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BACKGROUND: Falls are a leading cause of morbidity, functional decline, and premature care entry among older adults. Assistive living technologies (ALTs) are increasingly used for fall prevention, detection, and risk monitoring, but their value depends on how older adults, caregivers, and care staff experience them in practice.

BACKGROUND: The rapid expansion of digital health care technologies has substantially transformed nursing practice and has been accompanied by growing concerns regarding technology-related stress and burnout among nurses. Despite the growing body of literature, the intellectual structure and thematic evolution of this field remain insufficiently explored from a bibliometric perspective.

BACKGROUND: Patient safety reporting studies usually examine intention, submitted reports, or reporting rates, leaving unclear what happens after a report is started but before it is submitted, deleted, or left unfinished. We evaluated whether autosave and fixed-time reminders make this hidden stage observable.

BACKGROUND: The use of large language model (LLM)-based artificial intelligence (AI) to assess the quality of outpatient mental health documentation represents an understudied application. Critical questions remain about the effects of exposing human auditors to AI-generated recommendations on auditor efficiency, agreement, and patterns of decision-making.

BACKGROUND: Open foundation models designed for on-device and edge deployment, exemplified by the Qwen 3.5 small series (March 2, 2026) and Gemma 4 (April 2, 2026), challenge the default of routing all clinical natural language processing through commercial cloud application programming interfaces (APIs).

OBJECTIVE: Developing technology may assist paramedics in the competing tasks of providing life-saving care and accurately documenting that care. The objective of this study was to identify and classify paramedic user requirements for speech recognition (SR)-aided documentation.

OBJECTIVES: This study aimed to design and evaluate the usability of an electronic health record (EHR)-integrated clinical decision support prototype supporting pediatric surgical site infection (SSI) risk estimation and preoperative optimization using a multiphase human-centered design approach.

BACKGROUND: Diagnostic errors are common in medical care and can significantly affect patient outcomes. Symptom checker apps promise to help patients understand their symptoms, possible diagnoses, and actions to take, but lack evidence on accuracy, safety, and usability.

BACKGROUND: Sepsis is a life-threatening condition in which delayed recognition and treatment are associated with increased mortality. While predictive models such as Epic's Early Detection of Sepsis Model (ESM) were developed to support early intervention, their real-world effects after integration into clinical workflows remain difficult to evaluate.

BACKGROUND: Although interruptive alerts, or pop-ups, are one of the most common forms of clinical decision support (CDS) tools within the electronic health record (EHR), they are often ignored. Interruptive alerts have the potential to enhance patient care and reduce clinician burden, but end-users frequently experience alert fatigue and override alerts, defeating their purpose. Few studies have elicited the perspectives of all stakeholder groups involved in the development, experience, and [...]