The HALO Model: A Learning Health System Framework for Artificial Intelligence [0.03%]
HALO模型：人工智能学习健康系统的框架
Adrian H Zai,Mohammad Adibuzzaman,David D McManus et al.
Adrian H Zai et al.
Introduction: Artificial intelligence is increasingly embedded in healthcare delivery, yet existing Learning Health System (LHS) models do not fully account for the lifecycle management and continuous assurance requiremen...
Surveillance of Pharmaceutical Risk-Mitigation Behavior: Applying and Comparing Statistical Process Control Methods Using Real World Data [0.03%]
基于真实世界数据的药品风险管控行为监测及统计过程控制方法比较研究
Harris Butler,John D Rice,Nichole E Carlson et al.
Harris Butler et al.
Introduction: Active post-marketing surveillance of prescribing behavior of high-risk drugs may provide early warning of unforeseen issues in a population, yet analysis approaches for surveillance using real-world data ar...
The Value and Challenges of Stakeholder Engagement in Rehabilitation Learning Health Systems: A Qualitative Pilot Study of Rehabilitation Directors [0.03%]
康复学习健康系统中利益相关者参与的价值与挑战：康复主任定性试点研究
Nichole E Stetten,Kristin Ressel,Linda Resnik et al.
Nichole E Stetten et al.
Introduction: Stakeholder engagement is a core element of a learning health system (LHS). Meaningful engagement of stakeholders can improve learning within a health system by informing program development and service deli...
Shared Experiences and Care Improvement Priorities for Multimorbidity Management: An Experience-Based Co-Design Study [0.03%]
针对多病共存的照护改进优先项及共享体验：一项基于经验共同设计的研究
Binu Koirala,Chitchanok Benjasirisan,Arum Lim et al.
Binu Koirala et al.
Background: Multimorbidity-coexistence of two or more chronic conditions in the same individual-is a growing global healthcare challenge. Despite recognition of the difficulties in managing multimorbidity, there is a limi...
AI-powered nursing handoffs: Introducing and evaluating the patient report template [0.03%]
基于人工智能的护理交接报告：介绍及评估患者报告模板
Gabriel Vald,Yusuf Sermet,Nai-Ching Chi et al.
Gabriel Vald et al.
Purpose: To understand (1) if nurses view the individual components of the Patient Report Template as useful in their day-to-day workflow, and (2) understand the perceptions of nurses related to artificial intelligence us...
Large Language Models for Summarizing Advance Care Planning Information From Goals of Care Notes in the EHR [0.03%]
大型语言模型在电子健康记录中从护理目标笔记总结护理规划信息中的应用
Ninad Ekbote,Melody Akhondzadeh,Ross Graham et al.
Ninad Ekbote et al.
Objectives: Embedding systematic, structured data extraction within electronic health records (EHR) is vital for improved real-time insights into care delivery. This study evaluates the feasibility of using large language...
RescueGPT: An Automated System for Detecting Adverse Safety Events in Prehospital Emergency Medical Service Notes With a Zero-Shot Approach With Large Language Models: A Proof-of-Concept Study [0.03%]
基于零样本方法的大语义模型检测院前急救医疗记录中不良安全事件的自动系统：概念验证研究
Tina Yi Jin Hsieh,Carl Eriksson,Garth Meckler et al.
Tina Yi Jin Hsieh et al.
Introduction and objective: Traditional adverse safety events (ASE) identification relies on domain experts to manually review and annotate charts, which hinders the scalability of processing high-volume EMS data. This st...
Opportunities and Challenges in Using National EHR Networks for AI in Learning Health Systems [0.03%]
国家EHR网络在学习健康系统中用于AI的机会与挑战
Polina V Kukhareva,Ramkiran Gouripeddi,Niels Peek et al.
Polina V Kukhareva et al.
Background: National electronic health record (EHR) networks can support learning health systems (LHSs) by enabling large-scale data aggregation, monitoring, and benchmarking, but their capacity to produce trustworthy and...
Identification of Patients for a Community Health Worker Program Using an Artificial Intelligence Algorithm [0.03%]
基于人工智能算法识别社区卫生工作者项目的服务对象
Samuel T Savitz,Brendan Broderick,Margaret M Paul et al.
Samuel T Savitz et al.
Introduction: Community health workers (CHWs) help patients navigate community resources. CHW programs can improve health outcomes and reduce healthcare utilization, but identifying eligible patients is challenging. We de...
Sarah M Greene,Lucy A Savitz
Sarah M Greene