A chatbot for hypertension self-management support: user-centered design, development, and usability testing

Hypertension Management to Reduce Racial/Ethnic Disparities: Clinical and Community-Based Interventions

Purpose of the Review

Hypertension remains a major public health concern globally and in the United States with significant racial/ethnic disparities in prevalence, treatment, and control. Despite effective treatments, undiagnosed or uncontrolled hypertension persists, leading to an increased risk of cardiovascular disease and substantial healthcare costs. Addressing hypertension disparities requires a comprehensive approach, integrating clinical interventions with community-based strategies. This review examines the current landscape of clinic-and community-based interventions designed to improve hypertension management and reduce disparities.

Recent Findings

Clinic-based approaches highlighted include implementing evidence-based guidelines, using treatment algorithms, promoting self-management, integrating digital health technologies, and incorporating team-based care approaches. Community interventions discussed involve lifestyle modification programs, faith-based initiatives, trusted community spaces, culturally-tailored health education, engaging community health workers, and collaborative care models linking clinics and communities. This review stresses the importance of addressing SDoH, fostering community engagement, and delivering culturally competent care. Strengthening clinic-community linkages, evaluating long-term effectiveness and cost-effectiveness, leveraging technology and innovation, and addressing gaps in research for underrepresented groups are key priorities for advancing health equity in hypertension management.

Summary

To effectively close the widening gap in hypertension disparities, collaborative multi-level efforts integrating clinical excellence and community empowerment are essential to mitigate the disproportionate burden of hypertension among racial/ethnic minority populations.

Person-based design and evaluation of MIA, a digital medical interview assistant for radiology

Introduction

Radiologists frequently lack direct patient contact due to time constraints. Digital medical interview assistants aim to facilitate the collection of health information. In this paper, we propose leveraging conversational agents to realize a medical interview assistant to facilitate medical history taking, while at the same time offering patients the opportunity to ask questions on the examination.

Methods

MIA, the digital medical interview assistant, was developed using a person-based design approach, involving patient opinions and expert knowledge during the design and development with a specific use case in collecting information before a mammography examination. MIA consists of two modules: the interview module and the question answering module (Q&A). To ensure interoperability with clinical information systems, we use HL7 FHIR to store and exchange the results collected by MIA during the patient interaction. The system was evaluated according to an existing evaluation framework that covers a broad range of aspects related to the technical quality of a conversational agent including usability, but also accessibility and security.

Results

Thirty-six patients recruited from two Swiss hospitals (Lindenhof group and Inselspital, Bern) and two patient organizations conducted the usability test. MIA was favorably received by the participants, who particularly noted the clarity of communication. However, there is room for improvement in the perceived quality of the conversation, the information provided, and the protection of privacy. The Q&A module achieved a precision of 0.51, a recall of 0.87 and an F-Score of 0.64 based on 114 questions asked by the participants. Security and accessibility also require improvements.

Conclusion

The applied person-based process described in this paper can provide best practices for future development of medical interview assistants. The application of a standardized evaluation framework helped in saving time and ensures comparability of results.

Assessing the Quality and Reliability of AI-Generated Responses to Common Hypertension Queries

INTRODUCTION

The integration of artificial intelligence (AI) in healthcare, particularly through language models like ChatGPT and ChatSonic, has gained substantial attention. This article explores the utilization of these AI models to address patient queries related to hypertension, emphasizing their potential to enhance health literacy and disease understanding. The study aims to compare the quality and reliability of responses generated by ChatGPT and ChatSonic in addressing common patient queries about hypertension and evaluate these AI models using the Global Quality Scale (GQS) and the Modified DISCERN scale.

METHODS

A virtual cross-sectional observational study was conducted over one month, starting in October 2023. Ten common patient queries regarding hypertension were presented to ChatGPT (https://chat.openai.com/) and ChatSonic (https://writesonic.com/chat), and the responses were recorded. Two internal medicine physicians assessed the responses using the GQS and the Modified DISCERN scale. Statistical analysis included Cohen's Kappa values for inter-rater agreement.

RESULTS

The study evaluated responses from ChatGPT and ChatSonic for 10 patient queries. Assessors observed variations in the quality and reliability assessments between the two AI models. Cohen's Kappa values indicated minimal agreement between the evaluators for both the GQS and Modified DISCERN scale.

CONCLUSIONS

This study highlights the variations in the assessment of responses generated by ChatGPT and ChatSonic for hypertension-related queries. The findings highlight the need for ongoing monitoring and fact-checking of AI-generated responses.

