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A pipeline for harmonising NHS Scotland laboratory data to enable national-level analyses
Chuang Gao1, Shahzad Mumtaz2, Sophie McCall3
1Health Informatics Centre, School of Medicine, University of Dundee, UK; Population Health and Genomics, School of Medicine, University of Dundee, UK.
Journal of Biomedical Informatics
|January 4, 2025
Summary
Researchers can now access harmonized laboratory data across Scotland. An open-source software pipeline addresses data heterogeneity, enabling efficient, national-level health research using electronic health records.
Area of Science:
- Health Informatics
- Bioinformatics
- Public Health
Background:
- Electronic health records (EHRs) are crucial for data-driven health research.
- Medical laboratory data in Scotland exhibits significant heterogeneity across local health boards.
- Existing national datasets (hospitalization, prescribing, death registers) lack standardized laboratory data.
Purpose of the Study:
- To develop an open-source software pipeline for harmonizing regional laboratory data in Scotland.
- To create a unified, national-level laboratory dataset for research purposes.
- To reduce the effort and time required for researchers working with diverse regional laboratory datasets.
Main Methods:
- Obtained sample laboratory data from four regional Safe Havens in Scotland.
- Compared variables and mapped them to 11 FHIR and 2 Scottish-specific standardized terms.
- Developed data transformations to standardize units and used Read Codes and SNOMED CT for test encoding.
Main Results:
- Identified 180 key test codes representing 98.7% of all laboratory tests performed in Scotland.
- Successfully developed transformations to standardize test result units.
- The pipeline effectively encoded tests using both Read Codes and SNOMED CT.
Conclusions:
- Validated a harmonisation pipeline for Scottish laboratory data.
- The pipeline facilitates the generation of clean, harmonized laboratory data at a national level.
- This open-source solution can be readily adopted by researchers and Safe Havens to improve data accessibility and usability.

