Journal of Medical Internet Research

Real-time audiovisual connections between health care providers (HCPs) in neonatal care, known as TeleNeonatology (TeleNeo), can improve neonatal care. For patients and families, TeleNeo was found to improve patient outcomes and facilitate family-integrated and patient-centered care by ensuring timely access to expert involvement regardless of location, which can strengthen trust and reassurance. For clinicians and health care organizations, TeleNeo enables expert decision-making, fosters continuous professional development, promotes knowledge exchange between hospitals, and increases staff confidence in managing complex medical cases. However, organizational, technical, and infrastructural requirements can hinder successful implementation and sustained adoption of technological interventions, such as TeleNeo. Implementation can be time-consuming and may fail due to the challenges encountered during the implementation process, particularly when incorporating the intervention into existing workflows across multiple hospitals. Nevertheless, there are case studies that demonstrate successful implementation of TeleNeo into routine care. Informed by international experience and theoretical underpinnings of implementation science, this tutorial presents a toolkit to provide step-by-step guidance for health care institutions considering TeleNeo implementation. The objective of the toolkit is to help health care organizations effectively and efficiently implement TeleNeo in their neonatal care pathways. The toolkit provides a structured guide that encompasses the entire implementation process, from initial ideas to stakeholder engagement, workflow design, and program evaluation. It includes checklists, planning guidelines, and tools to help teams design a customized implementation strategy for their institution.

Healthy lifestyle behaviors, including a balanced diet, regular physical activity, adequate sleep, avoiding tobacco, moderating alcohol consumption, and stress management, are associated with reduced risk of chronic disease and improved well-being. In recent years, digital interventions have emerged as cost-effective platforms for promoting these behaviors, yet few have been integrated into services delivered by certified exercise practitioners (CEPs) or focused on multiple domains of health.

Surgical site infections (SSIs) remain a major cause of health care–associated infections, and early prediction is essential for improving patient outcomes. Machine learning (ML) has shown potential for SSI prediction; however, clinical implementation requires models that are both accurate and explainable. Despite recent progress in explainable ML, its clinical application to SSI prediction remains limited.

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Implementing digital health technologies is challenging because implementation is shaped by interacting technical, organizational, professional, and contextual factors. Although implementation frameworks such as the Nonadoption, Abandonment, Scale-Up, Spread, and Sustainability (NASSS) framework support understanding this complexity, translating them into practical tools for routine implementation remains difficult.

Subjective cognitive decline (SCD) and mild cognitive impairment (MCI) are heterogeneous clinical states that may represent early or at-risk stages of Alzheimer disease (AD) and other dementias in some individuals. Improved characterization and risk stratification in these populations may facilitate timely evaluation and intervention. Electroencephalography (EEG), a noninvasive, cost-effective neurophysiological technique with high temporal resolution, holds significant potential for elucidating neural mechanisms and providing candidate neurophysiological markers associated with SCD and MCI.

As populations age globally, AI-enabled digital health interventions (DHIs) are increasingly being adopted to support integrated, person-centered care for older adults. However, despite rapid advances in AI technologies, their economic value in older care remains poorly understood.

There is tremendous enthusiasm for the use of AI in health care because of the ability to analyze existing data for preventative, diagnostic, and treatment support. Agentic AI can feasibly provide access to large real-world datasets for the generation of real-world evidence for health care and clinical applications to health care providers, researchers, and administrators without access to large analytic programming resources.

Nonpharmaceutical interventions (NPIs), including digital contact tracing (DCT), are central to control the spread of airborne pathogens. Nevertheless, the effectiveness of individual interventions and the role of personal behavior remain insufficiently understood.

Temporal relation extraction (TRE) in clinical narratives is crucial for understanding patient history, disease progression, and treatment pathways. However, it remains challenging due to limited annotated data and to the complexity of clinical text, including domain-specific terminology, inconsistent information, and implicit temporal reasoning. These challenges are particularly acute for rare diseases, where information such as the date of diagnosis or the onset of key phenotypes is rarely available in structured data, despite being essential for estimating diagnostic delay and reconstructing the natural history of the disease.

A key symptom of depression is reduced behavioral activation, namely, low activity levels and reduced meaningful engagement with the external environment. Thus, objective and timely measures of activity levels are useful tools to precisely track individuals’ activity levels during treatment. Prior adult depression studies have shown that activity levels measured using passive sensing (eg, step counts and time spent away from home) predict depression relapse, persistence, and poor response to psychosocial interventions. However, there is scarce research on how passive sensing measures relate to behavioral activation, especially in late-life depression.

AI is increasingly integrated into prostate cancer diagnostics, with the potential to improve accuracy and efficiency. However, it also raises important questions about the conditions and barriers that may influence its successful implementation in this clinical context.