e.g. mhealth
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Skip search results from other journals and go to results- 80 JMIR Medical Informatics
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For instance, Berman et al [14] used comprehensive EHR data sourced from 2 prominent academic medical centers to identify variables predictive of 30-day readmission among patients with liver disease. Similarly, Hu and colleagues [15] conducted an analysis of 30-day and 90-day readmission rates for patients with end-stage liver disease, leveraging EHR data in conjunction with models such as logistic regression, support vector machines, and random forests.
JMIR Med Inform 2025;13:e63581
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Our NLP algorithm outperforms ICD codes in identifying NID patients and achieves competitive performance compared to the Llama 3.2 autoregressive model (an LLM with 3 B parameters) in zero-shot learning tasks, making it a valuable tool for large-scale EHR-based research to investigate the relationship between NID exposure and short- and long-term neurological outcomes.
JMIR Med Inform 2025;13:e63157
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Understanding the importance of broadband access, financial incentives such as the Centers for Medicare and Medicaid Services EHR (electronic health record) Incentive Program, also known as Meaningful Use Program, were created to support widespread implementation of internet-driven resources throughout health systems [1,5-7].
J Med Internet Res 2025;27:e69606
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EHR data consist of structured data, which are data in standardized format, and unstructured data, which are free-text data. Primary care (PC) EHR data provide extensive and longitudinal data from a patient’s health trajectory and changes over time. AI might prove to be a valuable method to extract clinically useful and actionable insight from this vast and complex source of patient data [13].
JMIR Med Inform 2025;13:e62862
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In this viewpoint paper, we highlight challenges in the quality and linkage of electronic health record (EHR) infrastructures in NHS England, including inconsistencies in data documentation, interoperability issues, and limitations in data linkage between primary and secondary care. Additionally, we discuss variations in findings due to differences in population characteristics, service settings, and outcome measures.
Online J Public Health Inform 2025;17:e66672
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Deep Phenotyping of Obesity: Electronic Health Record–Based Temporal Modeling Study
Using real-world EHR data from 444,219 patients with obesity or overweight diagnosed between 2005 and 2023, we analyzed commonly available data elements and their quality before pharmacotherapy. We also tested a multimodal longitudinal deep autoencoder to examine the feasibility, data requirements, clustering patterns, and challenges of EHR-based obesity deep phenotyping.
J Med Internet Res 2025;27:e70140
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EHR: electronic health record; ML: machine learning.
Relevant constructs for the provider study were drawn from the Consolidated Framework for Implementation Research (CFIR), a “metatheoretical” framework that synthesizes disparate implementation theories with a common taxonomy to facilitate theory-building of implementation science [28].
J Med Internet Res 2025;27:e57602
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The EHR integration is the premise and foundation of crossinstitutional “sharing and utilization,” which is the key link in releasing the EHR application, and also the bottleneck in the EHR development at present. There exists an urgent need to elucidate the co-opetition strategies between superior and subordinate hospitals facilitating EHR integration within medical consortiums, thereby establishing a theoretical foundation for policy optimization.
JMIR Med Inform 2025;13:e70866
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