Towards conversational assistants for health applications: using C hat GPT to generate conversations about heart failure (2025)

Abstract: We explore the potential of ChatGPT to generate conversations focused on self-care strategies for African-American patients with heart failure, a domain with limited specialized datasets. To simulate patient-health educator dialogues, we employed four prompting strategies: aspects, African American Vernacular English, Social Determinants of Health (SDOH), and SDOH-informed reasoning. Conversations were generated across key self-care aspects— food, exercise, and fluid intake—with varying turn lengths and incorporated patient-specific SDOH attributes such as age, gender, neighborhood, and socioeconomic status. Our findings show that effective prompt design is essential. While incorporating SDOH and reasoning improves dialogue quality, ChatGPT still lacks the empathy and engagement needed for meaningful healthcare communication.

## introduction heart failure (hf), or congestive heart failure, occurs when the heart cannot pump enough blood to meet the body's needs. effective self-care-such as managing salt intake, staying hydrated, exercising, adhering to medications, and attending regular check-ups-is critical for managing the condition @xcite . however, african americans (aa) in the u.s. face disproportionately worse outcomes due to genetic factors, limited healthcare access, socioeconomic challenges, and lower health literacy @xcite . existing self-care materials often cater to a white, educated population, lacking cultural relevance for minority communities @xcite . this gap contributes to poor adherence and worsened outcomes. personalized education can improve self-care understanding and reduce readmissions to hospitals @xcite . this study is part of a broader project aimed at developing a culturally sensitive conversational agent to support aa patients with heart failure in asking self-care related questions. a significant challenge is the lack of real-world patient-centered conversational data from underrepresented communities. to address this, we recruited three patient educators (pes) foot_0 to provide heart failure education to 18 aa and 2 hispanic/latino (h/l) patients @xcite . initial analysis revealed that educators dominated the conversations, with patients contributing less. the key topics discussed during these sessions included exercise, fluid intake, symptom management, sleep, weight management, familial aspects, and salt intake. unlike the recorded interactions, we wanted to generate conversational datasets that are initiated by patients and, more importantly, personalized based on the social determinants of health (sdoh) features. with the advancement of large language models (llms), we thought to examine different prompting strategies and evaluate whether they could be used for creating synthetic conversational datasets. our study is a feasibility assessment aimed at exploring chatgpt's ability to generate self-care conversations and its ability to adapt its responses based on varying prompts. the dataset is publicly available foot_1 . in this paper, we look into 4 different prompting approaches that will supplement real-world interactions to support the development of a patient-driven dialogue system. in summary, our main goal is to explore the potential of chatgpt in generating simulated conversations when framed within the context of selfcare for african american patients with heart failure. additionally, we aimed to determine whether chatgpt could be used to create personalized dialogues based on individual patient characteristics and whether the quality of these conversations improves when using reasoning. specifically, we focused on addressing the following key questions: additionally, natural language processing (nlp) is being leveraged to create diabetes self-care corpus @xcite , demonstrating the potential of ai and language technologies to enhance patient communication and health management. prompting recent advancements in llms have been driven by scaling up both model size and training data, resulting in improved performance and sample efficiency @xcite . researchers have explored various prompting techniques to enhance llm capabilities, starting with few-shot prompting @xcite , followed by more advanced methods such as chain-of-thought prompting @xcite and chain-of-thought with self-consistency @xcite . to address the remaining challenges, new approaches like tree-of-thought prompting have been introduced @xcite , where each "thought" is a coherent language sequence representing an intermediate step toward problemsolving. reasoning is a crucial capability for complex problem-solving. a comprehensive overview of reasoning strategies in llms is provided in @xcite , covering commonsense reasoning @xcite , mathematical reasoning @xcite , and symbolic reasoning @xcite . the react framework @xcite further integrates reasoning with action in a unified task. this reasoning ability is especially critical in healthcare contexts, where accurate, informed decision-making is essential. with the release of large-scale medical dialogue datasets, e.g., meddialog @xcite , meddg @xcite , medical dialogue response generation attracts increasing attention. @xcite undertakes the task of enhancing and fine-tuning the llama model with a dataset of approximately 100,000 patient-doctor dialogues. in @xcite , the authors used llm to extract sdoh features of housing, employment, transportation, parental status, relationship, and emotional support from the electronic health record (ehr) data. ## generating simulated conversations in the absence of a specialized dataset focused on self-care strategies for african-american patients with heart failure, we employed four progressively refined prompting strategies to generate simulated conversations. chatgpt 3.5-turbo and 4 were used to generate simulated conversations using four distinct prompting strategies: aspect, african american vernacular english (aave), social determinants of health (sdoh), and sdoh-informed reasoning. in our initial prompts to chatgpt, even though we specified it to be a patient educator, the model continued advising the patient to consult a doctor. to address this, we added a specific instruction to avoid phrases related to consulting a healthcare provider, as shown in figure 2 . furthermore, in our earlier attempts, there was no defined structure for the generated conversations. to achieve this, we introduced a specific conversation structure, where each exchange between the participants follows a predefined format: @xcite . this structure explicitly separates the speaker from their utterance. recognizing that patients with heart failure face significant challenges managing their health, we emphasized the need for empathetic communication from patient educators. since many patients have lower health literacy compared to their general literacy @xcite , we instructed the model to present medical information in simple terms. additionally, to accommodate different conversational styles, we experimented with various dialogue lengths, as some patients might prefer shorter exchanges while others engage in longer conversations. for each approach, conversations were created with varying numbers of conversation turns {5, 10, 15} and across different aspects of {food, water, exercise}. for the domain and aave approaches (approaches 1 and 2), conversations were also generated with varying temperature settings {0, 0.5, 0.7, 1.0}, which influenced the model's response variability. to maintain clarity and avoid overwhelming the patient, we also prompted the model to limit each educator's turn to 20 words, using plain english throughout (figure 5 ). these adjustments were aimed at creating more realistic dialogues. ## evaluation in total, 144 conversations were generated for the aspect approach, 72 for aave, and 432 for the sdoh and sdoh-informed reasoning approaches, resulting in a rich and diverse set of simulated dialogues for evaluation. we employed both qualitative and quantitative methods to evaluate the generated conversations. for the qualitative analysis, we distributed a questionnaire to 10 nlp phd students specializing in nlp in healthcare, none of whom were involved in the research. each participant was asked to review 3 conversations per prompt and to provide feedback based on targeted questions. the conversation samples were randomly selected to be shared with the evaluators. the qualitative questionnaire can be found in appendix a. all the questions were likert-scale questions (1-5, with 5 being the highest score and 1 being the lowest score), with some including follow-up questions. seven students responded, with each reviewing three conversations per prompt, resulting in a total of 84 conversations being evaluated (4*21 conversations for each prompt). while even 84 conversations are a limited sample size, the key findings of our study remain consistent. for both the sdoh and the sdoh-informed reasoning approach, we ensured that sdoh features remained consistent across evaluations. this allowed for more meaningful comparisons between the two approaches, even though the conversations themselves might be different. for the quantitative analysis, we assessed whether the system complied with some of the instructions provided in the prompts, focusing on the number of conversation exchanges and adherence to the correct format @xcite . we calculated the percentage of conversations that followed the correct number of turns (round adherence ratio), as well as the ratio of conversations that adhered to the required format (format adherence ratio). the results, presented in ## conclusion and future work this study assessed the capabilities of chatgpt (versions 3.5-turbo and 4) in generating simulated conversations related to self-care strategies for african-american patients with heart failure. simulated conversations were generated using four distinct prompts: aspect, african american vernacular english (aave), social determinants of health (sdoh), and sdoh-informed reasoning. our findings highlight the critical role of prompt design, revealing that while chatgpt can incorporate sdoh features and improve dialogue quality by generating reasoning prior to the conversation, further improvements are needed. specifically, there is a clear need to refine the conversational style to make interactions feel more engaging and empathetic, an essential element in healthcare communication. in the future, we plan to conduct more in-depth evaluations, including assessments of whether the aave used is linguistically correct. we also aim to develop a task-oriented dialogue system specifically designed to support the selfcare needs of african-american patients with heart failure, leveraging these simulated conversations. the system will incorporate sdoh features of age, gender, neighborhood, and socio-economic status to inform the educator's responses. while we acknowledge that the generated conversations are not perfect and may contain inaccuracies, we intend to integrate neuro-symbolic rules to help validate and ensure the reliability of the generated outputs.