JMIR Formative Research

Triple-negative breast cancer (TNBC) is an aggressive molecular subtype of breast cancer that needs to be identified accurately from pathology reports to inform decision-making. Large language models (LLMs) have been reported to show great potential in the automatic extraction of important oncology information from unstructured medical text.

User-centered design (UCD) processes often generate extensive lists of potential software features, necessitating prioritization methods to guide development. The MoSCoW method, categorizing features as “must have,” “should have,” “could have,” or “won’t have,” is widely used in software development for its simplicity and ease of adoption. Despite its popularity, limited evidence exists on its application within health informatics. The Decision Aid Navigator (DEAN) system, a clinical decision support tool, required prioritization of features for its administrative portal to ensure usability and alignment with health information technology (HIT) professionals’ needs.

Older adults living alone are at increased risk during medical emergencies, particularly due to delayed detection, information transmission, and physical access to the home. Digital health and Internet of Things (IoT)-based technologies offer opportunities to strengthen emergency response systems; however, evidence on the real-world implementation and performance of integrated home-based emergency assistance platforms remains limited.

An exploratory survey of health care–related X (formerly Twitter) users identified perceived challenges in digital professionalism, including exposure to misinformation, collegial disagreements, and unprofessional conduct, while highlighting gaps in institutional guidance and the need for clearer policies and digital-professionalism education.

This research letter describes the development and initial validation of SAFE-Nurse, an artificial intelligence–assisted patient safety incident reporting system for nurses, which demonstrated excellent content and face validity (average scale-level content validity index [S-CVI/Ave] 0.99; average scale-level face validity index [S-FVI/Ave] 1.00) and is ready to progress to usability testing.

Latina pregnant women and mothers of infants in the United States experience elevated risk for perinatal anxiety, stress, and depression alongside persistent barriers to culturally and linguistically responsive care. AI-powered digital health tools offer a potentially scalable approach to address these gaps, yet evidence on their acceptability and effectiveness for this population remains limited.

Only approximately half of adults with hypertension, a leading cardiovascular risk factor, achieve blood pressure (BP) control, with persistent disparities among low-income and minority populations. Wireless home BP monitoring (HBPM) with remote patient monitoring can improve BP control, but evidence is limited in medically underserved, predominantly Hispanic populations served by federally qualified health centers (FQHCs).

Despite strong evidence supporting the benefits of regular physical activity (PA) for managing coronary heart disease (CHD), PA advice remains infrequent in general practice. Barriers include the limited familiarity of general practitioners (GPs) with current PA guidelines and their application to CHD patients, their insufficient skills for delivering very brief advice, and time constraints. To our knowledge, no structured, theory-informed training has been reported in Germany to support GPs in delivering very brief PA advice in routine CHD care.

Among 80 wearable members stratified into quartiles of attainment of combined physical-activity and sleep recommendations, higher attainment showed age-adjusted associations with lower resting heart rate and BMI and higher heart rate variability. The lowest-attainment quartile incurred approximately 12-fold higher annual per-member health care spending than the other quartiles.

The growing interest in promoting child health requires minimally invasive biospecimen collection. Particularly during and since the COVID-19 pandemic, there has been increased interest in microsampling technology to democratize participation in research involving biospecimens, particularly for children. These advances mitigate barriers, such as scheduling, transportation, and psychosocial challenges. Tasso is a microsampling biomedical device that has gained popularity in research. It uses a vacuum-assisted capillary system that attaches to the upper arm and is considered minimally invasive, as it requires no venipuncture or phlebotomist. Yet, important gaps remain in understanding Tasso performance, particularly among pediatric populations where its administration is limited.

Veterans experiencing moral injury (ie, having witnessed or done something during military service that conflicts with one’s value system) may encounter challenges that affect engagement and learning in online contexts.

Depression prevalence is rising in adolescents and young adults (ages 10-24 years). Traditional screening methods for depression, such as the Patient Health Questionnaire (PHQ-9), are particularly challenging for adolescents. AI has potential to help with this, but the scarcity of annotated datasets highlights the need for off-the-shelf approaches. In this study, we investigate the feasibility of using state-of-the-art large language models (LLMs) to detect depressive symptoms in adolescent settings (ages 15-17 years).