JMIR Medical Informatics
Clinical informatics, decision support for health professionals, electronic health records, and ehealth infrastructures
JMIR Medical Informatics (JMI, ISSN 2291-9694; Impact Factor: 3.23) (Editor-in-chief: Christian Lovis MD MPH FACMI) is a PubMed/SCIE-indexed journal that focuses on clinical informatics, big data in health and health care, decision support for health professionals, electronic health records, eHealth infrastructures, and implementation. JMI has a focus on applied, translational research, with a broad readership including clinicians, CIOs, engineers, industry, and health informatics professionals.
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 (ready for deposit in PubMed Central/PubMed). The journal is indexed in PubMed, PubMed Central, DOAJ, SCOPUS, and SCIE (Clarivate). In 2022, JMI received a Journal Impact Factor™ of 3.23 (5-Year Journal Impact Factor: 3.56) (Source: Journal Citation Reports™ from Clarivate, 2022).
Adverse reactions to drugs attract significant concern in both clinical practice and public health monitoring. Multiple measures have been put into place to increase postmarketing surveillance of the adverse effects of drugs and to improve drug safety. These measures include implementing spontaneous reporting systems and developing automated natural language processing systems based on data from electronic health records and social media to collect evidence of adverse drug events that can be further investigated as possible adverse reactions.
Electronic health records (EHRs) are the electronic records of patient health information created during ≥1 encounter in any health care setting. The Health Information Technology Act of 2009 has been a major driver of the adoption and implementation of EHRs in the United States. Given that the adoption of EHRs is a complex and expensive investment, a return on this investment is expected.
Electronic health records (EHRs) were originally developed for clinical care and billing. As such, the data are not collected, organized, and curated in a fashion that is optimized for secondary use to support the Learning Health System. Population health registries provide tools to support quality improvement. These tools are generally integrated with the live EHR, are intended to use a minimum of computing resources, and may not be appropriate for some research projects. Researchers may require different electronic phenotypes and variable definitions from those typically used for population health, and these definitions may vary from study to study. Establishing a formal registry that is mapped to the Observation Medical Outcomes Partnership common data model provides an opportunity to add custom mappings and more easily share these with other institutions. Performing preprocessing tasks such as data cleaning, calculation of risk scores, time-to-event analysis, imputation, and transforming data into a format for statistical analyses will improve efficiency and make the data easier to use for investigators. Research registries that are maintained outside the EHR also have the luxury of using significant computational resources without jeopardizing clinical care data. This paper describes a virtual Diabetes Registry at Atrium Health Wake Forest Baptist and the plan for its continued development.
Cardiac magnetic resonance imaging (CMR) is a powerful diagnostic modality that provides detailed quantitative assessment of cardiac anatomy and function. Automated extraction of CMR measurements from clinical reports that are typically stored as unstructured text in electronic health record systems would facilitate their use in research. Existing machine learning approaches either rely on large quantities of expert annotation or require the development of engineered rules that are time-consuming and are specific to the setting in which they were developed.
Severe drug hypersensitivity reactions (DHRs) refer to allergic reactions caused by drugs and usually present with severe skin rashes and internal damage as the main symptoms. Reporting of severe DHRs in hospitals now solely occurs through spontaneous reporting systems (SRSs), which clinicians in charge operate. An automatic identification system scrutinizes clinical notes and reports potential severe DHR cases.
The adverse impact of COVID-19 on marginalized and under-resourced communities of color has highlighted the need for accurate, comprehensive race and ethnicity data. However, a significant technical challenge related to integrating race and ethnicity data in large, consolidated databases is the lack of consistency in how data about race and ethnicity are collected and structured by health care organizations.
Electronic health record (EHR) systems are becoming increasingly complicated, leading to concerns about rising physician burnout, particularly for primary care physicians (PCPs). Managing the most common cardiometabolic chronic conditions by PCPs during a limited clinical time with a patient is challenging.
Many factors influence patient satisfaction during an emergency department (ED) visit, but the perception of wait time plays a central role. A long wait time in the waiting room increases the risk of hospital-acquired infection, as well as the risk of a patient leaving before being seen by a physician, particularly those with a lower level of urgency who may have to wait for a longer time.