This analysis highlights the constraints of TriNetX in querying clinical laboratory data outcomes, suggesting improvements for better data utilization.
TriNetX, a rapidly growing global network of anonymized patient data, enables clinical researchers to perform large-scale retrospective cohort studies. However, its functionality for querying laboratory data outcomes is significantly constrained, as it only provides the results of the most recent test within a specified observation period. Consequently, the platform is not optimized for analyzing laboratory data collected at multiple time points during an observation period. This paper introduces innovative, data-informed solutions to address these limitations, offering practical guidance for researchers aiming to leverage TriNetX for examining clinical laboratory data.
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Wang et al. (2025) studied this question.
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