JAMIA Open is a peer-reviewed, online-only, and Gold Open Access journal, JAMIA Open provides a global forum for the publication of novel research and insights in the major areas of informatics for biomedicine and health (e.g., translational bioinformatics, clinical research informatics, clinical informatics, public health informatics, and consumer health informatics), as well as related areas such as data science, qualitative research, and implementation science.
JAMIA Open articles, which include application notes, database notes, and patient/community perspectives, alongside original research, reflect the broad diversity of the field of informatics community, focusing on the intersection of informatics, health, communication, and technology, and how that intersection can support patient care through research, practice, and education. JAMIA Open authors are encouraged to make data and source code accessible through publicly accessible repositories that can be cited using digital object identifiers. Accepted manuscripts are required to have a patient/community facing abstract that highlights key findings. Author guidelines.
Neil Sarkar, PhD, MLIS, FACMI, is the Editor-in-Chief and leads a team of informatics leaders serving as the JAMIA Open Editorial Board and Associate Editors.
Access
JAMIA Open is an open access journal and does not require a login or AMIA membership to read articles.
Recent JAMIA Open Articles
Workflows to automate covariate-adaptive randomization in REDCap via data entry triggers.
October 1, 2025
Covariate-adaptive randomization algorithms (CARAs) can reduce covariate imbalance in randomized controlled trials (RCTs), but a lack of integration into Research Electronic Data Capture (REDCap) has limited their use. We developed a software pipeline to seamlessly integrate CARAs into REDCap as part of the all2GETHER study, a 2-armed RCT concerning HIV […]
Deploying machine learning models in clinical settings: a real-world feasibility analysis for a model identifying adult-onset type 1 diabetes initially classified as type 2.
October 1, 2025
This study evaluates the performance and deployment feasibility of a machine learning (ML) model to identify adult-onset type 1 diabetes (T1D) initially coded as type 2 on electronic medical records (EMRs) from a health information exchange (HIE). To our knowledge, this is the first evaluation of such a model on […]
Automated phenotyping of congenital heart disease for dynamic patient aggregation and outcome reporting.
October 1, 2025
Accurate characterization of patients with congenital heart disease is fundamental to research, outcomes reporting, quality improvement, and clinical decision-making. Here we present an approach to computing the anatomy of patients with congenital heart disease based on the whole of their diagnostic and surgical codes.
Perceptions of and barriers to health information exchange use among emergency medicine and inpatient internal medicine clinicians in the Atlanta, Georgia metropolitan region.
October 1, 2025
Health information exchanges (HIE), tools that electronically share clinical data across healthcare organizations, provide the opportunity to improve patient care. While widely available, HIE is utilized in only 2%-10% of patient encounters. Few studies have explored current barriers to use. The goal of this study was to evaluate current clinician […]