ArticleHeliyon2023
Implementing an electronic public health record for policy planning in the UK military sector: Validation of a secure hashing algorithm.
Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Developing a nationwide registry of UK veterans seeking help from sector charities-a machine learning approach to stratification.European journal of public health · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.