• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

放射学中的信息学:测量和提高放射学质量:用信息学迎接挑战。

Informatics in radiology: Measuring and improving quality in radiology: meeting the challenge with informatics.

机构信息

Department of Radiology, Stanford University, Richard M. Lucas Center, 1201 Welch Rd, Office P285, Stanford, CA 94305-5488, USA. dlrubin@ stanford.edu

出版信息

Radiographics. 2011 Oct;31(6):1511-27. doi: 10.1148/rg.316105207.

DOI:10.1148/rg.316105207
PMID:21997979
Abstract

Quality is becoming a critical issue for radiology. Measuring and improving quality is essential not only to ensure optimum effectiveness of care and comply with increasing regulatory requirements, but also to combat current trends leading to commoditization of radiology services. A key challenge to implementing quality improvement programs is to develop methods to collect knowledge related to quality care and to deliver that knowledge to practitioners at the point of care. There are many dimensions to quality in radiology that need to be measured, monitored, and improved, including examination appropriateness, procedure protocol, accuracy of interpretation, communication of imaging results, and measuring and monitoring performance improvement in quality, safety, and efficiency. Informatics provides the key technologies that can enable radiologists to measure and improve quality. However, few institutions recognize the opportunities that informatics methods provide to improve safety and quality. The information technology infrastructure in most hospitals is limited, and they have suboptimal adoption of informatics techniques. Institutions can tackle the challenges of assessing and improving quality in radiology by means of informatics.

摘要

质量正成为放射学领域的一个关键问题。衡量和改进质量不仅对于确保医疗服务的最佳效果并符合日益增加的监管要求至关重要,而且对于应对当前导致放射服务商品化的趋势也至关重要。实施质量改进计划的主要挑战是开发收集与质量护理相关知识的方法,并将这些知识提供给护理点的从业者。放射学中有许多需要衡量、监测和改进的质量维度,包括检查的适当性、程序方案、解释的准确性、影像学结果的沟通以及衡量和监测质量、安全性和效率方面的绩效改进。信息学提供了可以使放射科医生衡量和改进质量的关键技术。但是,很少有机构认识到信息学方法在提高安全性和质量方面提供的机会。大多数医院的信息技术基础设施有限,并且信息学技术的采用也不理想。医疗机构可以通过信息学来应对评估和改进放射学质量的挑战。

相似文献

1
Informatics in radiology: Measuring and improving quality in radiology: meeting the challenge with informatics.放射学中的信息学:测量和提高放射学质量:用信息学迎接挑战。
Radiographics. 2011 Oct;31(6):1511-27. doi: 10.1148/rg.316105207.
2
Using informatics-enabled quality improvement techniques to meet health record documentation requirements in radiology reports.利用信息学支持的质量改进技术满足放射学报告中医嘱文档的要求。
Acad Radiol. 2013 Aug;20(8):1032-6. doi: 10.1016/j.acra.2013.04.007.
3
Rethinking radiology informatics.重新思考放射学信息学。
AJR Am J Roentgenol. 2015 Apr;204(4):716-20. doi: 10.2214/AJR.14.13840.
4
[Health policy and occupational health: tools and methods to assure quality and appropriateness of interventions].[卫生政策与职业健康:确保干预措施质量和适宜性的工具与方法]
Med Lav. 2004 Jan-Feb;95(1):3-10.
5
Protocol-directed shared care in cardiology.心脏病学中的协议导向型共享护理。
Stud Health Technol Inform. 1995;16:145-56.
6
The future quality and safety of medical imaging: proceedings of the third annual ACR FORUM.医学成像的未来质量与安全:第三届美国放射学会年会论文集
J Am Coll Radiol. 2004 Jan;1(1):33-9. doi: 10.1016/S1546-1440(03)00012-7.
7
Performance measures in radiology.放射学中的性能指标。
J Am Coll Radiol. 2014 May;11(5):456-63. doi: 10.1016/j.jacr.2013.11.019.
8
Informatics methods to enable patient-centered radiology.实现以患者为中心的放射学的信息学方法。
Acad Radiol. 2009 May;16(5):524-34. doi: 10.1016/j.acra.2009.01.009.
9
Six Sigma: not for the faint of heart.六西格玛:并非胆小者所能驾驭。
Radiol Manage. 2003 Mar-Apr;25(2):40-53.
10
Decommoditizing radiology.使放射学非商品化。
J Am Coll Radiol. 2009 Mar;6(3):167-70. doi: 10.1016/j.jacr.2008.11.004.

引用本文的文献

1
A radiomics model for predicting perineural invasion in stage II-III colon cancer based on computer tomography.基于 CT 的 II-III 期结肠癌患者发生神经周围侵犯的影像组学模型
BMC Cancer. 2024 Oct 4;24(1):1226. doi: 10.1186/s12885-024-12951-x.
2
Development of minimum data set and dashboard for monitoring adverse events in radiology departments.用于监测放射科不良事件的最小数据集和仪表板的开发。
Heliyon. 2024 Apr 24;10(9):e30054. doi: 10.1016/j.heliyon.2024.e30054. eCollection 2024 May 15.
3
A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises.
医学成像中的深度学习综述:成像特征、技术趋势、具有进展亮点的案例研究及未来展望。
Proc IEEE Inst Electr Electron Eng. 2021 May;109(5):820-838. doi: 10.1109/JPROC.2021.3054390. Epub 2021 Feb 26.
4
Collaborative Development of a PACS-Integrated Quality Control Dashboard: a Single Institutional Experience.基于 PACS 的质量控制仪表盘的协同开发:单机构经验。
J Digit Imaging. 2022 Oct;35(5):1350-1357. doi: 10.1007/s10278-022-00621-y. Epub 2022 Apr 20.
5
A holistic overview of deep learning approach in medical imaging.医学成像中深度学习方法的全面概述。
Multimed Syst. 2022;28(3):881-914. doi: 10.1007/s00530-021-00884-5. Epub 2022 Jan 21.
6
The Enterprise Imaging Value Proposition.企业成像的价值主张。
J Digit Imaging. 2020 Feb;33(1):37-48. doi: 10.1007/s10278-019-00293-1.
7
Detecting Technical Image Quality in Radiology Reports.在放射学报告中检测技术图像质量。
AMIA Annu Symp Proc. 2018 Dec 5;2018:780-788. eCollection 2018.
8
Dose estimation of ultra-low-dose chest CT to different sized adult patients.不同体型成人患者行超低剂量胸部 CT 的剂量估算。
Eur Radiol. 2019 Aug;29(8):4315-4323. doi: 10.1007/s00330-018-5849-5. Epub 2018 Dec 17.
9
Assessing information sources to elucidate diagnostic process errors in radiologic imaging - a human factors framework.评估信息来源以阐明放射影像学诊断过程中的错误 - 一个人为因素框架。
J Am Med Inform Assoc. 2018 Nov 1;25(11):1507-1515. doi: 10.1093/jamia/ocy103.
10
Using automatically extracted information from mammography reports for decision-support.利用从乳腺钼靶报告中自动提取的信息进行决策支持。
J Biomed Inform. 2016 Aug;62:224-31. doi: 10.1016/j.jbi.2016.07.001. Epub 2016 Jul 4.