The eICU research institute - a collaboration between industry, health-care providers, and academia.

Michael McShea, Randy Holl, Omar Badawi, Richard R Riker, Eric Silfen
{"title":"The eICU research institute - a collaboration between industry, health-care providers, and academia.","authors":"Michael McShea,&nbsp;Randy Holl,&nbsp;Omar Badawi,&nbsp;Richard R Riker,&nbsp;Eric Silfen","doi":"10.1109/MEMB.2009.935720","DOIUrl":null,"url":null,"abstract":"<p><p>As the volume of data that is electronically available promliferates, the health-care industry is identifying better ways to use this data for patient care. Ideally, these data are collected in real time, can support point-of-care clinical decisions, and, by providing instantaneous quality metrics, can create the opportunities to improve clinical practice as the patient is being cared for. The business-world technology supporting these activities is referred to as business intelligence, which offers competitive advantage, increased quality, and operational efficiencies. The health-care industry is plagued by many challenges that have made it a latecomer to business intelligence and data-mining technology, including delayed adoption of electronic medical records, poor integration between information systems, a lack of uniform technical standards, poor interoperability between complex devices, and the mandate to rigorously protect patient privacy. Efforts at developing a health care equivalent of business intelligence (which we will refer to as clinical intelligence) remains in its infancy. Until basic technology infrastructure and mature clinical applications are developed and implemented throughout the health-care system, data aggregation and interpretation cannot effectively progress. The need for this approach in health care is undisputed. As regional and national health information networks emerge, we need to develop cost-effective systems that reduce time and effort spent documenting health-care data while increasing the application of knowledge derived from that data.</p>","PeriodicalId":50391,"journal":{"name":"IEEE Engineering in Medicine and Biology Magazine","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2010-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/MEMB.2009.935720","citationCount":"58","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Engineering in Medicine and Biology Magazine","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MEMB.2009.935720","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 58

Abstract

As the volume of data that is electronically available promliferates, the health-care industry is identifying better ways to use this data for patient care. Ideally, these data are collected in real time, can support point-of-care clinical decisions, and, by providing instantaneous quality metrics, can create the opportunities to improve clinical practice as the patient is being cared for. The business-world technology supporting these activities is referred to as business intelligence, which offers competitive advantage, increased quality, and operational efficiencies. The health-care industry is plagued by many challenges that have made it a latecomer to business intelligence and data-mining technology, including delayed adoption of electronic medical records, poor integration between information systems, a lack of uniform technical standards, poor interoperability between complex devices, and the mandate to rigorously protect patient privacy. Efforts at developing a health care equivalent of business intelligence (which we will refer to as clinical intelligence) remains in its infancy. Until basic technology infrastructure and mature clinical applications are developed and implemented throughout the health-care system, data aggregation and interpretation cannot effectively progress. The need for this approach in health care is undisputed. As regional and national health information networks emerge, we need to develop cost-effective systems that reduce time and effort spent documenting health-care data while increasing the application of knowledge derived from that data.

eICU研究所——工业界、卫生保健提供者和学术界之间的合作。
随着电子数据量的激增,医疗保健行业正在寻找更好的方法来使用这些数据进行患者护理。理想情况下,这些数据是实时收集的,可以支持护理点临床决策,并且通过提供即时质量指标,可以在患者接受护理时创造改善临床实践的机会。支持这些活动的商业世界技术被称为商业智能,它提供了竞争优势、更高的质量和操作效率。医疗保健行业受到许多挑战的困扰,这些挑战使其成为商业智能和数据挖掘技术的后来者,包括电子医疗记录的延迟采用、信息系统之间的集成不良、缺乏统一的技术标准、复杂设备之间的互操作性差以及严格保护患者隐私的任务。开发医疗保健领域的商业智能(我们将其称为临床智能)的努力仍处于起步阶段。在整个卫生保健系统开发和实施基本技术基础设施和成熟的临床应用之前,数据收集和解释不能有效地取得进展。在卫生保健领域采用这种方法的必要性是无可争议的。随着区域和国家卫生信息网络的出现,我们需要开发具有成本效益的系统,减少记录卫生保健数据所花费的时间和精力,同时增加从这些数据中获得的知识的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
IEEE Engineering in Medicine and Biology Magazine
IEEE Engineering in Medicine and Biology Magazine 工程技术-工程:生物医学
自引率
0.00%
发文量
1
审稿时长
>12 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信