Abstract

The Health Belief Model is a theoretical model in which perceived barriers to action influence health behaviors. Unavailability of personalized, clear and timely healthcare information is a potential perceived barrier to effective patient self-care. Technological solutions like dialogue agents (DA) emulating health educators (HE) can address this barrier, reducing poor health outcomes in African American (AA) and Hispanic/Latino (H/L) heart failure (HF) patients. Objective: Use information collected inside and outside the clinical setting to develop a technological solution addressing the lack of personalized, clear, and timely information for AA and H/L HF patients, thereby reducing perceived barriers to self-care. Methods: We developed a prototype DA by combining existing literature with themes extracted from 20 transcripts of HE-HF patient interactions (18 AA & 2 H/L). Previously, we found that HE-HF patient interaction in clinical environments is controlled by HE and may not reveal information gaps of the patient. To identify these gaps and assess our prototype DA's performance, we conducted four 1:1 interviews with HF patients (3 AA & 1 H/L). In the interviews, we asked 12 questions on self-care routines (food, medication, understanding their condition & support systems) and then observed HF patient interaction with the DA. Interview content was compared to prior transcripts to uncover unique interview topics. We assessed the effectiveness of the DA by measuring the number of questions asked by patients and the accuracy of responses. Results: Participants reported a high level of satisfaction with their HE and displayed curiosity about topics not covered during HE-patient interactions, e.g., triggers for HF, impact on family, etc. A participant reported difficulty coming up with questions for the HE, attributing it to the clinical environment and language barriers. Questions asked by participants to the DA thematically matched our previous findings but had more personalized content, e.g., in the transcripts, patient questions addressed food items from the handout used by HE (e.g., ham, canned food, etc.) but when interacting with the DA, they were more likely to ask about foods they regularly ate (e.g., chocolate, fruit, etc.) When interacting with the DA, participants asked 4.25 questions on average. The DA responded correctly 44% of the time. Incorrect answers were either content errors (highly personalized question content or structure) and system errors (DA could not understand the user's speech.) Conclusion: There is a qualitative difference in the questions asked outside and inside a clinical setting by AA & H/L HF patients. Tech-based solutions built using information only from controlled clinical settings will replicate shortcomings in the existing HE process. By addressing needs of HF patients outside clinical settings, our DA is uniquely poised to reduce barriers to good self-care with personalized and timely information.

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