创建学习型卫生系统,纳入健康的环境决定因素:GroundsWell 的经验。

IF 2.6 Q2 HEALTH POLICY & SERVICES
Sarah E. Rodgers, Rebecca S. Geary, Roberto Villegas-Diaz, Iain E. Buchan, Hannah Burnett, Tom Clemens, Rebecca Crook, Helen Duckworth, Mark Alan Green, Elly King, Wenjing Zhang, Oliver Butters
{"title":"创建学习型卫生系统,纳入健康的环境决定因素:GroundsWell 的经验。","authors":"Sarah E. Rodgers,&nbsp;Rebecca S. Geary,&nbsp;Roberto Villegas-Diaz,&nbsp;Iain E. Buchan,&nbsp;Hannah Burnett,&nbsp;Tom Clemens,&nbsp;Rebecca Crook,&nbsp;Helen Duckworth,&nbsp;Mark Alan Green,&nbsp;Elly King,&nbsp;Wenjing Zhang,&nbsp;Oliver Butters","doi":"10.1002/lrh2.10461","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Introduction</h3>\n \n <p>Policies aiming to prevent ill health and reduce health inequalities need to consider the full complexity of health systems, including environmental determinants. A learning health system that incorporates environmental factors needs healthcare, social care and non-health data linkage at individual and small-area levels. Our objective was to establish privacy-preserving household record linkage for England to ensure person-level data remain secure and private when linked with data from households or the wider environment.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>A stakeholder workshop with participants from our regional health board, together with the regional data processor, and the national data provider. The workshop discussed the risks and benefits of household linkages. This group then co-designed actionable dataflows between national and local data controllers and processors.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>A process was defined whereby the Personal Demographics Service, which includes the addresses of all patients of the National Health Service (NHS) in England, was used to match patients to a home identifier, for the time they are recorded as living at that address. Discussions with NHS England resulted in secure and quality-assured data linkages and a plan to flow these pseudonymised data onwards into regional health boards. Methods were established, including the generation of matching algorithms, transfer processes and information governance approvals. Our collaboration accelerated the development of a new data governance application, facilitating future public health intervention evaluations.</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>These activities have established a secure method for protecting the privacy of NHS patients in England, while allowing linkage of wider environmental data. This enables local health systems to learn from their data and improve health by optimizing non-health factors. Proportionate governance of health and linked non-health data is practical in England for incorporating key environmental factors into a learning health system.</p>\n </section>\n </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 4","pages":""},"PeriodicalIF":2.6000,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11493545/pdf/","citationCount":"0","resultStr":"{\"title\":\"Creating a learning health system to include environmental determinants of health: The GroundsWell experience\",\"authors\":\"Sarah E. Rodgers,&nbsp;Rebecca S. Geary,&nbsp;Roberto Villegas-Diaz,&nbsp;Iain E. Buchan,&nbsp;Hannah Burnett,&nbsp;Tom Clemens,&nbsp;Rebecca Crook,&nbsp;Helen Duckworth,&nbsp;Mark Alan Green,&nbsp;Elly King,&nbsp;Wenjing Zhang,&nbsp;Oliver Butters\",\"doi\":\"10.1002/lrh2.10461\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Introduction</h3>\\n \\n <p>Policies aiming to prevent ill health and reduce health inequalities need to consider the full complexity of health systems, including environmental determinants. A learning health system that incorporates environmental factors needs healthcare, social care and non-health data linkage at individual and small-area levels. Our objective was to establish privacy-preserving household record linkage for England to ensure person-level data remain secure and private when linked with data from households or the wider environment.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>A stakeholder workshop with participants from our regional health board, together with the regional data processor, and the national data provider. The workshop discussed the risks and benefits of household linkages. This group then co-designed actionable dataflows between national and local data controllers and processors.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>A process was defined whereby the Personal Demographics Service, which includes the addresses of all patients of the National Health Service (NHS) in England, was used to match patients to a home identifier, for the time they are recorded as living at that address. Discussions with NHS England resulted in secure and quality-assured data linkages and a plan to flow these pseudonymised data onwards into regional health boards. Methods were established, including the generation of matching algorithms, transfer processes and information governance approvals. Our collaboration accelerated the development of a new data governance application, facilitating future public health intervention evaluations.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusion</h3>\\n \\n <p>These activities have established a secure method for protecting the privacy of NHS patients in England, while allowing linkage of wider environmental data. This enables local health systems to learn from their data and improve health by optimizing non-health factors. Proportionate governance of health and linked non-health data is practical in England for incorporating key environmental factors into a learning health system.</p>\\n </section>\\n </div>\",\"PeriodicalId\":43916,\"journal\":{\"name\":\"Learning Health Systems\",\"volume\":\"8 4\",\"pages\":\"\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2024-10-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11493545/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Learning Health Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/lrh2.10461\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"HEALTH POLICY & SERVICES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Learning Health Systems","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/lrh2.10461","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"HEALTH POLICY & SERVICES","Score":null,"Total":0}
引用次数: 0

