e.g. mhealth
Search Results (1 to 10 of 234 Results)
Download search results: CSV END BibTex RIS
Skip search results from other journals and go to results- 73 JMIR Medical Informatics
- 52 Journal of Medical Internet Research
- 31 JMIR Formative Research
- 11 JMIR Research Protocols
- 10 JMIR Human Factors
- 9 JMIR Public Health and Surveillance
- 8 JMIRx Med
- 7 JMIR Aging
- 4 Interactive Journal of Medical Research
- 4 JMIR AI
- 3 JMIR Cancer
- 3 JMIR Cardio
- 3 JMIR Medical Education
- 3 JMIR mHealth and uHealth
- 2 JMIR Diabetes
- 2 JMIR Mental Health
- 2 JMIR Nursing
- 2 JMIR Pediatrics and Parenting
- 1 JMIR Bioinformatics and Biotechnology
- 1 JMIR Dermatology
- 1 JMIR Perioperative Medicine
- 1 Journal of Participatory Medicine
- 1 Online Journal of Public Health Informatics
- 0 Medicine 2.0
- 0 iProceedings
- 0 JMIR Serious Games
- 0 JMIR Rehabilitation and Assistive Technologies
- 0 JMIR Preprints
- 0 JMIR Challenges
- 0 JMIR Biomedical Engineering
- 0 JMIR Data
- 0 JMIRx Bio
- 0 JMIR Infodemiology
- 0 Transfer Hub (manuscript eXchange)
- 0 JMIR Neurotechnology
- 0 Asian/Pacific Island Nursing Journal
- 0 JMIR XR and Spatial Computing (JMXR)
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
Download Citation: END BibTex RIS
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
Download Citation: END BibTex RIS
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
Download Citation: END BibTex RIS
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
Download Citation: END BibTex RIS
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
Download Citation: END BibTex RIS
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
Download Citation: END BibTex RIS
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
Download Citation: END BibTex RIS
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
Download Citation: END BibTex RIS
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
Download Citation: END BibTex RIS
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
Download Citation: END BibTex RIS