in this study.
Who cannot join
- Content team members, primary care providers, or patients who cannot write (content team members) or read (review panels) in either French or English will be excluded. Primary care providers or patients who do not have an email address, a computer or a cellphone to complete the evaluations on REDCap will also be excluded. The investigators will not include minors or patients who cannot provide informed consent themselves, such as those with advanced dementia.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: AI-CARE: AI to Create Accessible & Reliable Patient Education Materials
This study investigates the use of Generative AI (GAI) to support primary care practices in delivering accurate, accessible patient education. With the rise of health misinformation, increasingly complex patient needs, and a strained healthcare workforce, primary care must find new ways to communicate trusted health information effectively. Leveraging the Canadian Primary Care Information Network (CPIN), this study will generate patient education messages on key health topics using both GAI and human content experts.
Diverse review panels of patients and providers will assess the messages on quality of information, adaptability, and relevance and usefulness, with special attention to socioeconomic factors that may impact message accessibility. CPIN will recruit a diverse sample of participants to evaluate both GAI- and human-generated messages. Review panels will provide structured feedback via surveys, aiming to identify differences in content quality and effectiveness.
The study's goal is to determine whether GAI can produce high-quality health information that meets primary care standards. Results will reveal how GAI tools can support primary care in reducing misinformation and administrative burdens, fostering patient-provider relationships, and improving health equity. Findings will inform best practices for integrating GAI in primary care to ensure accessible, timely patient education across Canada.
Read the detailed description
Background. The increasing prevalence of health misinformation, complex patient needs, and a strained healthcare workforce necessitate innovative approaches to patient education in primary care. Generative AI (GAI) offers the potential to deliver accurate, accessible health information while reducing administrative burdens. This study explores the use of GAI to support primary care practices in producing trusted, high-quality patient education materials.
Objective. The investigators propose to leverage advances in GAI and our experience with CPIN to provide timely and accurate health information for primary care practices across Canada. Our goal is to determine whether GAI can produce education material for primary care that is non-inferior compared to experts in primary care and public health.
Methods. The content team for this study will consist of experts specializing in primary care, public health, or health communication. Team members will create digital health messages in two formats: a short, text-messaging format (850 characters or less), and a one-page handout for patients. On the other hand, a generative AI system will also generate messages. Topics and prompts for message writing will be provided to both the content team and the GAI. Messages in English and French will be available.
To evaluate the generated content, two review panels (a review panel of 25 providers and one of 25 patients) will assess messages created by both human experts and generative AI over the course of 12 months. Each month, using an evaluation grid provided to assess the quality of information, adaptability, and relevance and usefulness of the message, panelists will review a total of 16 messages (four topics x 4 messages).
Panelists will be blinded to the message generation source (AI or human). Short messages will be shown first to minimize potential bias from the detailed information in longer messages and ensure their clarity and completeness are effectively assessed.
Both providers and patients on the review panels will complete assessments via REDCap surveys. The evaluation grid will be the same for providers and patients and will use a Likert scale from 1 to 4 (1: Strongly disagree; 4: Strongly agree). Specifically, there will be statements on Adaptability (subcategories: Clarity and understandability, Appropriate emotional appeal, Appropriate rational appeal, Tone, and Inclusivity) and Relevance and Usefulness. Statements on Quality of Information (subcategories: Accuracy, Reliability, and Completeness) will only be asked to providers. Patients, in contrast, will be asked at the end whether they noticed any inaccuracies in the message.
- Conditions
- Primary Care Patients
- Being tested
- Health promotional messages generated by Artificial Intelligence, Health promotional messages generated by humans
- Keywords
- primary care, generative artificial intelligence, health promotion messages, patient education, primary care providers, primary care patients
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Health promotional messages generated by Artificial IntelligenceOther
Short (850 characters) and long (1 page) messages will be generated by a Generative Artificial Intelligence (ChatGPT 4.0) on different health-related topics
Health promotional messages generated by humansOther
Short (850 characters) and long (1 page) messages will be generated by a primary care and/or public health human expert on different health-related topics
| Study group | Role | What they receive |
|---|---|---|
| Artificial Intelligence | Experimental | Not described Receives: Other: Health promotional messages generated by Artificial Intelligence |
| Human expert | Active comparator | Not described Receives: Other: Health promotional messages generated by humans |
What is being measured
The primary outcome is the main question the study is designed to answer.
