JMIR Medical Informatics
Clinical informatics, decision support for health professionals, electronic health records, and ehealth infrastructures.
JMIR Medical Informatics (JMI, ISSN 2291-9694) is a top-rated, tier A journal which focuses on clinical informatics, big data in health and health care, decision support for health professionals, electronic health records, ehealth infrastructures and implementation. It has a focus on applied, translational research, with a broad readership including clinicians, CIOs, engineers, industry and health informatics professionals.
Published by JMIR Publications, publisher of the Journal of Medical Internet Research (JMIR), the leading eHealth/mHealth journal (Impact Factor 2016: 5.175), JMIR Med Inform has a slightly different scope (emphasizing more on applications for clinicians and health professionals rather than consumers/citizens, which is the focus of JMIR), publishes even faster, and also allows papers which are more technical or more formative than what would be published in the Journal of Medical Internet Research.
JMIR Medical Informatics journal features a rapid and thorough peer-review process, professional copyediting, professional production of PDF, XHTML, and XML proofs (ready for deposit in PubMed Central/PubMed). The site is optimized for mobile and iPad use.
JMIR Medical Informatics adheres to the same quality standards as JMIR and all articles published here are also cross-listed in the Table of Contents of JMIR, the worlds' leading medical journal in health sciences / health services research and health informatics (http://www.jmir.org/issue/current).
Oct 17, 2017
Oct 16, 2017
Oct 12, 2017
Oct 11, 2017
Oct 6, 2017
Oct 2, 2017
Sep 29, 2017
Sep 28, 2017
Sep 26, 2017
Sep 20, 2017
Sep 14, 2017
Sep 13, 2017
Citing this Article
Right click to copy or hit: ctrl+c (cmd+c on mac)
Latest Submissions Open for Peer-Review:View All Open Peer Review Articles
Date Submitted: Oct 11, 2017
Open Peer Review Period: Oct 13, 2017 - Dec 8, 2017
The increasing use of social media and mobile health applications has generated new opportunities for health care consumers to share information about their health and wellbeing. Information shared t...
The increasing use of social media and mobile health applications has generated new opportunities for health care consumers to share information about their health and wellbeing. Information shared through social media contains not only medical information but also valuable information about how the survivors manage disease and recovery in the context of daily life. The aim of the present study is to explore methods for analyzing forum posts of breast cancer patients to discover the “hidden” aspects of disease management and recovery. An open source software MALLET was used to reduce the postings to categories with similar content. Qualitative analysis of the categorization and statistical analyses confirmed clinical significance of the results.
Understanding barriers to physicians adopting clinical decision support systems: A Systematic Review
Date Submitted: Sep 6, 2017
Open Peer Review Period: Sep 8, 2017 - Nov 3, 2017
Background: The purpose of this paper was to conduct an analytical review and task analysis of CDSS re-search, and to develop a new framework for CDSS design in order to achieve user acceptance. Objec...
Background: The purpose of this paper was to conduct an analytical review and task analysis of CDSS re-search, and to develop a new framework for CDSS design in order to achieve user acceptance. Objective: This paper tackles a critical issue modern healthcare systems namely, the adoption of Health Informatics Technologies (HIT). The paper provides a comprehensive literature review of the current status quo of Clinical Decision Support Systems adoption, the barriers to adoption from users stand point, and proposes novel solutions to current gaps in order to the enhancement of healthcare delivery and execution. Methods: A literature review of CDSS papers was conducted with a focus on user acceptance. In order to gain a greater understanding of the problems associated with CDSS, we conducted a task analysis to identify and describe the goals, user input, system output, knowledge requirements, and constraints from two different perspectives. Results: Based on the literature review, findings are characterized by whether user acceptance was favorable and unfavorable for clinical guidelines, reminders, and diagnostic CDSS. We propose two models: 1) the User Acceptance and System Adaptation Design (UASAD) model which includes optimizing CDSS design based on user needs/expectations and 2) the Input-Process-Output-Engage (IPOE) model which reveals to users the processes the govern CDSS outputs. Conclusions: CDSS have the capacity to decrease medical errors and improve patient outcomes. However, current CDSS designs do not consistently show such improvements. We hypothesized that the incorporation of the proposed models will improve user acceptance to support beneficial impacts of CDSS adoption. Ultimately, if a user does not accept technology this not only poses a threat to the use of the technology but can also pose a threat to the health and wellbeing of patients.