摘要

导言:旨在预防疾病和减少健康不平等的政策需要考虑到健康系统的全部复杂性,包括环境决定因素。一个包含环境因素的学习型健康系统需要在个人和小区域层面将医疗保健、社会关怀和非健康数据联系起来。我们的目标是为英格兰建立保护隐私的家庭记录链接,以确保个人层面的数据在与来自家庭或更广泛环境的数据链接时保持安全和隐私:利益相关者研讨会,与会者来自地区卫生局、地区数据处理者和国家数据提供者。研讨会讨论了住户关联的风险和益处。该小组随后共同设计了国家和地方数据控制者与处理者之间的可操作数据流:确定了一个流程,根据该流程,个人人口统计服务(包括英格兰国家医疗服务体系(NHS)所有患者的地址)被用来将患者与家庭标识符进行匹配,以记录他们居住在该地址的时间。通过与英格兰国家医疗服务系统的讨论,建立了安全且有质量保证的数据链接,并制定了一项计划,将这些化名数据转入地区医疗委员会。我们制定了各种方法,包括生成匹配算法、传输流程和信息管理审批。我们的合作加快了新数据管理应用程序的开发,为未来的公共卫生干预评估提供了便利:这些活动为保护英格兰国家医疗服务系统(NHS)患者的隐私建立了一种安全的方法,同时允许将更广泛的环境数据联系起来。这使地方卫生系统能够从数据中学习,并通过优化非健康因素来改善健康状况。在英格兰,对健康数据和关联的非健康数据进行适度管理,将关键环境因素纳入学习型健康系统是切实可行的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Creating a learning health system to include environmental determinants of health: The GroundsWell experience

Creating a learning health system to include environmental determinants of health: The GroundsWell experience

Introduction

Policies aiming to prevent ill health and reduce health inequalities need to consider the full complexity of health systems, including environmental determinants. A learning health system that incorporates environmental factors needs healthcare, social care and non-health data linkage at individual and small-area levels. Our objective was to establish privacy-preserving household record linkage for England to ensure person-level data remain secure and private when linked with data from households or the wider environment.

Methods

A stakeholder workshop with participants from our regional health board, together with the regional data processor, and the national data provider. The workshop discussed the risks and benefits of household linkages. This group then co-designed actionable dataflows between national and local data controllers and processors.

Results

A process was defined whereby the Personal Demographics Service, which includes the addresses of all patients of the National Health Service (NHS) in England, was used to match patients to a home identifier, for the time they are recorded as living at that address. Discussions with NHS England resulted in secure and quality-assured data linkages and a plan to flow these pseudonymised data onwards into regional health boards. Methods were established, including the generation of matching algorithms, transfer processes and information governance approvals. Our collaboration accelerated the development of a new data governance application, facilitating future public health intervention evaluations.

Conclusion

These activities have established a secure method for protecting the privacy of NHS patients in England, while allowing linkage of wider environmental data. This enables local health systems to learn from their data and improve health by optimizing non-health factors. Proportionate governance of health and linked non-health data is practical in England for incorporating key environmental factors into a learning health system.

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
Learning Health Systems
Learning Health Systems HEALTH POLICY & SERVICES-
CiteScore
5.60
自引率
22.60%
发文量
55
审稿时长
20 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