Rachel A Katz,Daniel Z Buchman
Rachel A Katz
Why integration, not innovation, is the real-world challenge facing digital health [0.03%]
为什么整合而不是创新才是数字健康所面临的现实挑战
Amina Tariq,Sundresan Naicker,Usman Iqbal et al.
Amina Tariq et al.
Enhancing the accuracy of a multivariable prediction model to identify medical patients suitable for same day emergency care services [0.03%]
提高多变量预测模型的准确性以识别适合同日急诊服务的医疗患者
Catherine Atkin,Suzy Gallier,James Hodson et al.
Catherine Atkin et al.
Objectives: To assess the performance of the Glasgow admission prediction score (GAPS) and ambulatory score (AmbS) for identifying emergency department (ED) attendances suitable for medical same day emergency care (SDEC) ...
Clinicians' and patients' views and experiences on virtual hospital care: a systematic review of qualitative evidence [0.03%]
医务人员和患者对虚拟医院护理的看法和体验：定性证据的系统评价
Riyaas A Mohamed,Márcia Rodrigues Franco,Stephanie Mathieson et al.
Riyaas A Mohamed et al.
Objectives: To appraise and synthesise the literature on patient and clinician experiences with hospital-based virtual care services. Methods: ...
Machine learning-based prediction of shigellosis in children under five: development and internal validation of a prediction model [0.03%]
基于机器学习的儿童志贺菌病预测模型的开发和内部验证研究
Md Fuad Al Fidah,Md Ridwan Islam,Asg Faruque et al.
Md Fuad Al Fidah et al.
Objectives: Shigella remains a major cause of diarrhoea and mortality in children under five in low- and middle-income countries, where laboratory confirmation is often inaccessible and dysentery-based management lacks se...
Benchmarking large language models for de-identification of electronic health record notes [0.03%]
基准大规模语言模型用于电子健康记录去标识化
Omkar Panchal,Nai-Wen Chang,Zi-Rui Zhao et al.
Omkar Panchal et al.
Objectives: The rapid evolution of large language models (LLMs) and their growing application in clinical text processing have created an urgent need for reliable de-identification mechanisms. While LLMs show promise in i...
How well do we write for patients? Longitudinal analysis of the readability and complexity of 4.6 million ophthalmic clinical letters [0.03%]
眼科临床回函的可读性和复杂性分析——基于460万例数据分析患者医疗文书的质量水平
Ariel Yuhan Ong,Christine A Kiire,Siegfried K Wagner et al.
Ariel Yuhan Ong et al.
Objectives: Effective clinician-patient communication is a fundamental pillar of high-quality clinical care and can influence patient engagement, treatment adherence and clinical outcomes. However, the extent to which cli...
Streamlining elective surgical pathways using early digital screening with complexity grading: feasibility study of a co-designed innovation [0.03%]
基于早期数字筛查难度分级优化择期手术路径的可行性研究：一项协同设计创新
Tom Langford,Daveena Meeks,Deirdre Anderson et al.
Tom Langford et al.
Objectives: To evaluate the feasibility, usability and validity of a co-designed digital questionnaire for early complexity screening in elective surgery patients. ...
Towards a framework for implementing artificial intelligence in clinical medicine [0.03%]
迈向在临床医学中实施人工智能的框架
Arjun Mahajan,Avery H LaChance,David W Bates
Arjun Mahajan
Explainable machine learning revealing the impact of mental and physical health on arthritis [0.03%]
解释性机器学习揭示心理和身体健康对关节炎的影响
Md Atik Shams,Sumaiya Fatema,D M Hasibul Islam et al.
Md Atik Shams et al.
Objectives: To develop a robust and interpretable machine learning framework for arthritis risk prediction and to identify important risk factors associated with the disease. ...