开发一个相互关联的电子健康记录衍生数据平台,以支持健康老龄化研究。

IF 1.6 Q3 HEALTH CARE SCIENCES & SERVICES International Journal of Population Data Science Pub Date : 2023-01-01 DOI:10.23889/ijpds.v8i1.2129
Nadine E Andrew, Richard Beare, Tanya Ravipati, Emily Parker, David Snowdon, Kim Naude, Velandai Srikanth
{"title":"开发一个相互关联的电子健康记录衍生数据平台,以支持健康老龄化研究。","authors":"Nadine E Andrew,&nbsp;Richard Beare,&nbsp;Tanya Ravipati,&nbsp;Emily Parker,&nbsp;David Snowdon,&nbsp;Kim Naude,&nbsp;Velandai Srikanth","doi":"10.23889/ijpds.v8i1.2129","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>Digitalisation of Electronic Health Record (EHR) data has created unique opportunities for research. However, these data are routinely collected for operational purposes and so are not curated to the standard required for research. Harnessing such routine data at large scale allows efficient and long-term epidemiological and health services research.</p><p><strong>Objectives: </strong>To describe the establishment a linked EHR derived data platform in the National Centre for Healthy Ageing, Melbourne, Australia, aimed at enabling research targeting national health priority areas in ageing.</p><p><strong>Methods: </strong>Our approach incorporated: data validation, curation and warehousing to ensure quality and completeness; end-user engagement and consensus on the platform content; implementation of an artificial intelligence (AI) pipeline for extraction of text-based data items; early consumer involvement; and implementation of routine collection of patient reported outcome measures, in a multisite public health service.</p><p><strong>Results: </strong>Data for a cohort of >800,000 patients collected over a 10-year period have been curated within the platform's research data warehouse. So far 117 items have been identified as suitable for inclusion, from 11 research relevant datasets held within the health service EHR systems. Data access, extraction and release processes, guided by the Five Safes Framework, are being tested through project use-cases. A natural language processing (NLP) pipeline has been implemented and a framework for the routine collection and incorporation of patient reported outcome measures developed.</p><p><strong>Conclusions: </strong>We highlight the importance of establishing comprehensive processes for the foundations of a data platform utilising routine data not collected for research purposes. These robust foundations will facilitate future expansion through linkages to other datasets for the efficient and cost-effective study of health related to ageing at a large scale.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"8 1","pages":"2129"},"PeriodicalIF":1.6000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/63/f0/ijpds-08-2129.PMC10476553.pdf","citationCount":"1","resultStr":"{\"title\":\"Developing a linked electronic health record derived data platform to support research into healthy ageing.\",\"authors\":\"Nadine E Andrew,&nbsp;Richard Beare,&nbsp;Tanya Ravipati,&nbsp;Emily Parker,&nbsp;David Snowdon,&nbsp;Kim Naude,&nbsp;Velandai Srikanth\",\"doi\":\"10.23889/ijpds.v8i1.2129\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>Digitalisation of Electronic Health Record (EHR) data has created unique opportunities for research. However, these data are routinely collected for operational purposes and so are not curated to the standard required for research. Harnessing such routine data at large scale allows efficient and long-term epidemiological and health services research.</p><p><strong>Objectives: </strong>To describe the establishment a linked EHR derived data platform in the National Centre for Healthy Ageing, Melbourne, Australia, aimed at enabling research targeting national health priority areas in ageing.</p><p><strong>Methods: </strong>Our approach incorporated: data validation, curation and warehousing to ensure quality and completeness; end-user engagement and consensus on the platform content; implementation of an artificial intelligence (AI) pipeline for extraction of text-based data items; early consumer involvement; and implementation of routine collection of patient reported outcome measures, in a multisite public health service.</p><p><strong>Results: </strong>Data for a cohort of >800,000 patients collected over a 10-year period have been curated within the platform's research data warehouse. So far 117 items have been identified as suitable for inclusion, from 11 research relevant datasets held within the health service EHR systems. Data access, extraction and release processes, guided by the Five Safes Framework, are being tested through project use-cases. A natural language processing (NLP) pipeline has been implemented and a framework for the routine collection and incorporation of patient reported outcome measures developed.</p><p><strong>Conclusions: </strong>We highlight the importance of establishing comprehensive processes for the foundations of a data platform utilising routine data not collected for research purposes. These robust foundations will facilitate future expansion through linkages to other datasets for the efficient and cost-effective study of health related to ageing at a large scale.</p>\",\"PeriodicalId\":36483,\"journal\":{\"name\":\"International Journal of Population Data Science\",\"volume\":\"8 1\",\"pages\":\"2129\"},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/63/f0/ijpds-08-2129.PMC10476553.pdf\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Population Data Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23889/ijpds.v8i1.2129\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"HEALTH CARE SCIENCES & SERVICES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Population Data Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23889/ijpds.v8i1.2129","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
引用次数: 1

