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Improving Phenotyping of Patients With Immune-Mediated Inflammatory Diseases Through Automated Processing of Discharge Summaries: Multicenter Cohort Study

Improving Phenotyping of Patients With Immune-Mediated Inflammatory Diseases Through Automated Processing of Discharge Summaries: Multicenter Cohort Study

Since the 2010s, the widespread adoption of electronic health records (EHRs) and health data warehouses has enabled the development and application of new algorithms for patient phenotyping, which corresponds to the extraction of a set of observable patient characteristics, including laboratory test results, symptoms, diseases, and past or current treatments [1].

Adam Remaki, Jacques Ung, Pierre Pages, Perceval Wajsburt, Elise Liu, Guillaume Faure, Thomas Petit-Jean, Xavier Tannier, Christel Gérardin

JMIR Med Inform 2025;13:e68704

Efficacy of a Personalized mHealth App in Improving Micronutrient Supplement Use Among Pregnant Women in Karachi, Pakistan: Parallel-Group Randomized Controlled Trial

Efficacy of a Personalized mHealth App in Improving Micronutrient Supplement Use Among Pregnant Women in Karachi, Pakistan: Parallel-Group Randomized Controlled Trial

These envelopes were provided to the research team and were opened by the research assistant (RA) and Ph D research scholar after obtaining written informed consent from the participants. The allocation ratio between the intervention and nonintervention groups was 1:1. Blinding of participants and the research implementation team was not feasible due to the nature of the intervention.

Khadija Vadsaria, Rozina Nuruddin, Nuruddin Mohammed, Iqbal Azam, Saleem Sayani

J Med Internet Res 2025;27:e67166

Factors Impacting Mobile Health Adoption for Depression Care and Support by Adolescent Mothers in Nigeria: Preliminary Focus Group Study

Factors Impacting Mobile Health Adoption for Depression Care and Support by Adolescent Mothers in Nigeria: Preliminary Focus Group Study

In Nigeria, digital research has been successfully conducted for perinatal mental care within the WHO Mental Health Global Action Programme (mh GAP) task-shifting initiative in primary care [4-6] to address barriers to care in patients. Task shifting, which is a process of delegating tasks to less specialized health care workers where appropriate, is recommended by the WHO to bridge the treatment gap in global mental health [7].

Lola Kola, Tobi Fatodu, Manasseh Kola, Bisola A Olayemi, Adeyinka O Adefolarin, Simpa Dania, Manasi Kumar, Dror Ben-Zeev

JMIR Form Res 2025;9:e42406

Young Adult Perspectives on Artificial Intelligence–Based Medication Counseling in China: Discrete Choice Experiment

Young Adult Perspectives on Artificial Intelligence–Based Medication Counseling in China: Discrete Choice Experiment

Artificial intelligence (AI) is revolutionizing health care by improving the quality of care and facilitating personalized patient engagement. Tools such as virtual assistant chatbots and wearable devices provide real-time support and continuous health monitoring, leading to improved patient outcomes [1-5]. According to the Microsoft-International Data Corporation study in March 2024 [6], 79% of the health care organizations were using AI technology.

Jia Zhang, Jing Wang, JingBo Zhang, XiaoQian Xia, ZiYun Zhou, XiaoMing Zhou, YiBo Wu

J Med Internet Res 2025;27:e67744

Extracting Pulmonary Embolism Diagnoses From Radiology Impressions Using GPT-4o: Large Language Model Evaluation Study

Extracting Pulmonary Embolism Diagnoses From Radiology Impressions Using GPT-4o: Large Language Model Evaluation Study

Early documentation of PE and its extraction in the electronic medical record system, and consequently in clinical workflows, is crucial for improving patient outcomes. In this study, we aim to develop an advanced transformer-based text classification model to extract PE diagnoses from the impression section of radiology reports, expediting structured data availability and enhancing the quality of care through evidence-based practices.

