Ignite Creation Date:
2025-12-24 @ 6:59 PM
Ignite Modification Date:
2025-12-25 @ 4:32 PM
Study NCT ID:
NCT06684457
Status:
COMPLETED
Last Update Posted:
2025-07-08
First Post:
2024-11-08
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Patient Perceived Empathy of an AI Chatbot for Atrial Fibrillation Education
Sponsor:
University of Pittsburgh
Organization:
Study Overview
Official Title:
Patient Perceived Degree of Empathy Exhibited by Physician Guided, Prompt Engineered, Generative AI Chatbot Designed for Atrial Fibrillation Education
Status:
COMPLETED
Status Verified Date:
2024-11
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
Atrial Fibrillation is a chronic disease with significant health consequences like increased risk of stroke, heart failure, heart attack and death. Educating patients about the disease is important for them to be able to understand the condition better, feel empowered and take an active part in their care plan. AI technology can potentially be used to impart such education. However, doing so with care and empathy is equally important.
Therefore, it is necessary to ensure when AI technology is used to impart education about atrial fibrillation to patients, the humane aspects of the interaction are rigorously tested. This study examines a way to impart atrial fibrillation education through interaction with an AI chatbot, that uses text and links to educational videos. To participate in this study, people need to be age 18 or older and have a history of newly diagnosed atrial fibrillation. Approximately 40 individuals will be asked to take part in this study.
The first step to the study will be reading through, understanding, and signing an informed consent. People who then agree to join the study will have a one-time interaction with the AI chatbot and structured educational material by using an iPad provided to them for the approximately 1 hour duration of the study. People in the study will obtain atrial fibrillation education by typing one by one on the iPad, up to 10 questions about the disease. Answers will include text and links to videos. Before and after atrial fibrillation education, people who join this study will be asked to fill out a survey. The study team will teach patients how to use the iPad and type in questions.
Therefore, it is necessary to ensure when AI technology is used to impart education about atrial fibrillation to patients, the humane aspects of the interaction are rigorously tested. This study examines a way to impart atrial fibrillation education through interaction with an AI chatbot, that uses text and links to educational videos. To participate in this study, people need to be age 18 or older and have a history of newly diagnosed atrial fibrillation. Approximately 40 individuals will be asked to take part in this study.
The first step to the study will be reading through, understanding, and signing an informed consent. People who then agree to join the study will have a one-time interaction with the AI chatbot and structured educational material by using an iPad provided to them for the approximately 1 hour duration of the study. People in the study will obtain atrial fibrillation education by typing one by one on the iPad, up to 10 questions about the disease. Answers will include text and links to videos. Before and after atrial fibrillation education, people who join this study will be asked to fill out a survey. The study team will teach patients how to use the iPad and type in questions.
Detailed Description:
Background:
Atrial fibrillation is a the most common arrhythmic disorder in the United States, with significant morbidity and healthcare cost burden. The number of patients with atrial fibrillation is expected to reach 12.1 million in 2030. AFib related annual incremental healthcare costs were estimated to be $6 to $26 billion based on 2010AF prevalence projections.
Lifestyle modification is one of the four primary pillars of atrial fibrillation care, and patient education is paramount to foster behavioral change. There is also evidence that early rhythm control using medications and ablation therapy can mitigate the overall burden of atrial fibrillation and improve patient quality of life among patients with other cardiovascular risk factors and/or heart failure. However, social determinants of health including health literacy significantly impact patient management, referral practices for procedural interventions, and ultimately patients' clinical outcomes. Anticoagulation prescription for prevention of thromboembolic complications, and adherence to anticoagulation are also known to be correlated with patients' medical literacy and disease awareness. These data point to three important unmet needs. First, there is a need for more robust and intentional patient education to foster behavioral change including lifestyle modification. Second, patient awareness and understanding of the disease is paramount to facilitate anticoagulation adherence and seeking specialist referral which in turn could mitigate AFib related morbidity and healthcare costs. Third, patient education must be delivered in empathetic language, at a (6th-8th) grade reading level, and translated to languages other than English per patient preference.
In that context, LLMs (large language models) such as ChatGPT (Open AI, San Francisco), with built in capabilities for language simplification and translation could play a foundational role in modern medicine. Current data show that LLMs(Chat GPT) are capable of answering patient queries in an empathetic and knowledgeable manner. Data showing improved readability of surgical consent forms simplified using ChatGPT, while maintaining medical information and medicolegal integrity demonstrate the potential of using LLMs for consent processes and shared decision making. However, it is important to note that providing appropriate guardrails to minimize probabilistic guess responses and fine tune the content for accuracy and empathy requires human interaction with the software. The availability of such prompt-engineered technology for patient education could have a profound impact on patient behavior, clinical outcomes and provider-patient relationship.
Atrial fibrillation is a the most common arrhythmic disorder in the United States, with significant morbidity and healthcare cost burden. The number of patients with atrial fibrillation is expected to reach 12.1 million in 2030. AFib related annual incremental healthcare costs were estimated to be $6 to $26 billion based on 2010AF prevalence projections.
Lifestyle modification is one of the four primary pillars of atrial fibrillation care, and patient education is paramount to foster behavioral change. There is also evidence that early rhythm control using medications and ablation therapy can mitigate the overall burden of atrial fibrillation and improve patient quality of life among patients with other cardiovascular risk factors and/or heart failure. However, social determinants of health including health literacy significantly impact patient management, referral practices for procedural interventions, and ultimately patients' clinical outcomes. Anticoagulation prescription for prevention of thromboembolic complications, and adherence to anticoagulation are also known to be correlated with patients' medical literacy and disease awareness. These data point to three important unmet needs. First, there is a need for more robust and intentional patient education to foster behavioral change including lifestyle modification. Second, patient awareness and understanding of the disease is paramount to facilitate anticoagulation adherence and seeking specialist referral which in turn could mitigate AFib related morbidity and healthcare costs. Third, patient education must be delivered in empathetic language, at a (6th-8th) grade reading level, and translated to languages other than English per patient preference.
In that context, LLMs (large language models) such as ChatGPT (Open AI, San Francisco), with built in capabilities for language simplification and translation could play a foundational role in modern medicine. Current data show that LLMs(Chat GPT) are capable of answering patient queries in an empathetic and knowledgeable manner. Data showing improved readability of surgical consent forms simplified using ChatGPT, while maintaining medical information and medicolegal integrity demonstrate the potential of using LLMs for consent processes and shared decision making. However, it is important to note that providing appropriate guardrails to minimize probabilistic guess responses and fine tune the content for accuracy and empathy requires human interaction with the software. The availability of such prompt-engineered technology for patient education could have a profound impact on patient behavior, clinical outcomes and provider-patient relationship.
Study Oversight
Has Oversight DMC:
False
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
False
Is an FDA AA801 Violation?: