JMIR Medical Informatics

Alzheimer disease and related dementias are increasing worldwide, with early detection during the mild cognitive impairment (MCI) stage critical for timely intervention. Driving behavior, which reflects everyday cognitive functioning, has emerged as a promising, noninvasive, and inexpensive digital biomarker when paired with machine learning. However, prior research has often relied on controlled settings, high-level features, or assumptions that fail to capture the sporadic nature of MCI, leaving a gap in modeling naturalistic driving data for robust early detection.

The implementation of Clinical Data Interchange Standards Consortium (CDISC) standards is essential for accelerating clinical research and is mandated for new drug applications in Japan. However, the current status of their implementation and associated challenges in Japanese academic medical centers has not been comprehensively investigated.

Advancements in health technology and the adoption of electronic systems in hospital pharmacies have transformed pharmacy practice and service delivery, with patients and health care providers reporting perceived benefits related to patient care and safety. Therefore, it is of paramount importance to seek patients’ opinions based on their experiences in receiving outpatient pharmacy services through automated pharmacy systems.

The increasing reliance on online surveys for collecting patient-reported feedback for health care research has led to growing concerns over fraudulent responses generated by bots. These automated responses threaten data integrity by fabricating survey results, distorting statistical analyses, and potentially misguiding policy decisions. Addressing this issue is critical for maintaining the validity of research findings that inform health care practice and policy.

In large-scale clinical data analysis, CSV and traditional relational database management system–based approaches are widely used but impose substantial storage and processing constraints that delay research preparation and hinder multicenter collaboration. Although column-oriented storage formats such as Apache Parquet have gained attention in data science, systematic end-to-end evaluations in clinical environments remain limited, particularly regarding efficiency and scalability.

Medical discharge letters are critical for continuity of care but often lack clarity and personalization, making it difficult for health care providers to retrieve essential information. While large language models (LLMs) offer potential for automating summary generation, their effectiveness depends heavily on the quality and contextual relevance of the prompts used.

Accurate segmentation of cartilage from magnetic resonance imaging (MRI) is crucial for the diagnosis and surgical planning of knee osteoarthritis. However, manual segmentation is time-consuming, and conventional computed tomography–based surgical systems are limited by their inability to visualize cartilage.

Nursing care systems face significant challenges due to demographic changes, a workforce shortage, and rising demand for care services. Digital assistive technologies offer potential to address these challenges, but systematic and standardized nursing data are essential to evaluate both innovations and broader care processes. The Nursing Minimum Data Set (NMDS) provides a foundational framework for capturing structured information on nursing care, yet there is no international consensus on its core content, development, and practical use.

Scars and keloids impose significant physical and psychological burdens on patients, often leading to functional limitations, cosmetic concerns, and mental health issues such as anxiety or depression. Patients increasingly turn to online platforms for information; however, existing web-based resources on scars and keloids are frequently unreliable, fragmented, or difficult to understand. Large language models such as GPT-4 show promise for delivering medical information, but their accuracy, readability, and potential to generate hallucinated content require validation for patient education applications.

Adverse drug reactions pose a serious threat to health care, leading to patient harm and substantial economic burden. Dashboards for drug safety surveillance are a valuable tool to tackle it.

Electronic health records are essential for advancing research aimed at improving clinical outcomes. However, stringent data protection and privacy concerns severely limit the accessibility and use of real clinical data, particularly within Child and Adolescent Mental Health Services (CAMHS) involving vulnerable young individuals. This challenge can be effectively addressed through synthetic data generation, which safeguards individual privacy while facilitating comprehensive analyses of clinical information.

Individual-level behavioral interventions are designed to improve health behaviors and manage noncommunicable diseases. Neighborhood geo-referenced contexts (NGRCs) significantly impact the success of these interventions. Integrating NGRC data into health information systems (HISs), including electronic medical records (EMRs), electronic health records (EHRs), and personal health records (PHRs), can enhance personalized NGRC-focused behavioral interventions and improve health outcomes. Despite the potential benefits, there is a notable gap in the literature about NGRC-focused behavioral interventions using HISs.