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Skip search results from other journals and go to results- 73 JMIR Medical Informatics
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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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The eye-tracking device was set up at the virtual nurses’ workstations on the monitor that displays activity in the EHR. Prior to recording sessions, virtual nurses were oriented with the eye-tracking device and instructed to complete their tasks as they normally would. An initial calibration session was completed with each virtual nurse before recordings began to ensure data quality.
JMIR Hum Factors 2025;12:e67111
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This extensive dataset included a comprehensive array of demographic information and detailed preoperative baseline characteristics, including diagnosis codes, underlying diseases, laboratory test results, medications, type of surgery, and clinical outcomes from the EHR system (Table 1).
J Med Internet Res 2025;27:e66366
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As a growing number of RACFs implement electronic health record (EHR) systems, new opportunities have emerged to develop a personalized, dynamic approach to predicting residents’ fall risk by taking advantage of multiple potential contributory factors [8]. Some studies have integrated routinely collected EHR data including vital signs into the development of fall prediction tools through the application of machine learning models [9].
JMIR Aging 2025;8:e63609
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EHR: electronic health record; SNOMED CT: Systematized Nomenclature of Medicine–Clinical Terms.
Physicians encountered a learning curve while transitioning to the new EHR system, necessitating adjustments to specific documentation practices.
JMIR Form Res 2025;9:e63902
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Additionally, EHR data from 5 other regions—Shenzhen City, Foshan City, Hubei Province, Gansu Province, and Guizhou Province—were employed as an external cohort to validate the generalizability of the models across diverse populations. Details on the study design, as illustrated in Figure 1, are available in Appendix S1 in Multimedia Appendix 1.
Study design.
JMIR Public Health Surveill 2025;11:e67840
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However, traditional ML approaches cannot take full advantage of structured EHR data due to four key challenges:
Feature selection—manual feature selection, which requires medical knowledge from professional health care workers, is a time-consuming task and an expensive process.
J Med Internet Res 2025;27:e57358
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Clinical decision support (CDS) tools are health IT systems that can be housed in the electronic health record (EHR) system and be effective in improving provider adherence to guidelines and patient outcomes [22,23].
JMIR Form Res 2025;9:e65794
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