Clarity and Understandability score
Message score on three statements related to Clarity and Understandability after the participant has read the message. Three Likert scale questions from 1 - 4 (1: Strongly disagree and 4: Strongly agree), total score between 3 - 12 (low score: poorly rated message, high score: well rated message).
Measured: 12 months
Secondary outcomes (2)
Overall message score
Overall message score after the participant has read the message. Specifically, the scores from statements on Adaptability (subcategories: Clarity and understandability, Appropriate emotional appeal, Appropriate rational appeal, Tone, and Inclusivity) and Relevance and Usefulness will be used to measure the overall message score. Statements on Quality of Information (subcategories: Accuracy, Reliability, and Completeness) will only be asked to providers. A Likert scale from 1 - 4 will be used (1: Strongly disagree and 4: Strongly agree), total score between 22 and 88 (low score: poorly rated message, high score: well rated message).
Measured: 12 months
Message category and subcategory scores
Message score for each main category (quality of information, adaptability, and relevance and usefulness) and subcategories after the participant has read the message. A Likert scale from 1 - 4 will be used (1: Strongly disagree and 4: Strongly agree).
- Quality of information: total score between 5 and 20 (low score: poorly rated message, high score: well rated message)
- Adaptability: total score between 13 and 52 (low score: poorly rated message, high score: well rated message).
- Relevance and usefulness: total score between 3 and 12 (low score: poorly rated message, high score: well rated message).
Measured: 12 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted30 May 2025
- Study start16 Jan 2025
- Primary completion12 Jun 2026
- Study completion12 Jun 2026
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (3)Papers the sponsor has linked to this study.
- Other trials for Primary Care PatientsStudies 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
- Health services research
- Allocation
- Randomised
- Intervention model
- Crossover
- Model description
- Each month, patients and providers will receive a series of health promotion messages generated by AI and humans.
- Masking
- Double blind
- Who is masked
- Participant, Outcomes assessor
- Enrolment
- 56 participants (actual)
Sponsor and investigators
- Lead sponsor
- Hopital Montfort
- Sponsor type
- University, hospital or other
- Collaborators
- Cheo Research Institute
- Responsible party
- Sponsor
- Investigators
- Sharon Johnston, MD, LLM, Principal investigator, Institut du Savoir MontfortWilliam Hogg, MD, MSc, Principal investigator, Institut du Savoir Montfort
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06997107
- Sponsor's protocol ID
- 24-25-11-038
- Acronym
- AI-CARE
- Status last verified
- Aug 2026
- First submitted
- 21 May 2025
- Last updated
- 21 Sep 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Tam TYC, Sivarajkumar S, Kapoor S, Stolyar AV, Polanska K, McCarthy KR, Osterhoudt H, Wu X, Visweswaran S, Fu S, Mathur P, Cacciamani GE, Sun C, Peng Y, Wang Y. A framework for human evaluation of large language models in healthcare derived from literature review. NPJ Digit Med. 2024 Sep 28;7(1):258. doi: 10.1038/s41746-024-01258-7.
PubMed 39333376Bedi S, Liu Y, Orr-Ewing L, Dash D, Koyejo S, Callahan A, Fries JA, Wornow M, Swaminathan A, Lehmann LS, Hong HJ, Kashyap M, Chaurasia AR, Shah NR, Singh K, Tazbaz T, Milstein A, Pfeffer MA, Shah NH. Testing and Evaluation of Health Care Applications of Large Language Models: A Systematic Review. JAMA. 2025 Jan 28;333(4):319-328. doi: 10.1001/jama.2024.21700.
PubMed 39405325Lemieux A, Kutcher SA, Galani Tietcheu BR, Seitz G, Trickovic J, Archibald D, Grosjean S, Hogg W, Johnston S. A double-blind, crossover, non-inferiority randomised controlled trial where primary care providers and patients compare human-generated and AI-generated digital health messages: the AI-CARE study protocol. BMJ Open. 2026 Apr 7;16(4):e115673. doi: 10.1136/bmjopen-2025-115673.
