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Association of Virtual Nurses’ Workflow and Cognitive Fatigue During Inpatient Encounters: Cross-Sectional Study

Association of Virtual Nurses’ Workflow and Cognitive Fatigue During Inpatient Encounters: Cross-Sectional Study

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.

Saif Khairat, Jennifer Morelli, Wan-Ting Liao, Julia Aucoin, Barbara S Edson, Cheryl B Jones

JMIR Hum Factors 2025;12:e67111

Developing a Machine Learning Model for Predicting 30-Day Major Adverse Cardiac and Cerebrovascular Events in Patients Undergoing Noncardiac Surgery: Retrospective Study

Developing a Machine Learning Model for Predicting 30-Day Major Adverse Cardiac and Cerebrovascular Events in Patients Undergoing Noncardiac Surgery: Retrospective Study

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).

Ju-Seung Kwun, Houng-Beom Ahn, Si-Hyuck Kang, Sooyoung Yoo, Seok Kim, Wongeun Song, Junho Hyun, Ji Seon Oh, Gakyoung Baek, Jung-Won Suh

J Med Internet Res 2025;27:e66366

Development of a Predictive Dashboard With Prescriptive Decision Support for Falls Prevention in Residential Aged Care: User-Centered Design Approach

Development of a Predictive Dashboard With Prescriptive Decision Support for Falls Prevention in Residential Aged Care: User-Centered Design Approach

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].

S Sandun Malpriya Silva, Nasir Wabe, Amy D Nguyen, Karla Seaman, Guogui Huang, Laura Dodds, Isabelle Meulenbroeks, Crisostomo Ibarra Mercado, Johanna I Westbrook

JMIR Aging 2025;8:e63609

Exploring Physicians’ Dual Perspectives on the Transition From Free Text to Structured and Standardized Documentation Practices: Interview and Participant Observational Study

Exploring Physicians’ Dual Perspectives on the Transition From Free Text to Structured and Standardized Documentation Practices: Interview and Participant Observational Study

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.

Olga Golburean, Rune Pedersen, Line Melby, Arild Faxvaag

JMIR Form Res 2025;9:e63902

Identifying Data-Driven Clinical Subgroups for Cervical Cancer Prevention With Machine Learning: Population-Based, External, and Diagnostic Validation Study

Identifying Data-Driven Clinical Subgroups for Cervical Cancer Prevention With Machine Learning: Population-Based, External, and Diagnostic Validation Study

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.

Zhen Lu, Binhua Dong, Hongning Cai, Tian Tian, Junfeng Wang, Leiwen Fu, Bingyi Wang, Weijie Zhang, Shaomei Lin, Xunyuan Tuo, Juntao Wang, Tianjie Yang, Xinxin Huang, Zheng Zheng, Huifeng Xue, Shuxia Xu, Siyang Liu, Pengming Sun, Huachun Zou

JMIR Public Health Surveill 2025;11:e67840

Enhancing Patient Outcome Prediction Through Deep Learning With Sequential Diagnosis Codes From Structured Electronic Health Record Data: Systematic Review

Enhancing Patient Outcome Prediction Through Deep Learning With Sequential Diagnosis Codes From Structured Electronic Health Record Data: Systematic Review

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.

Tuankasfee Hama, Mohanad M Alsaleh, Freya Allery, Jung Won Choi, Christopher Tomlinson, Honghan Wu, Alvina Lai, Nikolas Pontikos, Johan H Thygesen

J Med Internet Res 2025;27:e57358

Development of a Clinical Decision Support Tool to Implement Asthma Management Guidelines in Pediatric Primary Care: Qualitative Study

Development of a Clinical Decision Support Tool to Implement Asthma Management Guidelines in Pediatric Primary Care: Qualitative Study

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].

David A Fedele, Jessica M Ray, Jaya L Mallela, Jiang Bian, Aokun Chen, Xiao Qin, Ramzi G Salloum, Maria Kelly, Matthew J Gurka, Jessica Hollenbach

JMIR Form Res 2025;9:e65794

Imputation and Missing Indicators for Handling Missing Longitudinal Data: Data Simulation Analysis Based on Electronic Health Record Data

Imputation and Missing Indicators for Handling Missing Longitudinal Data: Data Simulation Analysis Based on Electronic Health Record Data

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.

Molly Ehrig, Garrett S Bullock, Xiaoyan Iris Leng, Nicholas M Pajewski, Jaime Lynn Speiser

JMIR Med Inform 2025;13:e64354

Assessing Total Hip Arthroplasty Outcomes and Generating an Orthopedic Research Outcome Database via a Natural Language Processing Pipeline: Development and Validation Study

Assessing Total Hip Arthroplasty Outcomes and Generating an Orthopedic Research Outcome Database via a Natural Language Processing Pipeline: Development and Validation Study

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.

Nicholas H Mast, Clara L. Oeste, Dries Hens

JMIR Med Inform 2025;13:e64705