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2
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本文引用的文献

1
A Decade of Experience in Creating and Maintaining Data Elements for Structured Clinical Documentation in EHRs.电子健康记录中结构化临床文档数据元素创建与维护十年经验
AMIA Annu Symp Proc. 2017 Feb 10;2016:1293-1302. eCollection 2016.
2
Semantic Role Labeling of Clinical Text: Comparing Syntactic Parsers and Features.临床文本的语义角色标注:句法分析器与特征比较
AMIA Annu Symp Proc. 2017 Feb 10;2016:1283-1292. eCollection 2016.
3
Moderating the Influence of Current Intention to Improve Suicide Risk Prediction.调节当前意图对改善自杀风险预测的影响。
AMIA Annu Symp Proc. 2017 Feb 10;2016:1274-1282. eCollection 2016.
4
Checklist as a Memory Externalization Tool during a Critical Care Process.在重症护理过程中作为记忆外化工具的检查表。
AMIA Annu Symp Proc. 2017 Feb 10;2016:1080-1089. eCollection 2016.
5
Bayesian Machine Learning Techniques for revealing complex interactions among genetic and clinical factors in association with extra-intestinal Manifestations in IBD patients.贝叶斯机器学习技术用于揭示IBD患者肠道外表现相关的遗传和临床因素之间的复杂相互作用。
AMIA Annu Symp Proc. 2017 Feb 10;2016:884-893. eCollection 2016.
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A First Step towards a Clinical Decision Support System for Post-traumatic Stress Disorders.迈向创伤后应激障碍临床决策支持系统的第一步。
AMIA Annu Symp Proc. 2017 Feb 10;2016:837-843. eCollection 2016.
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A Compartmental Model for Zika Virus with Dynamic Human and Vector Populations.一种具有动态人类和病媒种群的寨卡病毒房室模型。
AMIA Annu Symp Proc. 2017 Feb 10;2016:743-752. eCollection 2016.
8
Using Best Practices to Extract, Organize, and Reuse Embedded Decision Support Content Knowledge Rules from Mature Clinical Systems.运用最佳实践从成熟临床系统中提取、组织和重用嵌入式决策支持内容知识规则。
AMIA Annu Symp Proc. 2017 Feb 10;2016:504-513. eCollection 2016.
9
A K-Reversible Approach to Model Clinical Trajectories.一种用于模拟临床轨迹的K可逆方法。
AMIA Annu Symp Proc. 2017 Feb 10;2016:460-469. eCollection 2016.
10
ARIA 2016: Care pathways implementing emerging technologies for predictive medicine in rhinitis and asthma across the life cycle.《变应性鼻炎及其对哮喘的影响:2016年更新版》:在整个生命周期中,针对鼻炎和哮喘的预测医学实施新兴技术的护理路径。
Clin Transl Allergy. 2016 Dec 30;6:47. doi: 10.1186/s13601-016-0137-4. eCollection 2016.

临床决策支持的进展:2015 - 2016年实践与文献综述要点

Advances in Clinical Decision Support: Highlights of Practice and the Literature 2015-2016.

作者信息

Jenders R A

出版信息

Yearb Med Inform. 2017 Aug;26(1):125-132. doi: 10.15265/IY-2017-012. Epub 2017 Sep 11.

DOI:10.15265/IY-2017-012
PMID:29063552
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6239223/
Abstract

Advances in clinical decision support (CDS) continue to evolve to support the goals of clinicians, policymakers, patients and professional organizations to improve clinical practice, patient safety, and the quality of care. Identify key thematic areas or foci in research and practice involving clinical decision support during the 2015-2016 time period. Thematic analysis consistent with a grounded theory approach was applied in a targeted review of journal publications, the proceedings of key scientific conferences as well as activities in standards development organizations in order to identify the key themes underlying work related to CDS. Ten key thematic areas were identified, including: 1) an emphasis on knowledge representation, with a focus on clinical practice guidelines; 2) various aspects of precision medicine, including the use of sensor and genomic data as well as big data; 3) efforts in quality improvement; 4) innovative uses of computer-based provider order entry (CPOE) systems, including relevant data displays; 5) expansion of CDS in various clinical settings; 6) patient-directed CDS; 7) understanding the potential negative impact of CDS; 8) obtaining structured data to drive CDS interventions; 9) the use of diagnostic decision support; and 10) the development and use of standards for CDS. Active research and practice in 2015-2016 continue to underscore the importance and broad utility of CDS for effecting change and improving the quality and outcome of clinical care.

摘要

临床决策支持(CDS)的进展不断演进,以支持临床医生、政策制定者、患者和专业组织改善临床实践、患者安全及医疗质量的目标。确定2015 - 2016年期间涉及临床决策支持的研究与实践中的关键主题领域或重点。采用与扎根理论方法一致的主题分析,对期刊出版物、关键科学会议的会议记录以及标准制定组织的活动进行了有针对性的审查,以确定与CDS相关工作的关键主题。确定了十个关键主题领域,包括:1)强调知识表示,重点是临床实践指南;2)精准医学的各个方面,包括传感器和基因组数据以及大数据的使用;3)质量改进方面的努力;4)基于计算机的医嘱录入(CPOE)系统的创新应用,包括相关数据显示;5)CDS在各种临床环境中的扩展;6)以患者为导向的CDS;7)了解CDS的潜在负面影响;8)获取结构化数据以推动CDS干预;9)诊断决策支持的使用;以及10)CDS标准的制定和使用。2015 - 2016年的积极研究和实践继续强调CDS对于实现变革以及改善临床护理质量和结果的重要性和广泛效用。