JMIR Human Factors

Online medical consultation (OMC) services have gained considerable attention as an integral component of telemedicine. Recently, AI has been increasingly integrated into OMC platforms, facilitating more efficient consultations and clinical decision-making. AI-driven OMC services can provide preliminary triage, medication guidance, and diagnostics for multiple medical conditions. Despite the availability and potential benefits of AI-driven OMC services, public acceptance and willingness to pay (WTP) for these services remain low.

Digital health platforms can expand access to HIV care, but among men who have sex with men (MSM) and transgender people living with HIV and AIDS in Nigeria, adoption is shaped by structural stigma, criminalization, and fear of disclosure as much as by system functionality. Teleconsultation and medication-delivery platforms offer alternative pathways to care, but their acceptance within marginalized populations cannot be assumed and requires empirical investigation.

Telemedicine adoption among health care professionals is hindered by resistance to change (RTC), comprising routine-seeking, emotional reactivity, short-term focus, and cognitive rigidity, which interacts with perceived ease of use (PEU) and anxiety within the Technology Acceptance Model framework.

Digital health interventions for psychosis, like SloMo, leverage smartphone technology to help transfer learning from therapy to real-life situations. Usage relies on motivation, recall, and awareness. Wearable devices that track physiological signs of stress can boost engagement by encouraging the use of coping strategies when most needed.

AI has the potential to enhance clinical decision-making in high-acuity settings such as intensive care units (ICUs) and emergency departments (EDs). However, despite promising performance, many AI-driven clinical decision support systems (AI-CDSSs) face poor adoption due to issues of trust, workflow disruption, and alert fatigue. Understanding the human factors that shape clinician acceptance is critical to guide safe and effective implementation of AI-CDSS in acute care. Theoretical frameworks, including the Systems Engineering Initiative for Patient Safety (SEIPS) 2.0 model and the technology acceptance model (TAM), suggest that successful adoption requires addressing sociotechnical interactions among clinician trust, system design, organizational readiness, and task complexity, yet few empirical studies have applied these frameworks to AI-CDSSs in acute care settings.

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Augmented reality (AR) glasses with waveguide optics and facial recognition may enhance patient identification and clinical workflows. However, their usability among physicians has not yet been evaluated.

Administrative workload in general practice limits time for direct patient care. AI-assisted documentation has been proposed as a way to reduce the documentation burden, but evidence from routine primary care settings remains limited.

The ready-made garments (RMG) industry is a crucial component of Bangladesh’s economy, using over 4 million workers from low-income backgrounds who often neglect their health care needs. Historically, this sector has faced criticism for labor exploitation, unsafe working conditions, and rights violations, as highlighted by tragic accidents resulting in loss of life. Compliant factories may uphold higher labor standards, but many noncompliant factories expose workers to poor conditions, increasing health risks. The COVID-19 pandemic intensified vulnerabilities, leading to widespread factory closures and job losses, increased health risks, and left millions of workers without wages. Although the government attempted to provide some relief, it fell short in offering job security, social protection, health services, and emergency assistance. Limited access to technology due to digital literacy gaps further hinders these workers, who primarily use basic mobile phones for communication rather than accessing health or emergency services. Thus, there is a pressing need to develop a sustainable system that capitalizes on their existing technological familiarity.

Individuals with germline variants face complex, preference-sensitive medical decisions under psychological distress. Structural constraints in genetic counseling create support gaps during waiting periods, prompting patients to rely on fragmented or misleading online information. Conversational AI may offer low-threshold, continuous informational support, yet methods for its responsible integration into clinical care remain unclear.

Hepato-pancreato-biliary (HPB) oncology involves complex surgical planning and multidisciplinary decision-making that present significant cognitive, spatial, and communicative challenges. While augmented reality (AR) technologies are emerging as valuable tools in surgical planning, most existing systems are developed in a technologically deterministic manner, often without consideration of patients’ lived experiences or the collaborative needs of multidisciplinary clinical teams. This limits their integration into complex workflows and their potential for meaningful clinical impact.