Self-regulating the use of large language models in clinical practice: a risk-stratified approach [0.03%]
基于风险的分层策略自主规范大型语言模型在临床实践中的应用
Milan Mohammad,Espen Jimenez-Solem,Michael Hejmadi et al.
Milan Mohammad et al.
The rapid integration of large language models (LLMs) into clinical practice offers promising benefits, including assistance with documentation, decision support and patient communication. However, these advantages are tempered by concerns ...
Early sepsis prediction using a hybrid LSTM-GAT model: a study on the PhysioNet 2019 dataset [0.03%]
基于混合LSTM-GAT模型的早期脓毒症预测：对PhysioNet 2019数据集的研究
Bahar Khorram,Samaneh Kouchaki
Bahar Khorram
Objective: Sepsis is a potentially fatal systemic response to infection, in which early clinical intervention is critical to reduce mortality. This study presents a hybrid deep learning model that combines temporal and st...
i-MoMCARE: AI-enabled mobile app for maternal and child health care in Cambodia - a pilot implementation and evaluation study [0.03%]
柬埔寨用于妇女儿童保健的AI驱动型移动应用程序i-MoMCARE试点实施与评估研究
Hendra Goh,Dyna Khuon,Mengieng Ung et al.
Hendra Goh et al.
Objectives: In Cambodia, village health support groups (VHSGs) are central to maternal and child health (MCH) service delivery, yet maternal mortality remains high with regional disparities. Although digital health soluti...
Does the accuracy of medication administration documentation improve with electronic medication systems? A stepped-wedge cluster randomised trial [0.03%]
基于步进楔形集群的随机试验：电子化药物管理系统能否提高给药记录准确性？
Tim Badgery-Parker,Ling Li,Amanda Woods et al.
Tim Badgery-Parker et al.
Objectives: Accurate documentation of medication administration is crucial for patient safety. This study compares the accuracy of documentation between paper-based and electronic medication management (eMM) systems. ...
Randomized Controlled Trial
BMJ health & care informatics. 2026 Apr 24;33(1):e101507. DOI:10.1136/bmjhci-2025-101507 2026
AI-generated clinical summaries: errors and susceptibility to speech and speaker variability [0.03%]
基于人工智能的临床小结中的错误及口语和说话人变化的影响
Thomas C Draper,Jason Leake,Timothy Cox et al.
Thomas C Draper et al.
Objectives: To evaluate whether variability in patients' communication style (personality), international English-accents (human and synthetic) and speech impairments affects the accuracy of a Clinical AI Scribe (CAIS) an...
Omission and hallucination prevalence of clinical guidelines in diagnostic large language model outputs [0.03%]
临床指南在诊断大型语言模型输出中的遗漏和幻觉发生率
Robin van Kessel,Michael Anderson,Brian McMillan et al.
Robin van Kessel et al.
Objective: Meaningful assessments of how large language models (LLMs) incorporate clinical guidelines require large-scale testing over many queries. Here, we evaluate the prevalence of clinical guideline omissions and hal...
Using a large language model artificial intelligence agent to improve the efficiency of clinical quality measure evidence evaluation: a case study [0.03%]
利用大型语言模型人工智能代理提高临床质量测量证据评估效率的案例研究
Jeffrey Geppert,Barbara Jones,Jeremy Bellay et al.
Jeffrey Geppert et al.
Objectives: To evaluate the feasibility and performance of a large language model (LLM)-based artificial intelligence (AI) agent, implemented within a structured Claim-Argument-Evidence System (CAES), for supporting the r...
Barriers associated with the implementation, adoption, scale-up and sustainability of mHealth in Sub-Saharan Africa: a systematic review guided by the NASSS Framework [0.03%]
移动健康在撒哈拉以南非洲地区实施、采纳、扩大规模及可持续发展过程中遇到的障碍：基于NASSS框架的系统评价研究
Jordan Murray,Thomas Connolly,Zaeem Haq et al.
Jordan Murray et al.
Objectives: The objectives of this study were to highlight and evaluate the factors affecting the non-adoption, abandonment of and challenges to scale-up, spread and sustainability of mHealth in Sub-Saharan Africa (SSA) a...
Supporting street medicine through medical informatics: a manifesto on research needs [0.03%]
街医运动的信息需求及研究方向宣言
Alfred Franz Winter,Andrea Alverà,Elske Ammenwerth et al.
Alfred Franz Winter et al.
Homeless individuals face major barriers in accessing regular healthcare, leading to the development of street medicine as a distinct humanitarian field. To enhance continuity and quality of care, consistent documentation is crucial. Howeve...
Robotic process automation for identifying missing codes on insurance claims [0.03%]
基于保险理赔的机器人流程自动化识别遗漏编码的方法
Jiyun Lee,Jun Hwan Cho,Won Joo Lee et al.
Jiyun Lee et al.
Objectives: This study aimed to develop and implement robotic process automation (RPA) for identifying missing codes during insurance claim post-review at a tertiary hospital and to evaluate its feasibility and effectiven...