JAMIA is AMIA's premier peer-reviewed journal for biomedical and health informatics. Covering the full spectrum of activities in the field, JAMIA includes informatics articles in the areas of clinical care, clinical research, translational science, implementation science, imaging, education, consumer health, public health, and policy. JAMIA's articles describe innovative informatics research and systems that help to advance biomedical science and promote health. Case reports, perspectives, and reviews also help readers stay connected with the most important informatics developments in implementation, policy, and education.
Suzanne Bakken is the Editor-in-Chief and leads a team of informatics leaders serving as the JAMIA Editorial Board.
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JAMIA is indexed in Index Medicus, MEDLINE, EMBASE/Excerpta Medica, CINAHL, Science Citation Index, Social Sciences Citation Index (SSCI), SciSearch, Social SciSearch, Research Alert, Current Contents/Social & Behavioral Sciences, and Current Contents/Clinical Medicine.
Recent JAMIA Articles
Please note: To access the full content of the articles listed below, AMIA members must access JAMIA via the Journal Access Center.
Improved Intrahospital Transport Time via Proximity-Based Staff Assignments.
May 9, 2025
Intrahospital patient transport is pivotal in enabling hospital operations and facilitating safe and efficient patient movement. However, transport delays are common in hospitals, signaling a need for improvement. This study develops, implements, and evaluates a proximity-based transporter-to-request assignment system aimed at improving transport service system efficiency.
The emergence of large language models as tools in literature reviews: a large language model-assisted systematic review.
May 7, 2025
This study aims to summarize the usage of large language models (LLMs) in the process of creating a scientific review by looking at the methodological papers that describe the use of LLMs in review automation and the review papers that mention they were made with the support of LLMs.
A comparative analysis of privacy-preserving large language models for automated echocardiography report analysis.
May 7, 2025
Automated data extraction from echocardiography reports could facilitate large-scale registry creation and clinical surveillance of valvular heart diseases (VHD). We evaluated the performance of open-source large language models (LLMs) guided by prompt instructions and chain of thought (CoT) for this task.
CDEMapper: enhancing National Institutes of Health common data element use with large language models.
May 7, 2025
Common Data Elements (CDEs) standardize data collection and sharing across studies, enhancing data interoperability and improving research reproducibility. However, implementing CDEs presents challenges due to the broad range and variety of data elements. This study aims to develop a CDE mapping tool to bridge the gap between local data elements […]