Curator – a data curation tool for clinical real-world evidence

Journal article

Objective
This research aims to establish an efficient, systematic, reproducible, and transparent solution for advanced curation of real-world data, which are highly complex and represent an invaluable source of information for academia and industry.

Materials and methods
We propose a novel software solution that splits the statistical analytical pipeline into two phases. The first phase is implemented through Curator, which performs data engineering and data modelling on deidentified real-world data to achieve advanced curation and provides selected information ready to be analyzed in the second phase by statistical packages. Curator is made of a suite of Python programs and uses MySQL as its database management system. Curator has been utilised with several UK primary and secondary care data sources.

Results
Curator has been used in 25 completed clinical and health economics research studies. Their output has been published in 2 NIHR-funded reports and 33 prestigious international peer-reviewed journals and presented at 38 global conferences. Curator has consistently reduced research time and costs by over 36% and made research more reproducible and transparent.

Discussion
Curator fits in well with recent UK governmental guidelines that recognise health data curation as a complex standalone technical challenge. Curator has been used extensively on UK real-world data and can handle several linked datasets. However, for Curator to be accessed by a wider audience, it needs to become more user-friendly.

Conclusion
Curator has proven to be a cost-effective and trustworthy data curation tool, which should be developed further and made available to third parties.

Authors: Delmestri, A; Prieto-Alhambra, D

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Elsevier
• Journal: Informatics in Medicine Unlocked
• Volume: 40
• Article number: 101291
• Publication date: 2023-06-07
• Acceptance date: 2023-06-05
• DOI: 10.1016/j.imu.2023.101291
• EISSN: 2352-9148
• Language: English
• Keywords: data wrangling; EHR; RWD; real-world data; electronic health records; FFR