AI Chatbot Education for Atrial Fibrillation

Overseen By Samir Saba, MD

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: University of Pittsburgh

Disqualifiers: Inability to use device, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: 4

What You Need to Know Before You Apply

What is the purpose of this trial?

This trial explores how an AI chatbot can educate people about atrial fibrillation, a heart condition that can lead to serious issues like strokes and heart failure. The researchers aim to determine if the chatbot can improve patients' understanding of their condition and increase their involvement in treatment. Participants will interact with the chatbot, asking up to 10 questions, and receive answers in text and video form. The trial seeks individuals over 18 who have been newly diagnosed with atrial fibrillation and can use an iPad to type questions. As an unphased trial, this study offers a unique opportunity to contribute to innovative research that could enhance patient education and engagement.

Will I have to stop taking my current medications?

The trial information does not specify whether you need to stop taking your current medications. It seems to focus on education about atrial fibrillation rather than medication changes.

What prior data suggests that this AI chatbot education method is safe for patients with atrial fibrillation?

Earlier research found that a digital education program effectively engaged patients with atrial fibrillation (AF) in learning about their condition. Another study demonstrated that a structured home education program positively impacted people with AF.

These studies highlight that learning about AF is generally safe and well-received. Technology-based patient education has not resulted in any serious negative effects. Education—whether from a person or a computer—does not have physical side effects like medication might.

This trial uses an AI chatbot for education, a new approach that still emphasizes learning and interaction. Based on existing research, using this AI tool for education should be safe for participants. ^{1 2 3 4 5}

Why are researchers excited about this trial?

Researchers are excited about using an AI chatbot for atrial fibrillation education because it offers a personalized, interactive approach to learning about the condition. Unlike standard educational materials, which can be static and overwhelming, the chatbot engages users in real-time and adapts to their individual learning pace and style. This method aims to enhance understanding and retention of crucial information, potentially leading to better patient self-management and outcomes.

What evidence suggests that this AI chatbot education is effective for atrial fibrillation?

Research has shown that AI chatbots can effectively educate people about atrial fibrillation, a heart condition. Studies have found that AI chatbots, such as ChatGPT, provide accurate information, enhancing patients' understanding of their health. One study discovered that using AI chatbots increased patients' awareness of atrial fibrillation and improved their quality of life. Patients also felt that these chatbots were caring, which is important for feeling supported. In this trial, all participants will interact with an AI chatbot for atrial fibrillation education. Overall, AI chatbots appear promising in helping people learn about and manage atrial fibrillation effectively. ^{6 7 8 9 10}

Who Is on the Research Team?

Samir Saba, MD

Principal Investigator

UPMC Heart and Vascular Institute

Are You a Good Fit for This Trial?

This trial is for adults over 18 with a known diagnosis of atrial fibrillation. It's suitable for those who can text and use an electronic device, as well as read English. People unable to interact with technology or who have language barriers are not eligible.

Inclusion Criteria

I am older than 18 years.

I have been diagnosed with atrial fibrillation.

Exclusion Criteria

Inability to text/use an electronic device

Inability to read English

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

1 week

1 visit (in-person)

Informed Consent

Participants read, understand, and sign an informed consent form

1 day

1 visit (in-person)

Interaction with AI Chatbot

Participants interact with the AI chatbot for atrial fibrillation education using an iPad

1 hour

1 session (virtual)

Follow-up

Participants complete pre- and post-interaction surveys to assess empathy, knowledge, and trust

1 day

1 session (virtual)

What Are the Treatments Tested in This Trial?

Interventions

• Atrial Fibrillation Education

Trial Overview

The study tests the effectiveness of AI chatbot-delivered education on atrial fibrillation. Participants will use an iPad to ask questions about their condition and receive answers with educational content, including video links, during a one-hour session.

How Is the Trial Designed?

Group I: Pilot interventional arm Experimental Treatment 1 Intervention

Single arm, wherein all consenting participants undergoing interaction with chatbot for atrial fibrillation education.

Who Is Running the Clinical Trial?

University of Pittsburgh

Lead Sponsor

Trials

1,820

Recruited

16,360,000+

Published Research Related to This Trial

Machine learning, particularly artificial neural networks, has significant potential to enhance the detection and management of atrial fibrillation, which affects one in three people and can lead to serious complications like stroke.

These technologies could improve patient care by enabling better screening for asymptomatic atrial fibrillation and helping cardiologists identify patients at high risk of recurrence after treatment, thus potentially improving clinical outcomes.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Machine learning in the detection and management of atrial fibrillation. Wegner, FK., Plagwitz, L., Doldi, F., et al.[2022]

A study evaluating responses to 200 medication-use questions found that a medication database provided more clinically correct, complete, safe, and useful answers than an AI chatbot, with 97% of questions answered by the database compared to 80% by the chatbot.

