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 CiteScore 7.9

JMIR Medical Informatics (JMI, ISSN 2291-9694, Journal Impact Factor™ 3.1 (Journal Citation Reports™ from Clarivate, 2023)) (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 MEDLINEPubMed, PubMed Central, DOAJ, Scopus, and SCIE (Clarivate)

With a CiteScore of 7.9, JMIR Medical Informatics ranks in the 78th percentile (#30 of 138) and the 77th percentile (#14 of 59) as a Q1 journal in the fields of Health Informatics and Health Information Management, according to Scopus data.

Recent Articles

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Standards and Interoperability

The increasing demand for personal health record (PHR) systems is driven by individuals’ desire to actively manage their healthcare. However, the limited functionality of current PHR systems has affected users’ willingness to adopt them, leading to lower-than-expected usage rates. The HL7 Personal Health Record System Functional Model (PHR-S FM) was proposed to address this issue, outlining all possible functionalities in PHR systems. Although the PHR-S FM provides a comprehensive theoretical framework, its practical effectiveness and applicability have not been fully explored.

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Health Information Exchange

Over 200 health information exchanges (HIEs) are currently operational in Japan. The feature for remote on-demand viewing, or searching for aggregated patient health data from multiple institutions is the most common. However, the usage of this feature by individual users and institutions remains unknown.

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Reviews in Medical Informatics

Large language models (LLMs) have substantially advanced natural language processing (NLP) capabilities but often struggle with knowledge-driven tasks in specialized domains such as biomedicine. Integrating biomedical knowledge sources such as SNOMED CT into LLMs may enhance their performance on biomedical tasks. However, the methodologies and effectiveness of incorporating SNOMED CT into LLMs have not been systematically reviewed.

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AI Language Models in Health Care

Generative artificial intelligence (GAI) systems by Google have recently been updated from Bard to Gemini and Gemini Advanced as of December 2023. Gemini is a basic, free-to-use model after a user’s login, while Gemini Advanced operates on a more advanced model requiring a fee-based subscription. These systems have the potential to enhance medical diagnostics. However, the impact of these updates on comprehensive diagnostic accuracy remains unknown.

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Secondary Use of Clinical Data for Research and Surveillance

Electronic health records (EHRs) are increasingly used for epidemiologic research to advance public health practice. However, key variables are susceptible to missing data or misclassification within EHRs, including demographic information or disease status, which could affect estimation of disease prevalence or risk factor associations.

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Natural Language Processing

Electronic Medical Records (EMRs) store extensive patient data and serve as a comprehensive repository, including textual medical records like surgical and imaging reports. Their utility in Clinical Decision Support Systems (CDSS) is significant, but the widespread use of ambiguous and unstandardized abbreviations in clinical documents poses challenges for Natural Language Processing (NLP) in CDSS. Efficient abbreviation disambiguation methods are needed for effective information extraction.

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Research Letter

Cardiovascular drug development requires synthesizing relevant literature about indications, mechanisms, biomarkers, and outcomes, with large language models (LLMs) capable of potentially addressing this need.

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Research Infrastructures and Registries

Data element repositories facilitate high-quality medical data sharing by standardizing data and enhancing semantic interoperability. However, the application of repositories is confined to specific projects and institutions.

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Implementation Report

Electronic medical record (EMR) systems are essential in health care for collecting and storing patient medical data. They provide critical information to doctors and caregivers, facilitating improved decision-making and patient care. Despite their significance, optimizing EMR systems is crucial for enhancing health care quality. Implementing the Observational Medical Outcomes Partnership (OMOP) shared data model represents a promising approach to improve EMR performance and overall health care outcomes.

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Research Infrastructures and Registries

The World Health Organization (WHO) reported that cardiovascular diseases (CVDs) are the leading cause of death worldwide. CVDs are chronic, with complex progression patterns involving episodes of comorbidities and multimorbidities. When dealing with chronic diseases, physicians often adopt a “watchful waiting” strategy, and actions are postponed until information is available. Population-level transition probabilities and progression patterns can be revealed by applying time-variant stochastic modeling methods to longitudinal patient data from cohort studies. Inputs from CVD practitioners indicate that tools to generate and visualize cohort transition patterns have many impactful clinical applications. The resultant computational model can be embedded in digital decision support tools for clinicians. However, to date, no study has attempted to accomplish this for CVDs.

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Natural Language Processing

Natural language processing (NLP) techniques can be used to analyze large amounts of electronic health record texts, which encompasses various types of patient information such as quality of life, effectiveness of treatments, and adverse drug event (ADE) signals. As different aspects of a patient’s status are stored in different types of documents, we propose an NLP system capable of processing 6 types of documents: physician progress notes, discharge summaries, radiology reports, radioisotope reports, nursing records, and pharmacist progress notes.

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Reviews in Medical Informatics

Data models are crucial for clinical research as they enable researchers to fully use the vast amount of clinical data stored in medical systems. Standardized data and well-defined relationships between data points are necessary to guarantee semantic interoperability. Using the Fast Healthcare Interoperability Resources (FHIR) standard for clinical data representation would be a practical methodology to enhance and accelerate interoperability and data availability for research.

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Preprints Open for Peer-Review

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