Digital health technologies (DHTs) represent a promising strategy to improve access to HTS (HIV testing services), particularly among underserved higher-risk populations often missed by current programming, including young adults under 25 years. In 2017, Kenya’s Ministry of Health introduced BeSure™, a DHT providing information on HIV, self-testing, and facility geo-location. Given increased risks for HIV among injured populations, this study assessed the acceptability of BeSure™ as a DHT for enhancing HTS in a Kenyan emergency department. Using purposive sampling, participants were provided a brief description of the tool BeSure™ and then completed in-depth interviews using a semistructured guide between August and November 2023. Deductive and inductive analyses were applied using a codebook based on a published framework for healthcare intervention acceptability, examining core themes of affect, burden, ethicality, coherence, opportunity cost, and perceived effectiveness. Among 24 participants, the median age was 25, half were female, and 58% had achieved secondary education or below. Few participants (21%) were aware of BeSure™ prior to data collection. Barriers to awareness included limited marketing of the tool and apathy toward health-related matters. However, strategic advertisement within healthcare encounters and through social media platforms including TikTok and Facebook (especially for young adult participants) could facilitate awareness. Barriers to potential use include low access to technology in rural communities, persisting stigma toward HIV, and low perceived HIV risk (especially among older participants). Despite these barriers, participants across age groups found the tool widely acceptable across the predetermined domains. These qualitative data highlight the acceptability of DHTs for HTS enhancement among injured populations in Nairobi, Kenya. Findings underscore the limited awareness of BeSure™ among this higher-risk population, suggesting that targeted advertisement, demand creation, and stigma reduction strategies are critical to successful implementation of these technologies.
Epistemic and ethical limits of large language models in evidence-based medicine: from knowledge to judgment
BackgroundThe rapid evolution of general large language models (LLMs) provides a promising framework for integrating artificial intelligence into medical practice. While these models are capable