Clinical trial NCT06684457: Patient Perceived Empathy of an AI Chatbot for Atrial Fibrillation Education
Official title: Patient Perceived Degree of Empathy Exhibited by Physician Guided, Prompt Engineered, Generative AI Chatbot Designed for Atrial Fibrillation Education
Study type: INTERVENTIONAL
Overall status: RECRUITING
Phase: NA
Conditions: Atrial Fibrillation (AF)
Keywords: education
lifestyle modification
atrial fibrillation
generative AI
chatbot
artificial intelligence
Lead sponsor: University of Pittsburgh
Sponsor class: OTHER
First posted: 2024-11-12
Enrollment: 40
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: NA
Intervention model: SINGLE_GROUP
Intervention model description: Study design:

A single center, cross sectional, uncontrolled, open-label, non-randomized, single arm, experimental pilot study using an LLM based prompt-engineered education chatbot, to impart a Q and A format atrial fibrillation education to patients, prior to their first appointment with a cardiologist or a cardiac electrophysiologist. The prompt-engineered chatbot is built on LLM capabilities yet tailored with the help of physician provided, vetted, open source atrial fibrillation educational material as well as empathy enhancing scripts, and simplified language to a 6th-8th grade reading level. The chatbot would be utilized to enhance the patient-provider interaction by providing empathetic education to patients regarding atrial fibrillation, and empower them with the knowledge they would need to ask informed questions during their patient visit. The chatbot will also be able to direct patients to Heart Rhythm Society's open source educational videos for atrial fibrillation (UpBeat
Primary purpose: PREVENTION

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.

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.

Eligibility criteria: Inclusion criteria:

* Age greater than18 yrs
* Known diagnosis of atrial fibrillation

Exclusion criteria:

* Inability to text/use an electronic device
* Inability to read English

Primary outcomes:
measure: Empathy
timeFrame: From enrollment to end of interaction with chatbot, approximately an hour
Secondary outcomes:
measure: Knowledge
timeFrame: From enrollment to end of interaction with chatbot, approximately an hour
measure: Trust
timeFrame: From enrollment to end of interaction with chatbot, approximately an hour
Other outcomes:
Locations:
city: Pittsburgh
country: United States
facility: UPMC Heart and Vascular Institute
state: Pennsylvania
Study officials:
affiliation: UPMC Heart and Vascular Institute
name: Samir Saba, MD
role: PRINCIPAL_INVESTIGATOR
References: