e.g. mhealth
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Asynchronous text-based communication—generally referred to as secure messaging—can be used either within the electronic health record (EHR) or through independent secure mobile platforms and is rapidly becoming the primary mode of communication in modern clinical settings [4].
JMIR Med Inform 2025;13:e66544
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In data analysis, missing weights will be managed using multiple imputations from time points near NDPP session delivery from GSM (global system for mobile communication) scale weights, weights extracted from EHR, and self-reported weights.
Inclusion criteria
Identified as Hispanic or Latino and or African American or Black as indicated in the electronic health record and later by self-report during screening.
JMIR Res Protoc 2025;14:e64405
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The findings from our retrospective descriptive study are consistent with that of department-focused “EHR thrive” training conducted by Livingston and Bovi [24], in which trained clinicians demonstrated improved efficiency in time management across documentation, basket management, and clinical orders.
JMIR Form Res 2025;9:e68491
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In contrast to imaging and genomic data, structured data from the electronic health record (EHR) offers a more accessible and cost-effective data source for initial research. Originally designed for administrative and billing purposes, structured EHR data have evolved into valuable tools for health care research, capturing a wealth of patient information, including clinical diagnoses, procedures, medications, and laboratory results, among others [11].
JMIR Cancer 2025;11:e64506
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Over the course of the project, the researchers aim to understand how health care professionals are currently using EMRs and EHRs to support their practice, what the role of these technologies is in performance feedback and reflective practice of medical practitioners, and how the design of these technologies can be rethought to support a “next-generation” EHR that could support reflective practice.
JMIR Res Protoc 2025;14:e66824
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However, assessing RECIST in retrospective electronic health record (EHR) data is challenging due to its strict assessment indicators [4]. RECIST considers changes in the size of individual target lesions over time and the presence or absence of new lesions to categorize disease status into complete or partial response, stable disease, or progression [5].
JMIR Cancer 2025;11:e64697
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AI copilots have benefits, but they operate on a manually maintained, costly, and continuously noncurrent EHR content and configurations, ie, their effectiveness is fundamentally limited by flaws in the underlying EHR architecture. These flaws result from the complexity and scale of configurable “solutions” that comprise health record platforms; to solve this issue, we propose the “Elastic EHR”.
JMIR AI 2025;4:e66741
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One of the main challenges has been an approach to rapidly identify and address bottlenecks and issues related to the EHR. As a result, we developed the “SWAT” initiative, which focuses on bringing an interdisciplinary team to rapidly triage and address issues related to the EHR in an agile manner [19].
JMIR Hum Factors 2025;12:e65656
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Association Between Risk Factors and Major Cancers: Explainable Machine Learning Approach
Machine learning has shown promising potential in cancer prediction by leveraging electronic health record (EHR) data to identify risk factors [17]. Current applications range from developing predictive models for early cancer detection to personalized treatment recommendations and outcome predictions, based on various patient characteristics and biomarkers. Despite these advancements, several challenges remain in cancer prediction using machine learning [18].
JMIR Cancer 2025;11:e62833
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