JMI - JMIR Medical Informatics (original) (raw)
Clinical informatics, decision support for health professionals, electronic health records, and eHealth infrastructures.
Editor-in-Chief:
**Arriel Benis, PhD, FIAHSI, SMIEEE, Associate Professor and Head of the Department of Digital Medical Technologies, Holon Institute of Technology (HIT), Israel
Impact Factor 3.8 More information about Impact Factor CiteScore 7.5 More information about CiteScore
JMIR Medical Informatics is an open-access journal that focuses on the challenges and impacts of clinical informatics, digitalization of care processes, and clinical and health data pipelines from acquisition to reuse, including semantics, natural language processing, natural interactions, meaningful analytics and decision support, electronic health records, infrastructures, implementation, and evaluation. The journal prioritizes research that bridges theoretical frameworks with actionable insights, ensuring that informatics solutions demonstrate measurable clinical or population impact (see Focus and Scope).
JMIR Medical Informatics adheres to rigorous quality standards, involving a rapid and thorough peer-review process, professional copyediting, and professional production of PDF, XHTML, and XML proofs.
The journal is indexed in MEDLINE, PubMed, PubMed Central, DOAJ, Scopus, and the Science Citation Index Expanded (SCIE).
JMIR Medical Informatics received a Journal Impact Factor of 3.8 (Source: Journal Citation Reports 2025 from Clarivate).
JMIR Medical Informatics received a Scopus CiteScore of 7.5 (2025), placing it in the 78th percentile (37/168) as a first quartile (Q1) journal in the field of Health Informatics.
Recent Articles
Viewpoints on and Experiences with Digital Technologies in Health
Secondary use of health data is essential for advancing medical research, innovation, and public health policy across Europe. Traditional static or broad consent models are increasingly inadequate in complex, multistakeholder digital ecosystems. Dynamic consent, which enables granular, interactive, and ongoing management of individual preferences, including revocation, has emerged as a patient-centered alternative. This integrative review examines the legal feasibility and practical challenges of implementing dynamic consent for secondary health data use under the General Data Protection Regulation (GDPR) and the European Health Data Space (EHDS) Regulation. Drawing on doctrinal legal analysis, European policy documents, national derogations, and technical standards including Health Level Seven Fast Healthcare Interoperability Resources, electronic Identification, Authentication and Trust Services 2.0, European Digital Identity Wallet, and distributed ledger approaches, the study synthesizes legal, governance, and informatics perspectives. Findings indicate that while the GDPR establishes parameters supportive of specific, informed, and revocable consent, significant barriers persist due to national fragmentation, divergent lawful bases for processing, and limited cross-border revocation mechanisms. The EHDS, with provisions phasing in from 2029, shifts governance toward institutional authorization via Health Data Access Bodies and secure processing environments, reducing reliance on individual consent for many large-scale uses. Technical prerequisites, machine-readable consent artifacts, high-assurance digital identity, and policy-based enforcement remain unevenly developed. Nevertheless, integration with data altruism mechanisms under the Data Governance Act and emerging interoperability tools offers promising pathways. A 3-stage operational architecture (consent administration, decision, and enforcement) is proposed to embed dynamic consent within the hybrid EHDS-GDPR framework. However, challenges including blockchain immutability conflicts with the right to erasure, revocation propagation across systems, implementation costs, consent fatigue, and digital divides must be addressed. Dynamic consent cannot serve as a universal solution but can meaningfully enhance transparency and trust when deployed contextually alongside institutional safeguards. Coordinated EU-level harmonization, standardization, and inclusive design will be essential for its successful operationalization.
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Norwegian Child and Adolescent Mental Health Services (CAMHS) use the World Health Organization’s (WHO) multiaxial diagnostic system based on the International Classification of Diseases, Tenth Revision (ICD-10); however, analysis of prescribing patterns among axes I-III is underexplored in electronic health records (EHRs) with intertwined patient, episode of care, and contact information.
