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创建一个纳入健康环境决定因素的学习型健康系统:GroundsWell项目经验。

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

作者信息

Rodgers Sarah E, Geary Rebecca S, Villegas-Diaz Roberto, Buchan Iain E, Burnett Hannah, Clemens Tom, Crook Rebecca, Duckworth Helen, Green Mark Alan, King Elly, Zhang Wenjing, Butters Oliver

机构信息

Public Health, Policy & Systems University of Liverpool Liverpool UK.

School of Geosciences, Institute of Geography University of Edinburgh Edinburgh UK.

出版信息

Learn Health Syst. 2024 Oct 10;8(4):e10461. doi: 10.1002/lrh2.10461. eCollection 2024 Oct.

Abstract

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.

摘要

引言

旨在预防疾病和减少健康不平等的政策需要考虑卫生系统的全面复杂性,包括环境决定因素。一个纳入环境因素的学习型卫生系统需要在个人和小区域层面实现医疗保健、社会护理和非健康数据的关联。我们的目标是为英格兰建立保护隐私的家庭记录关联,以确保个人层面的数据在与家庭或更广泛环境的数据关联时保持安全和私密。

方法

与我们地区卫生委员会的参与者、地区数据处理机构以及国家数据提供者举办了一次利益相关者研讨会。该研讨会讨论了家庭关联的风险和益处。然后,这个小组共同设计了国家和地方数据控制者与处理者之间可行的数据流。

结果

确定了一个流程,即利用包含英格兰国家医疗服务体系(NHS)所有患者地址的个人人口统计服务,在患者被记录为居住在该地址期间,将患者与家庭标识符进行匹配。与英格兰NHS的讨论促成了安全且质量有保证的数据关联,以及将这些假名化数据进一步流转至地区卫生委员会的计划。建立了相关方法,包括匹配算法的生成、传输流程和信息治理审批。我们的合作加速了一个新的数据治理应用程序的开发,有助于未来的公共卫生干预评估。

结论

这些活动建立了一种保护英格兰NHS患者隐私的安全方法,同时允许更广泛环境数据的关联。这使地方卫生系统能够从其数据中学习,并通过优化非健康因素来改善健康状况。在英格兰,对健康数据和相关非健康数据进行适度治理,以便将关键环境因素纳入学习型卫生系统是可行的。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff5f/11493545/1aa51f32f52d/LRH2-8-e10461-g001.jpg

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