Trial Summary
What is the purpose of this trial?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.
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 data supports the effectiveness of AI Chatbot Education for Atrial Fibrillation?
Research shows that computer-based patient education, like AI chatbots, can effectively teach patients about their health conditions by allowing them to learn at their own pace and track their understanding. This approach has been shown to motivate behavior change and improve patient education in other health areas, suggesting potential benefits for atrial fibrillation education as well.
12345How is the AI Chatbot Education for Atrial Fibrillation treatment different from other treatments for this condition?
The AI Chatbot Education for Atrial Fibrillation is unique because it uses an internet-based educational program to provide patients with reliable information about their condition, which is not a standard approach in AFib management. This treatment focuses on patient education rather than direct medical intervention, aiming to empower patients with knowledge to better manage their condition.
678910Eligibility Criteria
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
Trial Timeline
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)
Participant Groups
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.
1Treatment groups
Experimental Treatment
Group I: Pilot interventional armExperimental Treatment1 Intervention
Single arm, wherein all consenting participants undergoing interaction with chatbot for atrial fibrillation education.
Find a Clinic Near You
Research Locations NearbySelect from list below to view details:
UPMC Heart and Vascular InstitutePittsburgh, PA
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Who Is Running the Clinical Trial?
University of PittsburghLead Sponsor
References
Evaluating ChatGPT responses on thyroid nodules for patient education. [2023]ChatGPT, an artificial intelligence (AI) chatbot, is the fastest growing consumer application in history. Given recent trends identifying increasing patient use of Internet sources for self-education, we seek to evaluate the quality of ChatGPT-generated responses for patient education on thyroid nodules.
Evaluation of inpatient medication guidance from an artificial intelligence chatbot. [2023]To analyze the clinical completeness, correctness, usefulness, and safety of chatbot and medication database responses to everyday inpatient medication-use questions.
BPPV Information on Google Versus AI (ChatGPT). [2023]To quantitatively compare online patient education materials found using traditional search engines (Google) versus conversational Artificial Intelligence (AI) models (ChatGPT) for benign paroxysmal positional vertigo (BPPV).
Computer-based patient education revisited. [2019]Good patient education teaches ideas and skills that help patients cope with immediate medical problems, maintain health and avoid disease. Patient education is increasingly important as hospital stays are shortened, patients become more active health consumers, and there is more need to document informed consent for treatment. It is difficult to provide consistent high quality patient education and reimbursement is problematic. Computers have unique attributes for individualized, effective instruction, including variable lesson pacing controlled by the patient and the ability to accurately track the level of patient understanding to document informed consent and for third party reimbursement purposes. The ability of the computer to persuade as well as inform helps motivate behavior change. The unrealized potential of computer-based patient education makes clear the need for further research on how to effectively use this unique tool for patient education.
Evaluating ChatGPT responses on obstructive sleep apnea for patient education. [2023]We evaluated the quality of ChatGPT responses to questions on obstructive sleep apnea for patient education and assessed how prompting the chatbot influences correctness, estimated grade level, and references of answers.
Evaluating atrial fibrillation artificial intelligence for the ED: statistical and clinical implications. [2022]An artificial intelligence (AI) algorithm has been developed to detect the electrocardiographic signature of atrial fibrillation (AF) present on an electrocardiogram (ECG) obtained during normal sinus rhythm. We evaluated the ability of this algorithm to predict incident AF in an emergency department (ED) cohort of patients presenting with palpitations without concurrent AF.
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. [2022]In the structured care of patients with atrial fibrillation (AF), education is compulsory. Patients search for information but sources of reliable information are sparse. ASK FOR IT, an internet- and guideline-based educational program, offers such information.
Evaluation of Quantitative Decision-Making for Rhythm Management of Atrial Fibrillation Using Tabular Q-Learning. [2023]Background Rhythm management is a complex decision for patients with atrial fibrillation (AF). Although clinical trials have identified subsets of patients who might benefit from a given rhythm-management strategy, for individual patients it is not always clear which strategy is expected to have the greatest mortality benefit or durability. Methods and Results In this investigation 52 547 patients with a new atrial fibrillation diagnosis between 2010 and 2020 were retrospectively identified. We applied a type of artificial intelligence called tabular Q-learning to identify the optimal initial rhythm-management strategy, based on a composite outcome of mortality, change in treatment, and sustainability of the given treatment, termed the reward function. We first applied an unsupervised learning algorithm using a variational autoencoder with K-means clustering to cluster atrial fibrillation patients into 8 distinct phenotypes. We then fit a Q-learning algorithm to predict the best outcome for each cluster. Although rate-control strategy was most frequently selected by treating providers, the outcome was superior for rhythm-control strategies across all clusters. Subjects in whom provider-selected treatment matched the Q-table recommendation had fewer total deaths (4 [8.5%] versus 473 [22.4%], odds ratio=0.32, P=0.02) and a greater reward (P=4.8×10-6). We then demonstrated application of dynamic learning by updating the Q-table prospectively using batch gradient descent, in which the optimal strategy in some clusters changed from cardioversion to ablation. Conclusions Tabular Q-learning provides a dynamic and interpretable approach to apply artificial intelligence to clinical decision-making for atrial fibrillation. Further work is needed to examine application of Q-learning prospectively in clinical patients.
The Use of Artificial Intelligence to Predict the Development of Atrial Fibrillation. [2023]Atrial fibrillation (AF) is a major public health problem associated with preventable morbidity. Artificial intelligence (AI) is emerging as potential tool to prioritize individuals at increased risk for AF for preventive interventions. This review summarizes recent advances in the use of AI models to estimate AF risk.
Machine learning in the detection and management of atrial fibrillation. [2022]Machine learning has immense novel but also disruptive potential for medicine. Numerous applications have already been suggested and evaluated concerning cardiovascular diseases. One important aspect is the detection and management of potentially thrombogenic arrhythmias such as atrial fibrillation. While atrial fibrillation is the most common arrhythmia with a lifetime risk of one in three persons and an increased risk of thromboembolic complications such as stroke, many atrial fibrillation episodes are asymptomatic and a first diagnosis is oftentimes only reached after an embolic event. Therefore, screening for atrial fibrillation represents an important part of clinical practice. Novel technologies such as machine learning have the potential to substantially improve patient care and clinical outcomes. Additionally, machine learning applications may aid cardiologists in the management of patients with already diagnosed atrial fibrillation, for example, by identifying patients at a high risk of recurrence after catheter ablation. We summarize the current state of evidence concerning machine learning and, in particular, artificial neural networks in the detection and management of atrial fibrillation and describe possible future areas of development as well as pitfalls. Typical data flow in machine learning applications for atrial fibrillation detection.