摘要

电子健康记录(EHR)数据的数字化为研究创造了独特的机会。然而,这些数据通常是为业务目的收集的,因此没有按照研究所需的标准进行整理。大规模利用这类常规数据可以进行有效和长期的流行病学和卫生服务研究。目的:描述在澳大利亚墨尔本国家健康老龄化中心建立一个关联的电子病历衍生数据平台的情况,旨在促进针对老龄问题国家卫生优先领域的研究。方法:我们的方法包括:数据验证,管理和仓储,以确保质量和完整性;终端用户对平台内容的参与度和共识;实施人工智能(AI)管道,提取基于文本的数据项;早期消费者参与;在多站点公共卫生服务中实施患者报告结果措施的常规收集。结果:在该平台的研究数据仓库中收集了10年期间收集的>80万患者的队列数据。迄今为止,已从卫生服务电子健康档案系统中保存的11个研究相关数据集中确定了117个项目适合纳入。在“五个安全框架”的指导下,数据访问、提取和发布流程正在通过项目用例进行测试。已经实施了自然语言处理(NLP)管道,并制定了常规收集和合并患者报告结果措施的框架。结论:我们强调建立综合流程的重要性,利用非为研究目的收集的常规数据作为数据平台的基础。这些坚实的基础将通过与其他数据集的联系,促进今后的扩展,以便有效和具有成本效益地大规模研究与老龄化有关的健康问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Developing a linked electronic health record derived data platform to support research into healthy ageing.

Introduction: Digitalisation of Electronic Health Record (EHR) data has created unique opportunities for research. However, these data are routinely collected for operational purposes and so are not curated to the standard required for research. Harnessing such routine data at large scale allows efficient and long-term epidemiological and health services research.

Objectives: To describe the establishment a linked EHR derived data platform in the National Centre for Healthy Ageing, Melbourne, Australia, aimed at enabling research targeting national health priority areas in ageing.

Methods: Our approach incorporated: data validation, curation and warehousing to ensure quality and completeness; end-user engagement and consensus on the platform content; implementation of an artificial intelligence (AI) pipeline for extraction of text-based data items; early consumer involvement; and implementation of routine collection of patient reported outcome measures, in a multisite public health service.

Results: Data for a cohort of >800,000 patients collected over a 10-year period have been curated within the platform's research data warehouse. So far 117 items have been identified as suitable for inclusion, from 11 research relevant datasets held within the health service EHR systems. Data access, extraction and release processes, guided by the Five Safes Framework, are being tested through project use-cases. A natural language processing (NLP) pipeline has been implemented and a framework for the routine collection and incorporation of patient reported outcome measures developed.

Conclusions: We highlight the importance of establishing comprehensive processes for the foundations of a data platform utilising routine data not collected for research purposes. These robust foundations will facilitate future expansion through linkages to other datasets for the efficient and cost-effective study of health related to ageing at a large scale.

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
CiteScore
2.50
自引率
0.00%
发文量
386
审稿时长
20 weeks
期刊最新文献
Defining a low-risk birth cohort: a cohort study comparing two perinatal data sets in Ontario, Canada. Data resource profile: nutrition data in the VA million veteran program. Deprivation effects on length of stay and death of hospitalised COVID-19 patients in Greater Manchester. Variation in colorectal cancer treatment and outcomes in Scotland: real world evidence from national linked administrative health data. Examining the quality and population representativeness of linked survey and administrative data: guidance and illustration using linked 1958 National Child Development Study and Hospital Episode Statistics data
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1