Novel linkage approach to join community-acquired and national data

Claire Tochel*, Emma Pead, Alice McTrusty, Fiona Buckmaster, Tom MacGillvray, Andrew Tatham, Niall Strang, Baljean Dhillon, Miguel Bernabeu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

Background
Community optometrists in Scotland have performed regular free-at-point-of-care eye examinations for all, for over 15 years. Eye examinations include retinal imaging but image storage is fragmented and they are not used for research. The Scottish Collaborative Optometry-Ophthalmology Network e-research project aimed to collect these images and create a repository linked to routinely collected healthcare data, supporting the development of pre-symptomatic diagnostic tools.

Methods
As the image record was usually separate from the patient record and contained minimal patient information, we developed an efficient matching algorithm using a combination of deterministic and probabilistic steps which minimised the risk of false positives, to facilitate national health record linkage. We visited two practices and assessed the data contained in their image device and Practice Management Systems. Practice activities were explored to understand the context of data collection processes. Iteratively, we tested a series of matching rules which captured a high proportion of true positive records compared to manual matches. The approach was validated by testing manual matching against automated steps in three further practices.

Results
A sequence of deterministic rules successfully matched 95% of records in the three test practices compared to manual matching. Adding two probabilistic rules to the algorithm successfully matched 99% of records.

Conclusions
The potential value of community-acquired retinal images can be harnessed only if they are linked to centrally-held healthcare care data. Despite the lack of interoperability between systems within optometry practices and inconsistent use of unique identifiers, data linkage is possible using robust, almost entirely automated processes.
Original languageEnglish
Article number13
Number of pages8
JournalBMC Medical Research Methodology
Volume24
DOIs
Publication statusPublished - 17 Jan 2024

Keywords

  • Community optometry
  • Data linkage
  • Early disease detection
  • longitudinal data
  • image analysis
  • Image analysis
  • Longitudinal data

ASJC Scopus subject areas

  • Health Informatics
  • Epidemiology

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