"It's the future, come on!": a think aloud study exploring clinicians' use of knowledge-based AI decision support [0.03%]
"未来已来，快加入吧！"：一项关于临床医师使用基于知识的人工智能决策支持的思考 aloud研究
Adele Hill,Dylan Morrissey,William Marsh
Adele Hill
Purpose: AI-based clinical decision support (CDS) is hailed as the solution to many healthcare capacity problems. However, there is a known implementation gap in AI CDS. Studies exploring barriers and enablers rely on abs...
Artificial intelligence readiness among healthcare students in Nigeria: A cross-sectional study assessing knowledge gaps, exposure, and adoption willingness [0.03%]
尼日利亚医疗专业学生的人工智能就绪情况：一项关于知识差距、接触程度和采纳意愿的横断面研究
Aanuoluwapo Clement David-Olawade,Ojima Z Wada,Yinka Julianah Adeniji et al.
Aanuoluwapo Clement David-Olawade et al.
Background: Artificial intelligence (AI) is rapidly transforming healthcare globally, yet its adoption in developing countries remains limited. As future practitioners, the readiness of healthcare students is crucial for ...
Privacy-by-design: Case studies in interactive record linkage using a hybrid human-computer system [0.03%]
以互动记录链接为目标的隐秘设计：使用混合人机系统的案例研究
Hye-Chung Kum,Eric Ragan,Mahin Ramezani et al.
Hye-Chung Kum et al.
Objective: High-quality patient matching from several sources without a common identifier (ID) requires interactive record linkage (RL) using a hybrid human-computer system. MiNDFIRL (MInimum Necessary Disclosure For Inte...
Yoshiyasu Ito,Kohei Kajiwara,Jun Kako et al.
Yoshiyasu Ito et al.
Objectives: This study aimed to gain insights into the potential of using evidence from artificial intelligence (AI) in nursing support for clinical practice and decision-making and its implications for future studies. ...
A real-time web-based telemedicine framework based on AI and IoMT for emergency triage and initial diagnostics: the TeleMedQuick solution [0.03%]
一种基于AI和IoMT的实时网络远程医疗框架，用于急诊分诊和初步诊断：TeleMedQuick解决方案
Sura Saad Mohsin,Omar H Salman,Abdulrahman Ahmed Jasim et al.
Sura Saad Mohsin et al.
Background: Rapid medical decision-making for emergency and chronic conditions remains a global challenge, especially in under-resourced and remote settings. Traditional triage models often rely on narrowly focused algori...
Development of a digital health competency scale for public health nurses [0.03%]
公共卫生护士数字健康胜任力量表的编制
Eiki Akatsuka,Atsuko Taguchi,Shoko Miyagawa et al.
Eiki Akatsuka et al.
Objectives: This study developed and psychometrically validated a new instrument - the Digital Health Competency Scale for Public Health Nurses (hereafter, DHCP) - to assess digital health competencies among public health...
The role of artificial intelligence in blood-borne virus opt-out testing in emergency departments [0.03%]
人工智能在急诊科血液传播病毒(opt-out)检测中的作用
Adebayo Da&#x;Costa,Jennifer Teke,Joseph E Origbo et al.
Adebayo Da&#x;Costa et al.
Introduction: Blood-borne viruses (BBVs) such as HIV, hepatitis B, and hepatitis C continue to pose serious public health concerns, particularly within emergency departments (EDs), where patient volume and turnover are hi...
Machine learning approaches to predicting medication nonadherence: a scoping review [0.03%]
一种预测用药不当的机器学习方法：综述研究
Christian Rhudy,Jacob Johnson,Courtney Perry et al.
Christian Rhudy et al.
Background: Medication nonadherence is a common, preventable cause of adverse clinical outcomes. Predictive models identifying risk of nonadherence could enable proactive intervention. ...
New generative artificial intelligence model: ScholarGPT's performance on dental avulsion [0.03%]
新的生成式人工智能模型：ScholarGPT在牙齿脱位的表现
Taibe Tokgöz Kaplan
Taibe Tokgöz Kaplan
Background: This study aims to evaluate the performance of ScholarGPT, a Large Language Model (LLM) developed for academic purposes, on questions related to dental avulsion. In addition, to analyze and compare it with the...
One Size Fits None. How can we do better? using patient reported experience measure findings to drive local quality improvement across wards in a large Australian metropolitan hospital [0.03%]
因人而异。我们能做得更好吗？利用患者报告的经验测量结果在澳大利亚大型医院的各个科室之间推动本地质量改进
Teyl Engstrom,Christine Petrie,William Pinzon Perez et al.
Teyl Engstrom et al.
Introduction: Patient reported experience measures (PREMs) are being collected across entire jurisdictions, resulting in large volumes of rich qualitative patient feedback. However, this collection of data is often not co...