Journal of Medical Internet Research

The growing demand for home-based care, driven by rapid population aging, has accelerated the development of internet-based home care. Despite its emerging status, a subset of loyal patients has consistently used internet-based home care with high frequency, showing strong commitment and a willingness to recommend it to others. Understanding their experiences with trust-building among loyal patients is essential to optimize and scale this service model. However, few studies have specifically explored the experience of trust-building among this patient group, leaving a significant gap in the literature.

Studies suggest that the introduction of electronic health records (EHRs) has decreased the efficiency of clinical practice and increased clinician workload for US-based physicians. Most studies involve clinicians in primary care settings. Less is known about other health care settings, subspecialist clinicians, or whether markers of efficiency and workload change over time.

Dementia presents a pressing public health challenge, with informal caregivers (ICs) playing a pivotal role in supporting people with dementia. Digital interventions, such as the World Health Organization (WHO) iSupport, offer scalable, self-guided psychosocial and educational support for caregivers. However, effective implementation relies on strong usability, particularly for older adults with varying levels of digital literacy.

Prognostic information is essential for decision-making in breast cancer management. In recent years, trials and clinical practice have emphasized genomic prognostication tools, despite clinicopathological methods being more affordable and accessible. PREDICT v3 is one such tool with promising results across cohorts. Advances in machine learning (ML), transfer learning, and ensemble methods provide opportunities to enhance these approaches, especially where missing data and model assumptions differ across diverse populations.

Technology-facilitated sexual violence and abuse (TFSVA) encompasses sexual harms perpetrated through digital means. Rapid developments in this field pose challenges in precisely defining and measuring TFSVA. Consequently, existing measures may fail to capture its multifaceted nature.

Secondary use of clinical data offers unprecedented opportunities to rapidly conduct large-scale research and improve patient care. However, incomplete understanding of data quality requirements for a study often causes significant delays in executing analyses and validating results. Current practice has largely followed 2 paths. First, multi-institutional networks have developed general data quality programs, but these are typically tied to unique network characteristics and do not address study-specific requirements well. Second, models have been proposed to formalize the requirements for data fitness analyses without extending to the methods needed to meet these requirements. More recently, tools have been developed to conduct cohort-centric screening, focusing on generally applicable structural checks such as missingness or facial implausibility. These provide a first level of information but incompletely capture the fitness requirements of an analysis. In turn, investigators conduct per-study exploratory analyses, but these efforts are typically ad hoc and partially reported, which can hinder reproducible science and delay advances in patient care. Analogously to advances over the past decade in data modeling and reproducible analytics, there is a need for a more systematic, capable approach to study-specific data quality assessment (SSDQA). We discuss such a model, which guides improved SSDQA design and implementation, including metadata for consistent annotation and reporting of data quality assessment results. The model integrates theoretical principles of data quality testing with pragmatic considerations of application to clinical data, providing a consistent approach to specifying data quality assessment checks. Additionally, it proposes to regularize check application through a standard set of options. The SSDQA model builds on current practice, providing a path toward more complete, sound, and reproducible assessments. These characteristics foster multidisciplinary collaboration to identify data quality issues that, in turn, inform decisions about study design and provide important context that has a bearing on adoption of results.

Digital ecological momentary assessment (EMA) collects data on experiences as they occur in daily life, capturing dynamic, context-sensitive experiences often missed by retrospective reporting. While EMA shows promise for pediatric health research, preadolescents have distinct socioemotional and cognitive characteristics likely to affect engagement. Existing reviews have not focused on the acceptability and feasibility of EMA protocols for this age group.

Locomotor capacity, encompassing endurance, balance, muscle strength, muscle function, muscle power, and joint function of the body, is a key determinant of functional ability in older adults. Assessment tools based on digital technologies for objectively assessing locomotor capacity are increasingly being developed, but their reliability, validity, and clinical potential remain underexplored.

Emergency radiology requires highly accurate reporting under time constraints; yet, increasing workloads raise the risk of errors. While large language models (LLMs) show potential for proofreading in general radiology, their performance in emergency settings and non-English contexts remains unclear.

The German Health Data Utilization Act and the Digital Act aim to enhance health data sharing for health care and research in Germany and beyond while ensuring robust data protection. A key prerequisite is patients’ willingness to share their data for primary use (PU), such as medical care, and secondary use (SU), such as research. There is a lack of qualitative research examining patients’ perspectives on data sharing under the new legal framework, especially among vulnerable groups, such as those with mental health diseases.

Assessing Circumstances and Offering Resources for Needs (ACORN) is a US Department of Veterans Affairs (VA) clinical intervention designed to identify and address social needs to improve health and well-being among all veterans. We co-designed the ACORN Dashboard to facilitate access to real-time social needs and intervention data for VA clinical care teams and leadership.