I had not time to make it shorter: an exploratory analysis of how physicians reduce note length and time in notes [0.03%]
我没有时间再把它写短一点了：医师在病历中减少字数和时间的探索性分析
Nate C Apathy,Allison J Hare,Sarah Fendrich et al.
Nate C Apathy et al.
Objective: We analyze observed reductions in physician note length and documentation time, 2 contributors to electronic health record (EHR) burden and burnout. ...
Training digital natives to transform healthcare: a 5-tiered approach for integrating clinical informatics into undergraduate medical education [0.03%]
数字化时代培养医学人才：将临床信息学融入本科医学生教学的五级方法论
Allison J Hare,Jacqueline M Soegaard Ballester,Peter E Gabriel et al.
Allison J Hare et al.
Expansive growth in the use of health information technology (HIT) has dramatically altered medicine without translating to fully realized improvements in healthcare delivery. Bridging this divide will require healthcare professionals with ...
Assessing the carbon footprint of digital health interventions: a scoping review [0.03%]
数字健康干预措施的碳足迹评估：系统评价
Zerina Lokmic-Tomkins,Shauna Davies,Lorraine J Block et al.
Zerina Lokmic-Tomkins et al.
Objective: Integration of environmentally sustainable digital health interventions requires robust evaluation of their carbon emission life-cycle before implementation in healthcare. This scoping review surveys the eviden...
Ameen Eetemadi,Ilias Tagkopoulos
Ameen Eetemadi
Objective: A hallmark of personalized medicine and nutrition is to identify effective treatment plans at the individual level. Lifestyle interventions (LIs), from diet to exercise, can have a significant effect over time,...
Multisite evaluation of prediction models for emergency department crowding before and during the COVID-19 pandemic [0.03%]
新冠疫情前与疫情期间多中心急诊拥挤预测模型的评估
Ari J Smith,Brian W Patterson,Michael S Pulia et al.
Ari J Smith et al.
Objective: To develop a machine learning framework to forecast emergency department (ED) crowding and to evaluate model performance under spatial and temporal data drift. ...
A framework for a consistent and reproducible evaluation of manual review for patient matching algorithms [0.03%]
患者匹配算法的手动审查的一致性和可重复性评估框架
Agrayan K Gupta,Suranga N Kasthurirathne,Huiping Xu et al.
Agrayan K Gupta et al.
Healthcare systems are hampered by incomplete and fragmented patient health records. Record linkage is widely accepted as a solution to improve the quality and completeness of patient records. However, there does not exist a systematic appr...
New onset delirium prediction using machine learning and long short-term memory (LSTM) in electronic health record [0.03%]
基于电子健康记录的机器学习和长短期记忆(LSTM)的新发谵妄预测
Siru Liu,Joseph J Schlesinger,Allison B McCoy et al.
Siru Liu et al.
Objective: To develop and test an accurate deep learning model for predicting new onset delirium in hospitalized adult patients. Methods: ...
Team is brain: leveraging EHR audit log data for new insights into acute care processes [0.03%]
利用电子健康记录审计日志数据对急性护理过程进行新的洞察
Christian Rose,Robert Thombley,Morteza Noshad et al.
Christian Rose et al.
Objective: To determine whether novel measures of contextual factors from multi-site electronic health record (EHR) audit log data can explain variation in clinical process outcomes. ...
Will clinical standards not be part of the choir? Harmonization between the HL7 gender harmony project model and the NASEM measuring sex, gender identity, and sexual orientation report in the United States [0.03%]
临床标准不会成为唱诗班的一员吧？HL7性别和谐项目模型与美国国家科学院工程学和医学研究院关于测量性別、性别认同和性取向的报告之间的协调问题
Kellan E Baker,D&#x;Lane Compton,Ethan D Fechter-Leggett et al.
Kellan E Baker et al.
Objectives: To propose an approach for semantic and functional data harmonization related to sex and gender constructs in electronic health records (EHRs) and other clinical systems for implementors, as outlined in the Na...
An analysis of the effects of limited training data in distributed learning scenarios for brain age prediction [0.03%]
分布式学习场景下训练数据不足对脑年龄预测的影响分析
Raissa Souza,Pauline Mouches,Matthias Wilms et al.
Raissa Souza et al.
Objective: Distributed learning avoids problems associated with central data collection by training models locally at each site. This can be achieved by federated learning (FL) aggregating multiple models that were traine...