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, 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","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, 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\",\"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. 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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.