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.
Call for Papers
AMIA Open Special Issue: Health AI Evaluation Showcase
Submission Deadline: rolling basis until Jan. 31, 2025
Access
Access to JAMIA is a member benefit and only available to current AMIA members (Regular, student, retired and lifetime).
Access the JAMIA Journal (Member-only)
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.
Increasing adherence and collecting symptom-specific biometric signals in remote monitoring of heart failure patients: a randomized controlled trial.
January 1, 2025
Mobile health (mHealth) regimens can improve health through the continuous monitoring of biometric parameters paired with appropriate interventions. However, adherence to monitoring tends to decay over time. Our randomized controlled trial sought to determine: (1) if a mobile app with gamification and financial incentives significantly increases adherence to mHealth monitoring […]
Just another tool in their repertoire: uncovering insights into public and patient perspectives on clinicians' use of machine learning in perioperative care.
January 1, 2025
Successful implementation of machine learning-augmented clinical decision support systems (ML-CDSS) in perioperative care requires the prioritization of patient-centric approaches to ensure alignment with societal expectations. We assessed general public and surgical patient attitudes and perspectives on ML-CDSS use in perioperative care.