Roles, Users, Benefits, and Limitations of Chatbots in Health Care: Rapid Review

BACKGROUND

Chatbots, or conversational agents, have emerged as significant tools in health care, driven by advancements in artificial intelligence and digital technology. These programs are designed to simulate human conversations, addressing various health care needs. However, no comprehensive synthesis of health care chatbots' roles, users, benefits, and limitations is available to inform future research and application in the field.

OBJECTIVE

This review aims to describe health care chatbots' characteristics, focusing on their diverse roles in the health care pathway, user groups, benefits, and limitations.

METHODS

A rapid review of published literature from 2017 to 2023 was performed with a search strategy developed in collaboration with a health sciences librarian and implemented in the MEDLINE and Embase databases. Primary research studies reporting on chatbot roles or benefits in health care were included. Two reviewers dual-screened the search results. Extracted data on chatbot roles, users, benefits, and limitations were subjected to content analysis.

RESULTS

The review categorized chatbot roles into 2 themes: delivery of remote health services, including patient support, care management, education, skills building, and health behavior promotion, and provision of administrative assistance to health care providers. User groups spanned across patients with chronic conditions as well as patients with cancer; individuals focused on lifestyle improvements; and various demographic groups such as women, families, and older adults. Professionals and students in health care also emerged as significant users, alongside groups seeking mental health support, behavioral change, and educational enhancement. The benefits of health care chatbots were also classified into 2 themes: improvement of health care quality and efficiency and cost-effectiveness in health care delivery. The identified limitations encompassed ethical challenges, medicolegal and safety concerns, technical difficulties, user experience issues, and societal and economic impacts.

CONCLUSIONS

Health care chatbots offer a wide spectrum of applications, potentially impacting various aspects of health care. While they are promising tools for improving health care efficiency and quality, their integration into the health care system must be approached with consideration of their limitations to ensure optimal, safe, and equitable use.

User-Friendly Chatbot to Mitigate the Psychological Stress of Older Adults During the COVID-19 Pandemic: Development and Usability Study

BACKGROUND

To safeguard the most vulnerable individuals during the COVID-19 pandemic, numerous governments enforced measures such as stay-at-home orders, social distancing, and self-isolation. These social restrictions had a particularly negative effect on older adults, as they are more vulnerable and experience increased loneliness, which has various adverse effects, including increasing the risk of mental health problems and mortality. Chatbots can potentially reduce loneliness and provide companionship during a pandemic. However, existing chatbots do not cater to the specific needs of older adult populations.

OBJECTIVE

We aimed to develop a user-friendly chatbot tailored to the specific needs of older adults with anxiety or depressive disorders during the COVID-19 pandemic and to examine their perspectives on mental health chatbot use. The primary research objective was to investigate whether chatbots can mitigate the psychological stress of older adults during COVID-19.

METHODS

Participants were older adults belonging to two age groups (≥65 years and <65 years) from a psychiatric outpatient department who had been diagnosed with depressive or anxiety disorders by certified psychiatrists according to the Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) (DSM-5) criteria. The participants were required to use mobile phones, have internet access, and possess literacy skills. The chatbot's content includes monitoring and tracking health data and providing health information. Participants had access to the chatbot for at least 4 weeks. Self-report questionnaires for loneliness, depression, and anxiety were administered before and after chatbot use. The participants also rated their attitudes toward the chatbot.

RESULTS

A total of 35 participants (mean age 65.21, SD 7.51 years) were enrolled in the trial, comprising 74% (n=26) female and 26% (n=9) male participants. The participants demonstrated a high utilization rate during the intervention, with over 82% engaging with the chatbot daily. Loneliness significantly improved in the older group ≥65 years. This group also responded positively to the chatbot, as evidenced by changes in University of California Los Angeles Loneliness Scale scores, suggesting that this demographic can derive benefits from chatbot interaction. Conversely, the younger group, <65 years, exhibited no significant changes in loneliness after the intervention. Both the older and younger age groups provided good scores in relation to chatbot design with respect to usability (mean scores of 6.33 and 6.05, respectively) and satisfaction (mean scores of 5.33 and 5.15, respectively), rated on a 7-point Likert scale.

CONCLUSIONS

The chatbot interface was found to be user-friendly and demonstrated promising results among participants 65 years and older who were receiving care at psychiatric outpatient clinics and experiencing relatively stable symptoms of depression and anxiety. The chatbot not only provided caring companionship but also showed the potential to alleviate loneliness during the challenging circumstances of a pandemic.