Journal of Medical Internet Research

Patients with impaired glucose tolerance (IGT) identified during pregnancy who do not develop gestational diabetes mellitus (GDM) often do not receive additional interventions for their long-term metabolic risks.

Health care systems are increasingly confronted with the challenge of managing complex clinical processes. One proposed solution is a patient-centered management intervention called a care pathway that needs process mapping to support process improvement. Although the adoption and use of Business Process Modeling Notation (BPMN) for modeling patient health care trajectories has increased, evidence of the benefits of implementing it in health care organization management systems remains unclear.

Chronic diseases account for most global morbidity and mortality, increasing the need for effective long-term self-care support. Digital health interventions, such as mobile apps, telemonitoring, and connected devices, are increasingly used to promote self-care; yet, their overall effectiveness across chronic conditions remains unclear.

Digital health interventions show promise in supporting adults with type 2 diabetes mellitus (T2DM). Dario is a digital diabetes solution (DDS) that allows individuals with T2DM to automatically log blood glucose (BG) measurements and other variables to facilitate positive behavior changes.

Artificial intelligence (AI) has demonstrated strong potential in breast cancer diagnostics by improving accuracy, efficiency, and clinical workflow. However, adoption among physicians remains variable. Existing research often overlooks the contextual and experiential differences between clinicians who use AI and those who do not. A comprehensive understanding of barriers and facilitators, especially across user groups, is essential to inform equitable and effective AI implementation in real-world settings.

Hospital-at-home (HaH) care models are increasingly being adopted as a strategy to treat older adults with acute care needs and reduce strain on health care systems. Technological innovations, particularly digital communication platforms, have become essential in enabling care delivery beyond traditional hospital settings. Among these, asynchronous messaging tools have the potential to facilitate safe, timely, and coordinated interactions between health care providers, patients, and caregivers. Despite growing interest, little is known about how the content and relational dynamics of such exchanges influence care experiences in real-world HaH contexts.

The secondary use of health data is essential for advancing medical research and improving clinical practice. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) enables large-scale, multicenter studies but faces challenges related to consistency, completeness, and transparency during data mapping from original data sources.

Several artificial intelligence (AI) governance frameworks have emerged to help health systems (HS) address AI-related risks. However, most fail to capture the multidimensional and evolving nature of real-world governance.

Given the diversity of human characteristics and experiences, personalization in nudges, messages, choice presentations, interventions, and overall product design has been increasingly adopted in digital health to promote engagement. Past studies on moderators and personalization in digital health and mental health services generally focused on demographic and symptom variables, with generally inconsistent findings or null findings. Cognitive, motivational, and decisional psychological attributes are largely overlooked. Psychology often uses long self-report scales to measure various psychological attributes. Although they are useful in tapping into individuals’ psychological profiles, when applied in real-life, everyday settings to assess individual differences, people are most likely unwilling to complete them. With the pressing need to personalize digital health platforms to enhance uptake, retention, and engagement, ultrashort versions of these psychological scales may be considered to allow assessment of multiple attributes at the same time. Scale shortening can be achieved through regression analyses of each item, factor analyses, item response theory, ant colony optimization, and machine learning methods, with each method having advantages, disadvantages, and conditions required to make it suitable. To illustrate, we provided examples of regression analyses of each item and factor analyses, with potential implications for personalizing narrative versus research-based messages in digital mental health contexts. We present a 3-tiered decision framework for scale shortening method selection depending on goals and possible constraints, with guidelines on validation methods for ultrashort scales. Moving forward, more validation studies and field studies in digital health platforms are needed to evaluate the ecological validity, reliability, and generalizability of these methods, bearing in mind the limitations and conditions where such shortening methods may not work well. Researchers may compare the effectiveness and limitations of personalization using ultrashort scales with other commonly adopted personalization methods (eg, based on longer scales, behavioral data, and large language models). Ethical concerns need to be considered and mitigated carefully, respecting diverse preferences, informed choices, and the privacy of service users. Our viewpoint piece is primarily intended for digital mental health researchers and practitioners, but may also be informative for the fields of digital health and medicine as well as personalization (eg, personalized health care, personalized nudging, and message matching) more broadly, given the common goal of boosting uptake and engagement as well as improving service users’ experiences.

Medical real-world data (RWD) are often siloed across organizations, making them inaccessible for research. Unlocking these data could advance clinical research and patient care. The pan-European Data Nexus platform (DNP) links RWD, facilitating its use, for example by artificial intelligence (AI) tools, to support the generation of real-world evidence. In Europe, particularly Germany, the secondary use of health data is governed by stringent regulatory requirements, including informed consent.

Traditional in-person neuropsychological tests for Parkinson disease (PD) lack accessibility, scalability, and PD specificity. Mobility impairments hinder access to in-person assessments, and long waiting times for expert evaluation limit scalability. Common tools for cognitive screening, such as the Montreal Cognitive Assessment, are generic and not specific to PD.