While the chatbot's clinical correctness was comparable to the database at 85%, it lagged behind in completeness (65%), safety (71%), and usefulness (68%), indicating a need for further improvements in chatbot technology for reliable clinical use.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Evaluation of inpatient medication guidance from an artificial intelligence chatbot. Beavers, J., Schell, RF., VanCleave, H., et al.[2023]

Recent AI models have been developed that can accurately assess the risk of atrial fibrillation (AF) by analyzing electrocardiogram waveforms, providing additional predictive information beyond traditional clinical risk factors.

By identifying individuals at higher risk for AF, these AI-based models could enhance preventive strategies, such as targeted screening and risk factor modification, ultimately aiming to reduce the incidence of AF and its related health issues.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

The Use of Artificial Intelligence to Predict the Development of Atrial Fibrillation. Pipilas, D., Friedman, SF., Khurshid, S.[2023]

The ASK FOR IT educational program, designed for patients with atrial fibrillation, showed promising results in a pilot study with 15 participants, leading to significant improvements in symptoms and mental health-related quality of life after 3 months.

Participants found the program easy to use and understand, indicating its potential as a reliable source of information for patients, which will be further evaluated in a larger randomized controlled trial involving 200 patients.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

ASK FOR IT: An Internet-based educational program for patients with atrial fibrillation - Results from a pilot study and design of the randomized, controlled, multicenter ASK FOR IT study. Walfridsson, U., Brohede, D., Walfridsson, H.[2022]

ChatGPT provided correct answers to 69.2% of questions about thyroid nodules, regardless of the type of prompting used, indicating a generally reliable performance for patient education.

However, while prompting for references resulted in 100% of responses including citations, a significant portion of these references (72.5%) did not accurately report the information, highlighting concerns about the reliability of ChatGPT as a medical educational tool.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Evaluating ChatGPT responses on thyroid nodules for patient education. Campbell, DJ., Estephan, LE., Sina, EM., et al.[2023]

Information about benign paroxysmal positional vertigo (BPPV) obtained from Google search results is more readable and of higher quality compared to responses generated by the conversational AI model ChatGPT, based on a study analyzing 30 sources.

ChatGPT responses were found to be more difficult to read and comprehend, with lower scores in readability metrics and quality assessments, indicating that traditional search engines may provide more accessible patient education materials.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

BPPV Information on Google Versus AI (ChatGPT). Bellinger, JR., De La Chapa, JS., Kwak, MW., et al.[2023]

The AI algorithm demonstrated a moderate ability to predict new cases of atrial fibrillation (AF) in a cohort of 1403 patients presenting with palpitations, achieving an area under the curve (AUC) of 0.74.

With a sensitivity of 79.1% and specificity of 66.1%, the algorithm can effectively identify patients at risk for AF, although its clinical utility may be limited due to the low incidence of AF (3.1%) in this emergency department population.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Evaluating atrial fibrillation artificial intelligence for the ED: statistical and clinical implications. Kaminski, AE., Albus, ML., Ball, CT., et al.[2022]

In a study of 52,547 patients with newly diagnosed atrial fibrillation, rhythm-control strategies were found to be more effective than rate-control strategies in improving patient outcomes, including lower mortality rates.

Using artificial intelligence and Q-learning, researchers identified optimal treatment strategies for different patient clusters, showing that aligning provider-selected treatments with AI recommendations led to significantly better outcomes, including fewer deaths.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Evaluation of Quantitative Decision-Making for Rhythm Management of Atrial Fibrillation Using Tabular Q-Learning. Barrett, CD., Suzuki, Y., Hussein, S., et al.[2023]

Computer-based patient education can provide personalized instruction that adapts to individual learning paces, which is crucial as healthcare becomes more patient-centered and hospital stays shorten.

This method not only helps in teaching patients about their health and treatment options but also tracks their understanding, which is important for informed consent and potential reimbursement, highlighting the need for more research in this area.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Computer-based patient education revisited. Gillispie, MA., Ellis, LB.[2019]

ChatGPT provided correct answers to 71.9% of questions about obstructive sleep apnea, indicating it can be a useful tool for patient education, regardless of the type of prompting used.

Prompting for statistics and references significantly improved the quality of responses, with 96.1% of those answers including references, while patient-friendly prompts resulted in simpler language suitable for patients, maintaining an acceptable grade level for medical information.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Evaluating ChatGPT responses on obstructive sleep apnea for patient education. Campbell, DJ., Estephan, LE., Mastrolonardo, EV., et al.[2023]

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