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JMIR Medical Informatics

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 5.0 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 MEDLINEPubMedPubMed CentralDOAJ, Scopus, and the Science Citation Index Expanded (SCIE)

JMIR Medical Informatics received a 2025 Impact Factor of 5.0, ranking Q2 in Medical Informatics (17/54).

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

Two female doctors working on computers in a modern clinic
Decision Support for Health Professionals

Primary care is becoming increasingly complex, with primary care physicians (PCPs) facing rising workloads driven by workforce shortages, growing administrative demands, and expanding clinical responsibilities. Recent advances in large language models (LLMs) offer new opportunities to support PCPs across clinical, administrative, and communication-related tasks within their workflows. Understanding how these technologies are perceived and used in primary care practice is, therefore, critical to inform their safe, effective, and human-centered implementation.

Doctor in white coat using a tablet with medical data, stethoscope visible.
Advanced Data Analytics in eHealth

Cardiovascular diseases (CVDs) and type 2 diabetes (DM2) are influenced not only by biomedical risk factors but also by social determinants of health (SDOH). While the inclusion of SDOH in predictive models is increasingly advocated, few studies have quantified their specific contribution in a high-risk clinical cohort using robust statistical and machine-learning approaches.

Man lying in MRI scanner for medical imaging and diagnosis
Imaging Informatics

Supraspinatus tendon pathologies are common causes of shoulder pain. Magnetic resonance imaging (MRI) is the reference imaging method but requires expert interpretation. Automated classification may improve diagnostic consistency and support musculoskeletal imaging workflows.

Doctors review Orphanet rare disease classifications on computer and tablet.
Ontologies, Classifications, and Coding

Although individually uncommon, rare diseases (RDs) collectively affect an estimated 329-624 million people worldwide. There are over 6500 known RDs, 85% of which affect fewer than 1 person per million. Consequently, the critical amount of data necessary to improve knowledge, care, and treatment can only be achieved through cumulative data collection across countries. However, RDs remain underrepresented in medical terminologies and classification systems, hindering data sharing, interoperability, and public health monitoring.

Tablet displaying healthcare app with patient monitoring, vital signs, and doctor icons.
Ambient AI Scribes and AI-Driven Documentation Technologies

The increasing documentation burden on physicians is a significant contributor to burnout and decreases in care quality. Artificial intelligence (AI) has been proposed as a solution to reduce documentation burden in clinical care, but there are very limited data on its use in the inpatient and intensive care unit (ICU) environments.

Digital data flow from files to laptop, showing analytics and user interaction.
AI Language Models in Health Care

Qualitative thematic analysis is widely used in health research to examine patient experiences and inform the refinement of digital health interventions, but it is time- and labor-intensive. Large language models (LLMs) may help accelerate this process, yet their performance may depend not only on the model itself but also on how the analytic workflow is structured. Current evidence remains limited on how different LLMs perform across multistage thematic analysis workflows and across multiple health-related qualitative datasets.

Doctors reviewing patient data on tablet and clipboard
Advanced Data Analytics in eHealth

Irregularly sampled data, as a common data structure in the medical field, is frequently observed in emergency clinical datasets. It poses problems such as unequal sampling time intervals and frequencies, making it difficult to align the data without losing information for input into models. Meanwhile, due to information loss in the dataset, it is also difficult for the model to effectively analyze the overall data variation and predict the complex and dynamic conditions of emergency patients in the future.

Doctor in mask reviews ultrasound scan of breast on monitor and tablet.
Computer-Aided Diagnosis

Computer-aided diagnostic systems such as S-Detect (Samsung Medison) are increasingly integrated into breast ultrasound workflows. Notwithstanding extensive past evaluation of S-Detect’s diagnostic accuracy, its intrasystem repeatability at the software level with identical static images, a fundamental prerequisite for clinical reliability, has not been systematically investigated.

Man using laptop with a diagram on screen about design, research, style, and inspiration
Natural Language Processing

Concept mapping (CM) is a widely used mixed method research approach for structuring and visualizing complex ideas across various fields, such as the health sciences. A critical bottleneck in the CM process is the idea synthesis phase, which remains labor-intensive, subjective, and consequently challenging to scale for large datasets.

Person in green scrubs touching tablet screen with finger
Ambient AI Scribes and AI-Driven Documentation Technologies

In intensive care unit (ICU) settings, structured team-based communication, such as multidisciplinary rounds, handoffs, and goals-of-care discussions, is foundational to high-quality care. However, accurately documenting these complex discussions in the medical record remains a challenge due to time pressures, documentation burdens, and competing clinical demands. Ambient artificial intelligence (AI) scribes, which passively transcribe and summarize spoken interactions, offer a potential solution to assist ICU clinicians with documentation. Yet, little is known about how ICU clinicians perceive the integration of these tools into their high-stakes, collaborative workflows.

Preprints Open for Peer Review

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