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Home care services are under increasing pressure due to population aging, rising chronic disease prevalence, and workforce shortages. Digital health technologies, particularly home telemonitoring systems, are seen as promising tools to detect early clinical deterioration, reduce hospital use, and support continuity of care. However, real-world evidence on the implementation of such technologies in public home care systems remains limited. In 2023‐2024, the Quebec Ministry of Health and Social Services funded a pilot to implement a multidevice telemonitoring intervention for older adults with heart failure across three integrated health and social services centers (CISSS). The initiative aimed to assess feasibility, acceptability, and the organizational conditions shaping implementation.
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Laboratory testing is a cornerstone of diagnostic decision-making in emergency departments (EDs), yet its overuse contributes substantially to unnecessary health care costs and inefficiencies. Predictive approaches that leverage electronic health record data may help optimize and guide more appropriate test use.
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Existing work to understand adults’ health care experiences has focused on the analysis of patient feedback provided as written responses to after-visit surveys or social media discourse. Often, such written feedback has been studied using natural language processing techniques, such as topic detection and sentiment analysis, to provide coarse-grained insights. Storytelling is a powerful form of communication and may provide insights into factors contributing to gaps in health care outcomes and avenues for improvement. In addition, studying health care experiences using natural language processing techniques has been limited to patients. The experiences of stakeholders, such as caregivers and health care providers, remain underexplored.
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Low peak oxygen consumption (V̇O) is associated with higher cardiovascular and all-cause mortality, while improvements in peak V̇O reduce this risk. Although early detection allows timely intervention, practical screening tools remain lacking. As electrocardiograms (ECGs) reflect both cardiac and age-related changes, they may offer a viable screening approach.
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Parkinson disease frequently manifests early vocal impairment, motivating the development of noninvasive and scalable digital screening tools.
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Reviews in Medical Informatics
Intraoperative bleeding is a critical event that impacts surgical safety and patient outcomes. Machine learning (ML) has demonstrated potential in prediction tasks, yet its methodological rigor and clinical translation face challenges.
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Deep learning, particularly encoder-only transformer architectures, has demonstrated excellent performance in biomedical literature classification, facilitating evidence-based medicine, and knowledge synthesis. However, the opacity of these models’ decision-making processes limits their clinical interpretability, trustworthiness, and widespread adoption. Traditional explainable artificial intelligence methods, such as Shapley Additive Explanations (SHAP) and integrated gradients (IG), address this issue but often incur substantial computational overhead for text classification. Generative large language models may offer a novel approach to generating interpretable, context-aware explanations as autonomous agents.
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Non–small-cell lung cancer (NSCLC) is one of the most common cancers and a leading cause of cancer-related mortality, making prognostic prediction clinically essential. Machine learning models are increasingly used to assess prognosis; however, developing systems that combine high discrimination with clear, clinically interpretable reasoning remains challenging.
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Standards and Interoperability
National Medical Products Administration of China has actively encouraged organizations to adopt the Clinical Data Interchange Standards Consortium (CDISC) for clinical data submission since 2020.
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Preprints Open for Peer Review
Open Peer Review Period:
June 11, 2026 - August 06, 2026
Open Peer Review Period:
June 05, 2026 - July 31, 2026
Open Peer Review Period:
June 05, 2026 - July 31, 2026
Open Peer Review Period:
June 05, 2026 - July 31, 2026
Open Peer Review Period:
June 05, 2026 - July 31, 2026
Open Peer Review Period:
June 05, 2026 - July 31, 2026
Open Peer Review Period:
June 02, 2026 - July 28, 2026
Open Peer Review Period:
May 21, 2026 - July 16, 2026
Open Peer Review Period:
May 18, 2026 - July 13, 2026
Open Peer Review Period:
May 15, 2026 - July 10, 2026
Open Peer Review Period:
May 15, 2026 - July 10, 2026
Open Peer Review Period:
April 28, 2026 - June 23, 2026