Synthetic data within a common data model for artificial intelligence applications in maternal health: experience report in the Colombian context

Abstract

Introduction. Synthetic data in healthcare is an alternative for generating clinical records that resemble those registered in real clinical scenarios. The benefits of synthetic data are: greater volume of data, the possibility of representing specific patient populations, protection of real-data privacy, and improved data-sharing among different actors.
Objective. To formulate a synthetic data generation model for the gestational care process in Colombia and adapt it to the Observational Medical Outcomes Partnership (OMOP) common data model to facilitate its integration into artificial intelligence applications in maternal health.
Materials and methods. We conducted a case study of fully synthetic data formulation that included some of the most frequent outcomes and conditions during gestation based on a typical care process for pregnant women in Colombia. This approach was complemented by the generation of a common data model to facilitate data integration in future artificial intelligence applications or complementary systems that benefit from a standardized language, regardless of the system or form of classification.
Results. We formulated a model for the synthetic generation of clinical data –applicable to real clinical settings– that spans the entire gestational care until the perinatal period. The model included the most frequent clinical conditions and outcomes, which were diagrammed in the Synthea™ tool with their corresponding clinical probabilities of occurrence based on the reported literature or the usual practice of obstetric specialists in Colombia.
Conclusions. This study demonstrates that the generation of synthetic data applied to the gestational care process in Colombia was feasible and represents a pioneering contribution in the region.