PubMed 41946543
Study at a glance
From the registry record, last updated by the sponsor on 21 Sep 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- AI-CARE: AI to Create Accessible & Reliable Patient Education Materials
- ClinicalTrials.gov ID
- NCT06997107
- Acronym / sponsor ID
- AI-CARE / 24-25-11-038
- Conditions
- Primary Care Patients
- Keywords
- primary care, generative artificial intelligence, health promotion messages, patient education, primary care providers, primary care patients
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 56 participants (actual)
- Lead sponsor
- Hopital MontfortUniversity, hospital or other
- Collaborators
- Cheo Research Institute
- Study start
- 16 Jan 2025 (actual)
- Primary completion
- 12 Jun 2026 (actual)
- Study completion
- 12 Jun 2026 (actual)
- First posted
- 30 May 2025 (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.
This study investigates the use of Generative AI (GAI) to support primary care practices in delivering accurate, accessible patient education. With the rise of health misinformation, increasingly complex patient needs, and a strained healthcare workforce, primary care must find new ways to communicate trusted health information effectively. Leveraging the Canadian Primary Care Information Network (CPIN), this study will generate patient education messages on key health topics using both GAI and human content experts.
Diverse review panels of patients and providers will assess the messages on quality of information, adaptability, and relevance and usefulness, with special attention to socioeconomic factors that may impact message accessibility. CPIN will recruit a diverse sample of participants to evaluate both GAI- and human-generated messages. Review panels will provide structured feedback via surveys, aiming to identify differences in content quality and effectiveness.
The study's goal is to determine whether GAI can produce high-quality health information that meets primary care standards. Results will reveal how GAI tools can support primary care in reducing misinformation and administrative burdens, fostering patient-provider relationships, and improving health equity. Findings will inform best practices for integrating GAI in primary care to ensure accessible, timely patient education across Canada.
Detailed description
Background. The increasing prevalence of health misinformation, complex patient needs, and a strained healthcare workforce necessitate innovative approaches to patient education in primary care. Generative AI (GAI) offers the potential to deliver accurate, accessible health information while reducing administrative burdens. This study explores the use of GAI to support primary care practices in producing trusted, high-quality patient education materials.
Objective. The investigators propose to leverage advances in GAI and our experience with CPIN to provide timely and accurate health information for primary care practices across Canada. Our goal is to determine whether GAI can produce education material for primary care that is non-inferior compared to experts in primary care and public health.
Methods. The content team for this study will consist of experts specializing in primary care, public health, or health communication. Team members will create digital health messages in two formats: a short, text-messaging format (850 characters or less), and a one-page handout for patients. On the other hand, a generative AI system will also generate messages. Topics and prompts for message writing will be provided to both the content team and the GAI. Messages in English and French will be available.
To evaluate the generated content, two review panels (a review panel of 25 providers and one of 25 patients) will assess messages created by both human experts and generative AI over the course of 12 months. Each month, using an evaluation grid provided to assess the quality of information, adaptability, and relevance and usefulness of the message, panelists will review a total of 16 messages (four topics x 4 messages).
Panelists will be blinded to the message generation source (AI or human). Short messages will be shown first to minimize potential bias from the detailed information in longer messages and ensure their clarity and completeness are effectively assessed.
Both providers and patients on the review panels will complete assessments via REDCap surveys. The evaluation grid will be the same for providers and patients and will use a Likert scale from 1 to 4 (1: Strongly disagree; 4: Strongly agree). Specifically, there will be statements on Adaptability (subcategories: Clarity and understandability, Appropriate emotional appeal, Appropriate rational appeal, Tone, and Inclusivity) and Relevance and Usefulness. Statements on Quality of Information (subcategories: Accuracy, Reliability, and Completeness) will only be asked to providers. Patients, in contrast, will be asked at the end whether they noticed any inaccuracies in the message.
