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Implementing an electronic public health record for policy planning in the UK military sector: Validation of a secure hashing algorithm

The digitalisation of healthcare services is a major resource to inform policy-makers. However, the availability of data and the establishment of a data flow present new issues to address, such as data anonymisation, records' reliability, and data linkage. The veterans' population in the U...

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Detalles Bibliográficos
Autores principales: Tomietto, Marco, McGill, Andrew, Kiernan, Matthew D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10230209/
https://www.ncbi.nlm.nih.gov/pubmed/37265623
http://dx.doi.org/10.1016/j.heliyon.2023.e16116
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author Tomietto, Marco
McGill, Andrew
Kiernan, Matthew D.
author_facet Tomietto, Marco
McGill, Andrew
Kiernan, Matthew D.
author_sort Tomietto, Marco
collection PubMed
description The digitalisation of healthcare services is a major resource to inform policy-makers. However, the availability of data and the establishment of a data flow present new issues to address, such as data anonymisation, records' reliability, and data linkage. The veterans' population in the UK presents complex needs and many organisations provide social and healthcare support, but their databases are not linked or aggregated to provide a comprehensive overview of service planning. This study aims to test the sensitivity and specificity of a Secure Hashing Algorithm to generate a unique anonymous identifier for data linkage across different organisations in the veterans' population. A Secure Hashing Algorithm was performed by considering two input variables from two different datasets. The uniqueness of the identifier was compared against the single personal key adopted as a current standard identifier. Chi-square, sensitivity, and specificity were calculated. The results demonstrated that the unique identifier generated by the Secure Hashing Algorithm detected more unique records when compared to the current gold standard. The identifier demonstrated optimal sensitivity and specificity and it allowed an enhanced data linkage between different datasets. The adoption of a Secure Hashing Algorithm improved the uniqueness of records. Moreover, it ensured data anonymity by transforming personal information into an encrypted identifier. This approach is beneficial for big data management and for creating an aggregated system for linking different organisations and, in this way, for providing a more comprehensive overview of healthcare provision and the foundation for precision public health strategies.
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spelling pubmed-102302092023-06-01 Implementing an electronic public health record for policy planning in the UK military sector: Validation of a secure hashing algorithm Tomietto, Marco McGill, Andrew Kiernan, Matthew D. Heliyon Research Article The digitalisation of healthcare services is a major resource to inform policy-makers. However, the availability of data and the establishment of a data flow present new issues to address, such as data anonymisation, records' reliability, and data linkage. The veterans' population in the UK presents complex needs and many organisations provide social and healthcare support, but their databases are not linked or aggregated to provide a comprehensive overview of service planning. This study aims to test the sensitivity and specificity of a Secure Hashing Algorithm to generate a unique anonymous identifier for data linkage across different organisations in the veterans' population. A Secure Hashing Algorithm was performed by considering two input variables from two different datasets. The uniqueness of the identifier was compared against the single personal key adopted as a current standard identifier. Chi-square, sensitivity, and specificity were calculated. The results demonstrated that the unique identifier generated by the Secure Hashing Algorithm detected more unique records when compared to the current gold standard. The identifier demonstrated optimal sensitivity and specificity and it allowed an enhanced data linkage between different datasets. The adoption of a Secure Hashing Algorithm improved the uniqueness of records. Moreover, it ensured data anonymity by transforming personal information into an encrypted identifier. This approach is beneficial for big data management and for creating an aggregated system for linking different organisations and, in this way, for providing a more comprehensive overview of healthcare provision and the foundation for precision public health strategies. Elsevier 2023-05-19 /pmc/articles/PMC10230209/ /pubmed/37265623 http://dx.doi.org/10.1016/j.heliyon.2023.e16116 Text en © 2023 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Research Article
Tomietto, Marco
McGill, Andrew
Kiernan, Matthew D.
Implementing an electronic public health record for policy planning in the UK military sector: Validation of a secure hashing algorithm
title Implementing an electronic public health record for policy planning in the UK military sector: Validation of a secure hashing algorithm
title_full Implementing an electronic public health record for policy planning in the UK military sector: Validation of a secure hashing algorithm
title_fullStr Implementing an electronic public health record for policy planning in the UK military sector: Validation of a secure hashing algorithm
title_full_unstemmed Implementing an electronic public health record for policy planning in the UK military sector: Validation of a secure hashing algorithm
title_short Implementing an electronic public health record for policy planning in the UK military sector: Validation of a secure hashing algorithm
title_sort implementing an electronic public health record for policy planning in the uk military sector: validation of a secure hashing algorithm
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10230209/
https://www.ncbi.nlm.nih.gov/pubmed/37265623
http://dx.doi.org/10.1016/j.heliyon.2023.e16116
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