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 3.1

JMIR Medical Informatics (JMI, ISSN 2291-9694, Impact Factor: 3.2) (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 2023, JMI received a Journal Impact Factor™ of 3.2 (5-Year Journal Impact Factor: 3.6) (Source: Journal Citation Reports™ from Clarivate, 2023).

Recent Articles

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Ontologies, Classifications, and Coding

Chronic obstructive pulmonary disease (COPD) is a chronic condition among the main causes of morbidity and mortality worldwide, representing a burden on health care systems. Scientific literature highlights that nutrition is pivotal in respiratory inflammatory processes connected to COPD, including exacerbations. Patients with COPD have an increased risk of developing nutrition-related comorbidities, such as diabetes, cardiovascular diseases, and malnutrition. Moreover, these patients often manifest sarcopenia and cachexia. Therefore, an adequate nutritional assessment and therapy are essential to help individuals with COPD in managing the progress of the disease. However, the role of nutrition in pulmonary rehabilitation (PR) programs is often underestimated due to a lack of resources and dedicated services, mostly because pneumologists may lack the specialized training for such a discipline.

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Secondary Use of Clinical Data for Research and Surveillance

Self-administered web-based questionnaires are widely used to collect health data from patients and clinical research participants. REDCap (Research Electronic Data Capture; Vanderbilt University) is a global, secure web application for building and managing electronic data capture. Unfortunately, stakeholder needs and preferences of electronic data collection via REDCap have rarely been studied.

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Implementation Report

Biomedical data warehouses have become an essential tool to facilitate the reuse of health data for both research and decisional applications. Beyond technical issues, the implementation of biomedical data warehouses (BDW) requires strong institutional data governance and operational knowledge of the European and national legal framework for the management of research data access and use.

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Ontologies, Classifications, and Coding

Dermoscopy is a growing field that uses microscopy to allow dermatologists and primary care physicians to identify skin lesions. For a given skin lesion, a wide variety of differential diagnoses exist, which may be challenging for inexperienced users to name and understand.

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Natural Language Processing

Vaccines serve as a crucial public health tool, although vaccine hesitancy continues to pose a significant threat to full vaccine uptake and, consequently, community health. Understanding and tracking vaccine hesitancy is essential for effective public health interventions; however, traditional survey methods present various limitations.

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Adoption and Change Management of eHealth Systems

The chronic disease information systems in hospitals and communities play a significant role in disease prevention, control, and monitoring. However, due to various reasons, the platforms are generally isolated, the patient health information and medical resources are not effectively integrated, and Internet plus medical technology is not implemented throughout the patient consultation process.

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Decision Support for Health Professionals

Diagnostic errors pose significant health risks and contribute to patient mortality. With the growing accessibility of electronic health records, machine learning models offer a promising avenue for enhancing diagnosis quality. Current research has primarily focused on a limited set of diseases with ample training data, neglecting diagnostic scenarios with limited data availability.

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Decision Support for Health Professionals

Synthetic patient data (SPD) generation for survival analysis in oncology trials holds significant potential for accelerating clinical development. Various machine learning methods, including classification and regression trees (CART), random forest (RF), Bayesian network (BN), and CTGAN, have been employed for this purpose, but their performance in reflecting actual patient survival data remains under investigation.

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Viewpoints on and Experiences with Digital Technologies in Health

The extensive utilization of personal health data is one of the key success factors of modern medical research. Obtaining consent to the use of such data during clinical care, however, bears the risk of low and unequal approval rates and of consequent methodological problems in the scientific use of the data. In view of these shortcomings, and of the proven willingness of people to contribute to medical research by sharing personal health data, the paradigm of informed consent needs to be reconsidered. The European General Data Protection Regulation gives the EU member states considerable leeway with regard to permitting the research use of health data without consent. Following this approach would however require alternative offers of information that compensate for the lack of direct communication with experts during medical care. We therefore introduce the concept of ‘health data literacy’, defined as the capacity to find, understand and evaluate information about the risks and benefits of the research use of personal health data, and to act accordingly. Specifically, health data literacy includes basic knowledge about the goals and methods of data-rich medical research, and about the possibilities and limits of data protection. Although the responsibility for developing the necessary resources lies primarily with those directly involved in data-rich medical research, improving health data literacy should ultimately be of concern to everyone interested in the success of this type of research.

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Reviews in Medical Informatics

Burnout among health care professionals is a significant concern, with detrimental effects on health care service quality and patient outcomes. The use of the electronic health record (EHR) system has been identified as a significant contributor to burnout among health care professionals.

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Decision Support for Health Professionals

Tinnitus diagnosis poses a challenge in otolaryngology owing to an extremely complex pathogenesis, lack of effective objectification methods, and factor-affected diagnosis. There is currently a lack of explainable auxiliary diagnostic tools for tinnitus in clinical practice.

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Decision Support for Health Professionals

Despite restrictive opioid management guidelines, opioid use disorder (OUD) remains a major public health concern. Machine learning (ML) offers a promising avenue for identifying and alerting clinicians about OUD, thus supporting better clinical decision-making regarding treatment. The performance of a ML application to alert clinicians of a patient’s risk of OUD, was evaluated by comparing it to a structured chart review by clinicians.

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Preprints Open for Peer-Review

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