Mohammed Mahyoub, Kacie Dougherty, Ajit Shukla

JMIR Med Inform 2025;13:e67706

Co-Designing a Web-Based and Tablet App to Evaluate Clinical Outcomes of Early Psychosis Service Users in a Learning Health Care Network: User-Centered Design Workshop and Pilot Study

Co-Designing a Web-Based and Tablet App to Evaluate Clinical Outcomes of Early Psychosis Service Users in a Learning Health Care Network: User-Centered Design Workshop and Pilot Study

The Early Psychosis Intervention Network of California (EPI-CAL) [4] was developed to support the provision of quality EPI services and to create an infrastructure to conduct standardized measurement of the impact of early psychosis care delivery. To support this goal, the EPI-CAL team, in collaboration with several California counties, developed a learning health care network (LHCN) consisting of EPI programs across the state.

Kathleen E Burch, Valerie L Tryon, Katherine M Pierce, Laura M Tully, Sabrina Ereshefsky, Mark Savill, Leigh Smith, Adam B Wilcox, Christopher Komei Hakusui, Viviana E Padilla, Amanda P McNamara, Merissa Kado-Walton, Andrew J Padovani, Chelyah Miller, Madison J Miles, Nitasha Sharma, Khanh Linh H Nguyen, Yi Zhang, Tara A Niendam

JMIR Hum Factors 2025;12:e65889

A Risk Prediction Model (CMC-AKIX) for Postoperative Acute Kidney Injury Using Machine Learning: Algorithm Development and Validation

A Risk Prediction Model (CMC-AKIX) for Postoperative Acute Kidney Injury Using Machine Learning: Algorithm Development and Validation

Acute kidney injury (AKI) represents a critical challenge in postoperative care, significantly affecting patient outcomes and health care systems. It is a common complication that affects up to 5% to 7.5% of all hospitalized patients, with a markedly higher prevalence of 20% in intensive care units [1]. Among all AKI in hospitalized patients, 40% occur in postoperative patients [1].

Ji Won Min, Jae-Hong Min, Se-Hyun Chang, Byung Ha Chung, Eun Sil Koh, Young Soo Kim, Hyung Wook Kim, Tae Hyun Ban, Seok Joon Shin, In Young Choi, Hye Eun Yoon

J Med Internet Res 2025;27:e62853

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

With over 300 million noncardiac surgeries performed annually, accurate preoperative risk assessment has become essential to optimize patient outcomes and reduce health care costs [5,6]. However, the predictive accuracy of traditional assessment tools is not consistently high, and various tools are used at different physicians’ discretion [7].

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

Factors Influencing Information Distortion in Electronic Nursing Records: Qualitative Study

Factors Influencing Information Distortion in Electronic Nursing Records: Qualitative Study

As health care professionals increasingly rely on electronic medical records (EMRs) to support patient care, the consequences of information distortion become even more pronounced in the digital era. Technology-driven tools, such as clinical decision support systems and algorithms, depend on accurate EMR data to provide reliable guidance and improve health. However, inaccuracies in data can lead to inappropriate medical decisions being perpetuated across multiple patients [13,14].

Jianan Wang, Yihong Xu, Zhichao Yang, Jie Zhang, Xiaoxiao Zhang, Wen Li, Yushu Sun, Hongying Pan

J Med Internet Res 2025;27:e66959

A New Mobile App to Train Attention Processes in People With Traumatic Brain Injury: Logical and Ecological Content Validation Study

A New Mobile App to Train Attention Processes in People With Traumatic Brain Injury: Logical and Ecological Content Validation Study

For instance, a previous study from our team [14] showed that a mindfulness intervention integrated into an ecological virtual environment was safe, feasible, and acceptable for people with mild TBI. The use of attentional focusing techniques of mindfulness could also be useful for the management of attention difficulties in people who have experienced moderate to severe TBI. Indeed, focusing on the present moment is a way of controlling one’s own attentional processes.

Roxanne Laverdière, Philip L Jackson, Frédéric Banville

JMIR Form Res 2025;9:e64174