Augmenting maternal clinical cohort data with administrative laboratory dataset linkages: a validation study.

Abstract

Background: The use of big data and large language models in healthcare can play a key role in improving patient treatment and healthcare management, especially when applied to large-scale administrative data. A major challenge to achieving this is ensuring that patient confidentiality and personal information is protected. One way to overcome this is by augmenting clinical data with administrative laboratory dataset linkages in order to avoid the use of demographic information.

Methods: We explored an alternative method to examine patient files from a large administrative dataset in South Africa (the National Health Laboratory Services, or NHLS), by linking external data to the NHLS database using specimen barcodes associated with laboratory tests. This provides a deterministic way of performing data linkages without accessing demographic information. In this paper, we quantify the performance metrics of this approach.

Results: The linkage of the large NHLS data to external hospital data using specimen barcodes achieved a 95% success. Out of the 1200 records in the validation sample, 87% were exact matches and 9% were matches with typographic correction. The remaining 5% were either complete mismatches or were due to duplicates in the administrative data.

Conclusions: The high success rate indicates the reliability of using barcodes for linking data without demographic identifiers. Specimen barcodes are an effective deterministic linkage tool that enable creation of large linked datasets without compromising confidentiality.