JMIR Medical Informatics

Clinical informatics, decision support for health professionals, electronic health records, and eHealth infrastructures.

Editor-in-Chief:

Arriel Benis, PhD, FIAHSI, Associate Professor and Head of the Department of Digital Medical Technologies, Holon Institute of Technology (HIT), Israel


Impact Factor 3.8 CiteScore 7.7

JMIR Medical Informatics (JMI, ISSN 2291-9694, Journal Impact Factor 3.8) (Editor-in-chief: Arriel Benis, PhD, FIAHSI) is an open-access journal that focuses on the challenges and impacts of clinical informatics, digitalization of care processes, 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 (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 Journal Impact Factor of 3.8 (Source:Journal Citation Reports 2025 from Clarivate).

JMIR Medical Informatics received a Scopus CiteScore of 7.7 (2024), placing it in the 79th percentile (#32 of 153) as a Q1 journal in the field of Health Informatics.

Recent Articles

Article Thumbnail
Natural Language Processing

Accurate staging of esophageal cancer is crucial for determining prognosis and guiding treatment strategies, but manual interpretation of radiology reports by clinicians is prone to variability and limited accuracy, resulting in reduced staging accuracy. Recent advances in large language models (LLMs) have shown promise in medical applications, but their utility in esophageal cancer staging remains underexplored.

|
Article Thumbnail
Machine Learning

The burden of paralytic ileus (PI) in the intensive care unit (ICU) remains high, and the Charlson Comorbidity Index (CCI) is strongly associated with the prognosis of several acute and chronic diseases. However, evidence specifically evaluating the prognostic value of CCI in ICU patients with PI remains limited.

|
Article Thumbnail
Natural Language Processing

Clinical natural language processing (cNLP) techniques are commonly developed and used to extract information from clinical notes to facilitate clinical decision making and research. However, they are less established for rare diseases such as lymphoid malignancies due to the lack of annotated data as well as the heterogeneity and complexity of how clinical information is documented. In addition, there is increasing evidence that cNLP techniques may be prone to biases embedded in clinical documentation or model development. These biases can result in disparities in performance when extracting clinical information or predicting patient outcomes.

|
Article Thumbnail
Reviews in Medical Informatics

Integrating telehealth into established care processes can be challenging. With the integration of telehealth into routine health care practices, there is a growing need to evaluate telehealth outcomes to understand its impact on health care delivery. However, existing literature on telehealth outcomes to support evaluation remains limited.

|
Article Thumbnail
AI Language Models in Health Care

Real World Data (RWD)-based feasibility assessments enhance clinical trial design, but automating eligibility criteria conversion to database queries is hindered by challenges due to difficulties in ensuring high accuracy and generating clear, usable outputs.

|
Article Thumbnail
Theme Issue: Medical Informatics and COVID-19

Amidst the COVID-19 pandemic, the proliferation of misinformation on social media, termed the "infodemic," has complicated global health responses.

|
Article Thumbnail
Tools, Programs and Algorithms

Chronic obstructive pulmonary disease (COPD) remains a leading global health burden. In primary care, the inconsistent availability of spirometry and symptom scores limits the detection of patients with poor disease control. There is a pressing need for scalable, data-driven tools that leverage routinely collected clinical information to support timely, equitable, and guideline-concordant interventions.

|
Article Thumbnail
Decision Support for Health Professionals

Offline reinforcement learning (RL) has been increasingly applied to clinical decision-making problems. However, due to the lack of a standardized pipeline, prior work often relied on strategies that may lead to over-fitted policies and inaccurate evaluations.

|
Article Thumbnail
Machine Learning

Machine learning (ML) has shown great potential in recognizing complex disease patterns and supporting clinical decision-making. Diabetic foot ulcers (DFUs) represent a significant multifactorial medical problem with high incidence and severe outcomes, providing an ideal example for a comprehensive framework that encompasses all essential steps for implementing ML in a clinically relevant fashion.

|
Article Thumbnail
Reviews in Medical Informatics

Digital-based interventions have the potential to support the prevention of mother-to-child HIV transmission (PMTCT) services. Nevertheless, reviews to explore mothers living with HIV’s experiences and perspectives toward digital-based interventions remain limited.

|
Article Thumbnail
Clinical Communication, Electronic Consultation and Telehealth

Telemonitoring can be implemented using either centralized or distributed organizational models. However, few published studies explore which conditions make one model preferable over the other, or how to choose between these two.

|
Article Thumbnail
Theme Issue 2024: Health Natural Language Processing and Applications with Large Language Models

Early diagnosis and intervention in glottic carcinoma can significantly improve long-term prognosis. However, the accurate diagnosis of early glottic carcinoma is challenging due to its morphological similarity to vocal cord dysplasia, with the difficulty further exacerbated in medically underserved areas.

|

Preprints Open for Peer-Review

|

Open Peer Review Period:

-

|

Open Peer Review Period:

-

We are working in partnership with