JMIR Formative Research

The COVID-19 pandemic accelerated the adoption of telehealth as a key strategy within Brazil’s Unified Health System. In São Paulo State, digital health initiatives have been developed to implement telehealth-based care models across all 3 levels of health care.

Approximately 3.8 billion people lack access to essential health services, and diagnostic interpretation remains a major bottleneck in remote and resource-constrained settings. Limited access to specialists and the complexity of biomedical signal interpretation (eg, electrocardiogram [ECG] and electroencephalogram) contribute to delays in recognizing cardiovascular and neurological conditions.

Extracting accurate medication information from Thai hospital records presents challenges due to the narrative style of medical notes, which often combine Thai and English terminology. Named entity recognition (NER) serves as the foundational step for advanced clinical information extraction (IE) tasks, including medical concept normalization and relation extraction. This study aimed to establish a robust NER framework to address these difficulties by leveraging ontology-based annotation and pretrained transformer models.

Incorporating prespecified Google Trends indicators into leakage-controlled stacked-ensemble models improved a 2025 holdout prediction of subjective well-being by using 2022-2025 data from Japan’s 47 prefectures, reducing the mean squared error from 0.0050 to 0.0045.

The experiences of patients with COVID-19 and their families manifested the most devastating effects of family separation since the 1918 Spanish influenza pandemic and, with it, a call for solutions to patient isolation and its effect on family mental health.

Physical activity (PA) has an important role in the prevention and treatment of type 2 diabetes (T2D). Interventions with mobile-based technology (mobile health [mHealth]) seem promising in PA promotion, but their behavioral framework is often vague, and the implementation is seldom reported.

Although a general purpose (GPT-5), and a fine-tuned (ASReviewLab) artificial intelligence were able to rank abstracts for likely inclusion in a variety of Cochrane systematic reviews, some actually included studies were not highly ranked, necessitating human review of all abstracts.

Personalized behavioral recommendations through mobile apps have proven effective in preventing serious chronic diseases such as diabetes. Recent studies have primarily focused on optimizing personalized recommendations using reinforcement learning. However, the main problem with these approaches is that they focus on behavioral changes and overlook clinical outcomes.

Immune checkpoint inhibitors are widely used in oncology but can cause immune-related adverse events (irAEs), which may be severe or life-threatening if not detected early. Electronic patient-reported outcome (ePRO) symptom monitoring systems may facilitate timely recognition and management of irAEs. Usability testing is a critical stage in ePRO system development, yet no published examples of formal usability and acceptance testing exist.

Smartwatches have gained popularity for their potential to provide accurate measurements of various physiological parameters. However, the validity of energy expenditure (EE) across different smartwatch models remains a topic of ongoing investigation. Discrepancies between results obtained from different models and gold standard methods are particularly critical across varying exercise intensities and types, as validation studies have demonstrated overestimation when wearable activity monitors are compared with indirect calorimetry.

Prospective integration of large language model tools into a classical hematology challenging-cases conference was feasible, increased clinician familiarity and interest, and was perceived as diagnostically and educationally valuable.

This study analyzed social media posts from patients with congenital ichthyosis and their caregivers across Europe and found that users, primarily young women, discussed hygiene care, psychological impact, therapeutic challenges, and lack of disease awareness. This allowed for the identification of unmet needs and potential actions to improve patients’ quality of life.