JMIR Medical Informatics

Triage errors in emergency departments (EDs), including undertriage and overtriage, pose significant risks to patient safety and resource allocation. With increasing patient volumes and staffing challenges, artificial intelligence (AI) integration into triage protocols has gained attention as a potential solution.

Primary care in Thailand often uses mixed Thai-English free-text documentation for diagnoses and clinical problems, limiting standardization, interoperability, and secondary data use. Clinical terminologies like Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT), a comprehensive reference terminology, can bridge this gap through the use of structured clinical data. Developing and mapping a local user interface terminology (UIT) is one of the key strategies for implementing SNOMED CT in real-world clinical settings.

Effective secondary use of healthcare data is hindered by fragmentation and a lack of semantic interoperability due to heterogeneous local terminologies. Standardizing clinical terms using SNOMED CT (Systematized Nomenclature of Medicine Clinical Terms) is essential but remains a manual, labor-intensive, and inconsistent process, especially across multiple institutions. Automated, scalable solutions are needed to support reliable mapping and new concept authoring for large-scale research.

Technology has improved patient care in hospitals, enhancing the overall patient experience. However, digitalization raises questions on effectively integrating technological strategies to ensure assertive communication of information during emergency department (ED) journeys. Keeping patients well-informed boosts their service perception and satisfaction, a factor often neglected by institutions in EDs.

Cancer risk prediction models are vital for precision prevention, enabling individualized assessment of cancer susceptibility based on genetic, clinical, environmental, and lifestyle factors. However, the practical use of these models is hindered by fragmented resources, heterogeneous reporting, and the absence of transparent, structured systems for systematic discovery and comparison.

Clinical decision-making is a critical process where physicians balance risks and benefits. Clinical Decision Support Systems (CDSSs) are increasingly used to help in this process. The regulatory landscape for CDSSs is evolving significantly, with the new European Medical Device Regulation (MDR) now requiring, CE certification for certain CDSSs. This shift poses challenges for health care providers to develop CDSSs in an effective and useful manner while adhering to regulations. This viewpoint comments on diverse challenges and provides solutions to develop a reliable, well integrated and practical tool for clinical use. Using three tools (the Early Onset Sepsis Calculator, Feverkidstool, and Neonatal Procalcitonin Intervention Study algorithm) as examples, we explore the development of CDSSs across four core characteristics: scientific basis, technical aspects, safety, and sustainability. These characteristics recur across the main development processes; scientific development, regulatory assessment, and implementation in routine practice. Successful integration of CDSSs into clinical practice requires a comprehensive understanding of the interconnections between these processes. For example, decisions on algorithm validation and platform selection in the scientific process influence choices for technical safety during the regulatory process. Developers should consider both regulation requirements and clinical needs, to create CDSSs that are not only compliant but also adaptable to the rapidly changing healthcare landscape. We outline a developer’s checklist, for practical guidance, but also appeal for structural support, including national protocols and dedicated hospital roles, to help developers implement CDSSs successfully.

The expanding use of multisource real-world electronic health record (EHR) and claims data offers major opportunities for research, drug discovery, and clinical decision support. While standards such as Logical Observation Identifiers Names and Codes (LOINC) can ensure semantic interoperability for laboratory observations, clinical documents, and other clinical terms, properly assigning these concepts remains a challenge. Studies show that 6% to 19% of laboratory tests cannot be accurately mapped to LOINC. Existing systems try to address this challenge but often depend on source data strings and other input features that may be absent, null, or incorrect. This underscores the need for a scalable approach to correct LOINC code assignments, standardize units, and ensure data integrity across multisource laboratory data.

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.