QUAIL: Quality Analyser for Interpreting Linkage

ObjectiveThe QUality Analyser for Interpreting Linkage (QUAIL) is a project that aims to provide a package of recommended researched methodologies and tools to streamline the quality assurance process of linked data. We aim for QUAIL to be as generalisable and as automated as possible. Standardised, yet suitable for a variety of linkage contexts. MethodsWe will highlight key processes and considerations made in the development of QUAIL and its sub-modules. These include candidate link creation for the purpose of false positive and false negative estimation, stratification, sampling, clerical review, and the computation of key quality metrics, including precision and recall. Results and ConclusionResults will focus on our recommendations based on our evaluation of each of these modules separately, and in conjunction. We will highlight the advantages of using QUAIL to facilitate quality assurance of linked data. This includes reducing reviewer burden with easy-to-use tools, while simultaneously enhancing the accuracy, reliability, efficiency and reproducibility of quality reporting.

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Publication Details

Journal
International Journal for Population Data Science
Published
2025-08-28
DOI
https://doi.org/10.23889/ijpds.v10i4.3116
Primary Topic
Interpreting and Communication in Healthcare
Type
article
Field-Weighted Citation Impact
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article

QUAIL: Quality Analyser for Interpreting Linkage

Laszlo Antal, Leah Maizey, Tim Gammon, Matt Wray et al.
International Journal for Population Data Science
Interpreting and Communication in Healthcare
article

QUAIL: Quality Analyser for Interpreting Linkage

Laszlo Antal, Leah Maizey, Tim Gammon, Matt Wray, G. R. Thomson, Jack Linaker
article en

Abstract

ObjectiveThe QUality Analyser for Interpreting Linkage (QUAIL) is a project that aims to provide a package of recommended researched methodologies and tools to streamline the quality assurance process of linked data. We aim for QUAIL to be as generalisable and as automated as possible. Standardised, yet suitable for a variety of linkage contexts. MethodsWe will highlight key processes and considerations made in the development of QUAIL and its sub-modules. These include candidate link creation for the purpose of false positive and false negative estimation, stratification, sampling, clerical review, and the computation of key quality metrics, including precision and recall. Results and ConclusionResults will focus on our recommendations based on our evaluation of each of these modules separately, and in conjunction. We will highlight the advantages of using QUAIL to facilitate quality assurance of linked data. This includes reducing reviewer burden with easy-to-use tools, while simultaneously enhancing the accuracy, reliability, efficiency and reproducibility of quality reporting.

International Journal for Population Data ScienceVol. 10(4)
Office for National Statistics (GB)
Openalex Percentile: Top 64%
Interpreting and Communication in Healthcare
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QUAIL: Quality Analyser for Interpreting Linkage — Laszlo Antal, Leah Maizey, et al. · International Journal for Population Data Science (2025) | TGRS Research Map | TGRS