e.g. mhealth
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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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However, given high recidivism rates [77], many incarcerated individuals will have multiple data points in the jail EHR to inform HIV risk prediction, including data on prior HIV/STI testing and substance use.
JMIR Res Protoc 2025;14:e64813
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Electronic health record (EHR) data have many analytic uses, including patient monitoring, clinical decision support, quality improvement projects, and research initiatives [1]. However, missing data are pervasive in EHRs because these systems were largely designed for the purposes of billing and because of the fragmented nature of health care in the United States where patients often use multiple health systems with disparate EHR systems.
JMIR Med Inform 2025;13:e64354
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Using natural language processing (NLP) technology, we developed a system that can extract relevant postoperative problems from unstructured EHR data [4]. We questioned whether such an AI-supported approach might be used to provide precise, continuing feedback on the quality of care provided after THA in a high-volume, nonacademic clinical setting. We assumed that a computer algorithm would perform at least as well as a human reviewer, which is considered the industry standard.
JMIR Med Inform 2025;13:e64705
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Inclusion criteria are age 18 years or older, a clinical diagnosis of ALS by a qualified neurologist as documented in the EHR, home zip code within 100 miles of the clinic, and either the presence of a live-in caregiver (eg, spouse or adult child) or a Montreal Cognitive Assessment (Mo CA) score >22.
JMIR Res Protoc 2025;14:e60437
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The CASI is a patient portal- and electronic health record (EHR)–integrated tool that aims to improve patient and caregiver well-being by routinely eliciting patients’ and caregivers’ values, preferences, and supportive care needs. The CASI supports agenda-setting and person-centered communication between patients, caregivers, and clinicians.
JMIR Cancer 2025;11:e66801
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This study is a retrospective electronic health record (EHR) data analysis. This retrospective observational study used EHR data collected at a tertiary acute care hospital with over 2500 medical conditions in Seoul, Republic of Korea. To develop a readmission early prediction model for readmission of high-risk discharge patients, we employed a retrospective study design utilizing nursing data.
JMIR Med Inform 2025;13:e56671
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