应用“大数据”和商业智能洞察改善癌症临床护理

John Lewis, S. Liaw, P. Ray
{"title":"应用“大数据”和商业智能洞察改善癌症临床护理","authors":"John Lewis, S. Liaw, P. Ray","doi":"10.1109/ISTAS.2015.7439399","DOIUrl":null,"url":null,"abstract":"The current business intelligence capability of health information systems used in Australian health systems does not provide clinicians with sufficient actionable insights relevant to their own clinical settings. This paper explores ways to improve clinical outcomes by linking different multiple datasets held in a diverse set of clinical, research and other repositories in many different organisations. This research aims to realise this potential by identifying the most effective ways to utilise the growing amount of data generated by cancer care information systems through improved data linkage and application of big data and emerging business intelligence applications. Given the growing number and sophistication of big data in health initiatives and the insights that current big data researchers are producing, the issue of how well they are taken up and applied by clinicians becomes more important. For that reason, this paper aims to realise this potential by researching the most effective ways for clinicians to utilise the growing amount of data generated by cancer care information systems.","PeriodicalId":357217,"journal":{"name":"2015 IEEE International Symposium on Technology and Society (ISTAS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Applying \\\"big data\\\" and business intelligence insights to improving clinical care for cancer\",\"authors\":\"John Lewis, S. Liaw, P. Ray\",\"doi\":\"10.1109/ISTAS.2015.7439399\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The current business intelligence capability of health information systems used in Australian health systems does not provide clinicians with sufficient actionable insights relevant to their own clinical settings. This paper explores ways to improve clinical outcomes by linking different multiple datasets held in a diverse set of clinical, research and other repositories in many different organisations. This research aims to realise this potential by identifying the most effective ways to utilise the growing amount of data generated by cancer care information systems through improved data linkage and application of big data and emerging business intelligence applications. Given the growing number and sophistication of big data in health initiatives and the insights that current big data researchers are producing, the issue of how well they are taken up and applied by clinicians becomes more important. For that reason, this paper aims to realise this potential by researching the most effective ways for clinicians to utilise the growing amount of data generated by cancer care information systems.\",\"PeriodicalId\":357217,\"journal\":{\"name\":\"2015 IEEE International Symposium on Technology and Society (ISTAS)\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE International Symposium on Technology and Society (ISTAS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISTAS.2015.7439399\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE International Symposium on Technology and Society (ISTAS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISTAS.2015.7439399","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

摘要

目前在澳大利亚卫生系统中使用的卫生信息系统的商业智能能力不能为临床医生提供与他们自己的临床环境相关的足够的可操作的见解。本文探讨了通过连接不同的临床、研究和许多不同组织的其他存储库中持有的不同的多个数据集来改善临床结果的方法。本研究旨在通过改进数据链接、大数据应用和新兴商业智能应用,确定最有效的方法来利用癌症护理信息系统产生的日益增长的数据量,从而实现这一潜力。鉴于卫生倡议中大数据的数量和复杂性不断增加,以及当前大数据研究人员正在产生的见解,临床医生如何接受和应用这些数据的问题变得更加重要。因此,本文旨在通过研究临床医生利用癌症护理信息系统产生的不断增长的数据量的最有效方法来实现这一潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Applying "big data" and business intelligence insights to improving clinical care for cancer
The current business intelligence capability of health information systems used in Australian health systems does not provide clinicians with sufficient actionable insights relevant to their own clinical settings. This paper explores ways to improve clinical outcomes by linking different multiple datasets held in a diverse set of clinical, research and other repositories in many different organisations. This research aims to realise this potential by identifying the most effective ways to utilise the growing amount of data generated by cancer care information systems through improved data linkage and application of big data and emerging business intelligence applications. Given the growing number and sophistication of big data in health initiatives and the insights that current big data researchers are producing, the issue of how well they are taken up and applied by clinicians becomes more important. For that reason, this paper aims to realise this potential by researching the most effective ways for clinicians to utilise the growing amount of data generated by cancer care information systems.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
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