JMIR Medical Informatics

Clinical informatics, decision support for health professionals, electronic health records, and eHealth infrastructures

Editor-in-Chief:

Christian Lovis, MD, MPH, FACMI, Division of Medical Information Sciences, University Hospitals of Geneva (HUG), University of Geneva (UNIGE), Switzerland


Impact Factor 3.23

JMIR Medical Informatics (JMI, ISSN 2291-9694; Impact Factor: 3.23) (Editor-in-chief: Christian Lovis, MD, MPH, FACMI) is an open-access PubMed/SCIE-indexed journal that focuses on the challenges and impacts of clinical informatics, digitalization of care processes, clinical and health data pipelines from acquisition to reuse, including: semantics, natural language processing, natural interactions, meaningful analytics and decision support, electronic health records, infrastructures, implementation, and evaluation (see Focus and Scope).

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. 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).

Recent Articles

Article Thumbnail
Standards and Interoperability

Increasing digitalization in the medical domain gives rise to large amounts of health care data, which has the potential to expand clinical knowledge and transform patient care if leveraged through artificial intelligence (AI). Yet, big data and AI oftentimes cannot unlock their full potential at scale, owing to nonstandardized data formats, lack of technical and semantic data interoperability, and limited cooperation between stakeholders in the health care system. Despite the existence of standardized data formats for the medical domain, such as Fast Healthcare Interoperability Resources (FHIR), their prevalence and usability for AI remain limited.

|
Article Thumbnail
Computerized Provider Order Entry (CPOE)

Clinical practice guidelines (CPGs) and associated order sets can help standardize patient care and lead to higher-value patient care. However, difficult access and poor usability of these order sets can result in lower use rates and reduce the CPGs’ impact on clinical outcomes. At our institution, we identified multiple CPGs for general pediatrics admissions where the appropriate order set was used in <50% of eligible encounters, leading to decreased adoption of CPG recommendations.

|
Article Thumbnail
Clinical Informatics in Low-Resource Settings and the Developing World

Personal health records (PHRs) are consumer-centric tools designed to facilitate the tracking, management, and sharing of personal health information. PHR research has mainly been conducted in high-income countries rather than in low- and middle-income countries. Moreover, previous studies that proposed PHR design in low- and middle-income countries did not describe integration with other systems, or there was no stakeholder involvement in exploring PHR requirements.

|
Article Thumbnail
Adoption and Change Management of eHealth Systems

People with long-term conditions frequently transition between care settings that require information about a patient’s medicines to be transferred or translated between systems. This process is currently error prone and associated with unintentional changes to medications and miscommunication, which can lead to serious patient consequences. One study estimated that approximately 250,000 serious medication errors occur in England when a patient transitions from hospital to home. Digital tools can equip health care professionals with the right information at the right time and place to support practice.

|
Article Thumbnail
Secondary Use of Clinical Data for Research and Surveillance

To discover new knowledge from data, they must be correct and in a consistent format. OntoCR, a clinical repository developed at Hospital Clínic de Barcelona, uses ontologies to represent clinical knowledge and map locally defined variables to health information standards and common data models.

|
Article Thumbnail
Reviews

Sensors are increasingly used in health interventions to unobtrusively and continuously capture participants’ physical activity in free-living conditions. The rich granularity of sensor data offers great potential for analyzing patterns and changes in physical activity behaviors. The use of specialized machine learning and data mining techniques to detect, extract, and analyze these patterns has increased, helping to better understand how participants’ physical activity evolves.

|
Article Thumbnail
Implementation Report

Clinical decision support (CDS) tools in electronic health records (EHRs) are often used as core strategies to support quality improvement programs in the clinical setting. Monitoring the impact (intended and unintended) of these tools is crucial for program evaluation and adaptation. Existing approaches for monitoring typically rely on health care providers’ self-reports or direct observation of clinical workflows, which require substantial data collection efforts and are prone to reporting bias.

|
Article Thumbnail
Reviews

Over the past 2 decades, various desktop and mobile telemedicine systems have been developed to support communication and care coordination among distributed medical teams. However, in the hands-busy care environment, such technologies could become cumbersome because they require medical professionals to manually operate them. Smart glasses have been gaining momentum because of their advantages in enabling hands-free operation and see-what-I-see video-based consultation. Previous research has tested this novel technology in different health care settings.

|
Article Thumbnail
Health Information Exchange

Interoperability—the exchange and integration of data across the health care system—remains a challenge despite ongoing policy efforts aimed at promoting interoperability.

|
Article Thumbnail
Natural Language Processing

Management of abdominal aortic aneurysms (AAAs) requires serial imaging surveillance to evaluate the aneurysm dimension. Natural language processing (NLP) has been previously developed to retrospectively identify patients with AAA from electronic health records (EHRs). However, there are no reported studies that use NLP to identify patients with AAA in near real-time from radiology reports.

|
Article Thumbnail
Decision Support for Health Professionals

Prevention of binge eating through just-in-time mobile interventions requires the prediction of respective high-risk times, for example, through preceding affective states or associated contexts. However, these factors and states are highly idiographic; thus, prediction models based on averages across individuals often fail.

|

We are working in partnership with