Noninterruptive tool to support provider malnutrition documentation and minimize documentation queries [0.03%]
一种支持医护人员记录营养不良状况并减少医疗文件疑问的非打扰式工具
Kevin O&#x;Malley,Patricia Dasch,Sarah C Bauer et al.
Kevin O&#x;Malley et al.
Objectives: Determine if an electronic documentation tool can reduce documentation queries for malnutrition without impacting diagnostic coding. Materials...
Navigating the new NIH Data Management Sharing plan: the NIH HEAL Initiative Data2Action DMPTool template [0.03%]
NIH数据管理共享计划(NIH Data Management and Sharing plan)下的NIH资助型HEAL项目的数据管理计划模板(Data2Action DMPTool template)
Kaitlyn N Shreeve,Robert W Hurley,Meredith C B Adams
Kaitlyn N Shreeve
Objectives: This work aims to provide a specialized template for integrating diverse study data types with varying sharing restrictions specific to NIH HEAL Initiative chronic pain and substance use research. ...
An approach to evaluating the impact of virtual specialty care: the Veterans Health Administration's clinical resource hub as case study [0.03%]
虚拟专科护理影响评估方法：退伍军人健康管理局临床资源中心个案研究
Rebecca L Tisdale,Kedron Burnett,Matthew Rogers et al.
Rebecca L Tisdale et al.
Objectives: Telemedicine for specialty medical care is evolving from a COVID-19 pandemic-era requirement to an option for patients and clinicians alike, requiring evidence to guide optimal use of virtual specialty care. H...
A phenotyping algorithm for classification of single ventricle physiology using electronic health records [0.03%]
基于电子健康记录的单心室生理表型分类算法
Hang Xu,Pierangelo Renella,Ramin Badiyan et al.
Hang Xu et al.
Objectives: Congenital heart disease (CHD) patients with single ventricle physiology (SVP) have heterogeneous characteristics that challenge cohort classification. We aim to develop a phenotyping algorithm that accurately...
Evaluating algorithmic bias on biomarker classification of breast cancer pathology reports [0.03%]
评估生物标志物在乳腺癌病理报告分类中的算法偏差
Jordan Tschida,Mayanka Chandrashekar,Alina Peluso et al.
Jordan Tschida et al.
Objectives: This work evaluated algorithmic bias in biomarkers classification using electronic pathology reports from female breast cancer cases. Bias was assessed across 5 subgroups: cancer registry, race, Hispanic ethni...
Integrating public preferences to overcome racial disparities in research: findings from a US survey on enhancing trust in research data-sharing practices [0.03%]
整合公众偏好以克服研究中的种族差异：一项关于提升美国公众对科研数据共享实践信任度的调查结果
Stephanie Niño de Rivera,Yihong Zhao,Shalom Omollo et al.
Stephanie Niño de Rivera et al.
Objectives: Data-sharing policies are rapidly evolving toward increased data sharing. However, participants' perspectives are not well understood and could have an adverse impact on participation in research. We evaluated...
Assessing the acceptability of using patient portals to recruit pregnant women and new mothers for maternal-child health research [0.03%]
评估使用患者门户网站招募孕妇和新晋母亲参与母子健康研究的接受度
Sean N Halpin,Rebecca Wright,Angela Gwaltney et al.
Sean N Halpin et al.
Objective: Electronic patient portals (PP) allow for targeted and efficient research recruitment. We assessed pre- and postnatal women's recruitment methods preferences, focusing on PP. ...
Accurate treatment effect estimation using inverse probability of treatment weighting with deep learning [0.03%]
利用深度学习进行逆概率治疗加权的精确疗效估计
Junghwan Lee,Simin Ma,Nicoleta Serban et al.
Junghwan Lee et al.
Objectives: Observational data have been actively used to estimate treatment effect, driven by the growing availability of electronic health records (EHRs). However, EHRs typically consist of longitudinal records, often i...
"Everything is electronic health record-driven": the role of the electronic health record in the emergency department diagnostic process [0.03%]
“事事以电子健康档案为中心”——急诊科诊断过程中的电子病历的作用
Tyler G James,Courtney W Mangus,Sarah J Parker et al.
Tyler G James et al.
Objectives: There is limited knowledge on how providers and patients in the emergency department (ED) use electronic health records (EHRs) to facilitate the diagnostic process. While EHRs can support diagnostic decision-m...
Correction to: Leveraging deep learning to detect stance in Spanish tweets on COVID-19 vaccination [0.03%]
Correction to: 利用深度学习检测西班牙语推文中的COVID-19疫苗接种立场
[This corrects the article DOI: 10.1093/jamiaopen/ooaf007.]. © The Author(s) 2025. Published by Oxford University Press on behalf of the American Medica...
Published Erratum
JAMIA open. 2025 Apr 15;8(2):ooaf028. DOI:10.1093/jamiaopen/ooaf028 2025