Patient Perceived Empathy of an AI Chatbot for Atrial Fibrillation Education

Patient Perceived Degree of Empathy Exhibited by Physician Guided, Prompt Engineered, Generative AI Chatbot Designed for Atrial Fibrillation Education

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.

Study Overview

• Status: Completed
• Conditions: Atrial Fibrillation (AF)
• Intervention / Treatment: Behavioral: Atrial Fibrillation Education

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.

• Study Type: Interventional
• Enrollment (Actual): 40
• Phase: Not Applicable

Contacts and Locations

Study Locations

• United States
  • Pennsylvania
    • Pittsburgh, Pennsylvania, United States, 15213
      • UPMC Heart and Vascular Institute

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

Description

Inclusion criteria:

• Age greater than18 yrs
• Known diagnosis of atrial fibrillation

Exclusion criteria:

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

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Prevention
• Allocation: N/A
• Interventional Model: Single Group Assignment
• Masking: None (Open Label)

Number of Arms

1

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: Pilot interventional arm; Single arm, wherein all consenting participants undergoing interaction with chatbot for atrial fibrillation education.	Behavioral: Atrial Fibrillation Education; Atrial fibrillation education rendered through a generative AI chatbot, using an iPAD. The intervention itself is an approximately 1 hour long interaction with the chatbot or approximately 10 questions about atrial fibrillation, for 1 single interaction.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Empathy	Assess patient perceived degree of empathy exhibited by chatbot responses. The degree of empathy will be measured using a pre- and post- chatbot interaction 7 point Likert scale survey, with a higher score suggesting a higher level of perceived empathy. The title of the survey will be "Likert scale based survey for perceived empathy and trust," with the lowest score being 4 and the highest score being 28. Survey form available upon reasonable request.	From enrollment to end of interaction with chatbot, approximately an hour

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Knowledge	Assess knowledge about AFib imparted by chatbot based on patient survey questions. Knowledge will be measured by administering a pre- and post- bot interaction survey, titled "Jessa Atrial Fibrillation Knowledge Questionnaire," with a minimum score of 0 and a maximum score of 16. A higher score reflects that the patient is more knowledgeable about atrial fibrillation. Survey form available upon reasonable request.	From enrollment to end of interaction with chatbot, approximately an hour
Trust	Assess impact of chatbot interaction on patient perceived trust in the provider. The degree of trust will be measured using a pre- and post- chatbot interaction 7 point Likert scale survey, with a higher score suggesting a higher level of perceived empathy. The title of the survey will be "Likert scale based survey for perceived empathy and trust," with the lowest score being 4 and the highest score being 28. Survey form available upon reasonable request.	From enrollment to end of interaction with chatbot, approximately an hour

Collaborators and Investigators

• Sponsor: University of Pittsburgh
• Investigators: Principal Investigator: Samir Saba, MD, UPMC Heart and Vascular Institute

Study record dates

Study Major Dates

• Study Start (Actual): November 25, 2024
• Primary Completion (Actual): May 2, 2025
• Study Completion (Actual): May 2, 2025

Study Registration Dates

• First Submitted: November 8, 2024
• First Submitted That Met QC Criteria: November 8, 2024
• First Posted (Actual): November 12, 2024

Study Record Updates

• Last Update Posted (Estimated): July 8, 2025
• Last Update Submitted That Met QC Criteria: July 2, 2025
• Last Verified: November 1, 2024

More Information

Terms related to this study

• Keywords: atrial fibrillation; artificial intelligence; education; lifestyle modification; chatbot; generative AI
• Additional Relevant MeSH Terms: Cardiovascular Diseases; Pathologic Processes; Heart Diseases; Arrhythmias, Cardiac; Atrial Fibrillation
• Other Study ID Numbers: STUDY24010018

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO
• IPD Plan Description: Deidentified IPD may be shared at the discretion of investigators, upon reasonable request.

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No