Study design
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Crossover
- Masking
- Double blind (participant, outcomes assessor)
- Number of arms
- 2
- Model description
- Each month, patients and providers will receive a series of health promotion messages generated by AI and humans.
| Arm | Type | Receives |
|---|---|---|
| Artificial Intelligence | Experimental | Other: Health promotional messages generated by Artificial Intelligence |
| Human expert | Active comparator | Other: Health promotional messages generated by humans |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (3 listed)
- Content team members: Content team members must have an expertise in primary care, public health, or health communication. The investigators aim to recruit at least two or three members whose mother tongue is French.
- Providers' review panel: The investigators will recruit a diverse group of primary care providers. All providers who provide comprehensive care in Canada and will be eligible to participate. They must be proficient in either written English or French. They must be able to consent to participate in this study.
- Patients' review panel: The investigators will recruit a diverse group of patients. All must be aged 18 years or older and be proficient in either written English or French. They must be able to consent to participate in this study.
Key exclusion criteria (1 listed)
- Content team members, primary care providers, or patients who cannot write (content team members) or read (review panels) in either French or English will be excluded. Primary care providers or patients who do not have an email address, a computer or a cellphone to complete the evaluations on REDCap will also be excluded. The investigators will not include minors or patients who cannot provide informed consent themselves, such as those with advanced dementia.
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 |
|---|---|---|
| Health promotional messages generated by Artificial IntelligenceShort (850 characters) and long (1 page) messages will be generated by a Generative Artificial Intelligence (ChatGPT 4.0) on different health-related topics | Other | Artificial Intelligence |
| Health promotional messages generated by humansShort (850 characters) and long (1 page) messages will be generated by a primary care and/or public health human expert on different health-related topics | Other | Human expert |
Primary outcome
Clarity and Understandability score
Time frame: 12 months
Message score on three statements related to Clarity and Understandability after the participant has read the message. Three Likert scale questions from 1 - 4 (1: Strongly disagree and 4: Strongly agree), total score between 3 - 12 (low score: poorly rated message, high score: well rated message).
2 secondary outcomes
Overall message score
Time frame: 12 months
Message category and subcategory scores
Time frame: 12 months
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 21 Sep 2026.
- Background: PubMed 39333376Tam TYC, Sivarajkumar S, Kapoor S, Stolyar AV, Polanska K, McCarthy KR, Osterhoudt H, Wu X, Visweswaran S, Fu S, Mathur P, Cacciamani GE, Sun C, Peng Y, Wang Y. A framework for human evaluation of large language models in healthcare derived from literature review. NPJ Digit Med. 2024 Sep 28;7(1):258. doi: 10.1038/s41746-024-01258-7.
- Background: PubMed 39405325Bedi S, Liu Y, Orr-Ewing L, Dash D, Koyejo S, Callahan A, Fries JA, Wornow M, Swaminathan A, Lehmann LS, Hong HJ, Kashyap M, Chaurasia AR, Shah NR, Singh K, Tazbaz T, Milstein A, Pfeffer MA, Shah NH. Testing and Evaluation of Health Care Applications of Large Language Models: A Systematic Review. JAMA. 2025 Jan 28;333(4):319-328. doi: 10.1001/jama.2024.21700.
- Derived from this study: PubMed 41946543Lemieux A, Kutcher SA, Galani Tietcheu BR, Seitz G, Trickovic J, Archibald D, Grosjean S, Hogg W, Johnston S. A double-blind, crossover, non-inferiority randomised controlled trial where primary care providers and patients compare human-generated and AI-generated digital health messages: the AI-CARE study protocol. BMJ Open. 2026 Apr 7;16(4):e115673. doi: 10.1136/bmjopen-2025-115673.
- Canadian Primary Care Information NetworkLink provided by the sponsor.
- 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.
Sharon Johnston, MD, LLM principal investigator
Institut du Savoir Montfort
William Hogg, MD, MSc principal investigator
Institut du Savoir Montfort
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Health promotional messages generated by Artificial Intelligence and Health promotional messages generated by humans in people with Primary Care Patients, run by Hopital Montfort at 1 site in Canada.
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 3 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: finished, as of the sponsor's last update on 21 Sep 2026. 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 Canada. 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Clarity and Understandability score (time frame: 12 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06997107, which also lists 3 linked publications.
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 21 Sep 2026.