Generative Adversarial Networks for Primary Healthcare Planning: A Systematic Review of Synthetic Data Generation and Budget Forecasting

Vollständiger Abstract

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Primary healthcare systems, particularly in low and middle income countries, are caused by insufficient data, constraints on resources, and shifts in the demand for services. The focus of this paper is on a sys- tematic literature review to understand the impact of Generative Adversarial Networks (GANs) in the area of budget and planning in primary healthcare systems. Peer-reviewed literature was used for the analysis, and 35 studies published in the period of 2017 to 2025 were selected and reviewed based on the inclusion criteria of relevance to the area of forecasting, simulation, and decision support in healthcare data. Some of the key variations of GANs that are discussed in the paper involve the use of Conditional GANs, Condi- tional Tabular GANs (CTGANs) and other recurrent or temporal based GANs, which are commonly used for imaging, and electronic health record and time series data in healthcare. The generated findings suggest that, in resource-limited healthcare contexts, the use of GAN- based synthetic data generation improves the flexibility and consistency of predictive models, facilitates resource planning through scenario-based simu- lation, and allows for the controlled sharing of data across collaborating institutions. However, the review findings point to several gaps that include the absence of operational use in primary healthcare budgeting, difficulties in interpretability and governance of models, and no agreed-upon criteria for evaluating the trade-off between the reliability of synthetic data and associated privacy concerns. Therefore, the review demonstrates the possibilities and constraints of GAN-based methods and delineates paths for subsequent research to foster the integration of these methods into primary healthcare budgeting processes.