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Objective: Chatbots have potential to deliver interactive self-management interventions but have rarely been studied in the context of hypertension or medication adherence. The objective of this study was to better understand patient information needs and perceptions of chatbots to support hypertension medication self-management.
Materials And Methods: Mixed methods were used to assess self-management needs and preferences for using chatbots. We purposively sampled adults with hypertension who were prescribed at least one medication. Participants completed questionnaires on sociodemographics, health literacy, self-efficacy, and technology use. Semi-structured interviews were conducted, audio-recorded, and transcribed verbatim. Quantitative data were analyzed using descriptive statistics, and qualitative data were analyzed using applied thematic analysis.
Results: Thematic saturation was met after interviewing 15 participants. Analysis revealed curiosity toward chatbots, and most perceived them as humanlike. The majority were interested in using a chatbot to help manage medications, refills, communicate with care teams, and for accountability toward self-care tasks. Despite general enthusiasm, there were concerns with chatbots providing too much information, making demands for lifestyle changes, invading privacy, and usability issues with deployment on smartphones. Those with overall positive perceptions toward chatbots were younger and taking fewer medications.
Discussion: Chatbot-related informational needs were consistent with existing self-management research, and many felt chatbots would be valuable if customizable and compatible with patient portals, pharmacies, or health apps.
Conclusion: Although most were not familiar with chatbots, patients were interested in interacting with them, but this varied. This research informs future design and functionalities of conversational interfaces to support hypertension self-management.
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http://dx.doi.org/10.1093/jamiaopen/ooab021 | DOI Listing |
J Empir Res Hum Res Ethics
September 2025
TOBB ETU School of Medicine, History of Medicine and Ethics Department, Ankara, Turkey.
This study investigates how scientists, educators, and ethics committee members in Türkiye perceive the opportunities and risks posed by generative AI and the ethical implications for science and education. This study uses a 22-question survey developed by the EOSC-Future and RDA AIDV Working Group. The responses were gathered from 62 universities across 208 universities in Türkiye, with a completion rate of 98.
View Article and Find Full Text PDFRes Integr Peer Rev
September 2025
Centre for Journalology, Ottawa Methods Centre, Ottawa Hospital Research Institute, Ottawa, ON, Canada.
Background: Artificial intelligence chatbots (AICs) are designed to mimic human conversations through text or speech, offering both opportunities and challenges in scholarly publishing. While journal policies of AICs are becoming more defined, there is still a limited understanding of how Editors in chief (EiCs) of biomedical journals' view these tools. This survey examined EiCs' attitudes and perceptions, highlighting positive aspects, such as language and grammar support, and concerns regarding setup time, training requirements, and ethical considerations towards the use of AICs in the scholarly publishing process.
View Article and Find Full Text PDFClin Pediatr (Phila)
September 2025
College of Medicine, King Saud University, Riyadh, Saudi Arabia.
To optimize the deployment of Generative Artificial Intelligence in health care, it's essential for health care professionals (HCPs) to understand these technologies' capabilities and constraints. This study explores HCPs' initial impressions and experiences using ChatGPT, a Generative Pre-trained Transformer, in Pediatric Critical Care Units (PICUs). By conducting focus groups with a diverse set of HCPs, we aimed to assess their awareness, utilization, perceived benefits, and concerns about incorporating ChatGPT into their PICUs.
View Article and Find Full Text PDFBMJ Open
September 2025
Department of Anesthesiology, The Third People's Hospital of Chengdu, Chengdu, Sichuan, People's Republic of China
Objective: This study aimed to explore orthopaedic patients' and families' experiences with artificial intelligence (AI)-driven chatbots for perioperative health information, focusing on usability, effectiveness and perceptions.
Design: A descriptive qualitative design was employed.
Setting: This study was conducted at a regional care centre for orthopaedics.
Sci Rep
September 2025
School of Design, Shenzhen City Polytechnic, Shenzhen, 5180381, Guangdong Province, China.
Space planning and interior design require not only technical precision but also creative thinking and spatial awareness. Although earlier research has examined the cognitive and educational elements that influence spatial ability, such as fuzzy DEMATEL and ISM-based models, these studies lack real-time decision-making support, machine-aided creativity, and practical implementation. To overcome these limitations, this study suggests an intelligent framework for enhancing interior design and space planning.
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