International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Synthetic Data Generation for QA in Healthcare Analytics Pipelines

Author(s) Narasimha Chaitanya Samineni
Country United States
Abstract Healthcare analytics pipelines depend on high-quality, privacy-preserving datasets for testing, validation, and continuous integration. However, real patient data cannot always be used due to strict privacy regulations, limited PHI access, and operational risks associated with test failures. Synthetic data generation (SDG) has emerged as a critical capability for enabling quality assurance (QA) across healthcare ETL/ELT workflows, clinical reporting systems, machine learning models, and operational dashboards. This study presents a comprehensive framework for using synthetic data in QA environments, including methodological foundations, generative modeling techniques, architecture design, privacy considerations, and evaluation metrics. The research integrates statistical simulation, rule-based generation, generative models, and domain-constrained synthesis to produce clinically plausible, statistically representative, and privacy-safe datasets. We additionally propose a multilayer SDG pipeline architecture tailored to healthcare analytics workflows and provide metrics to evaluate utility, privacy, and domain validity in QA settings. The study concludes with limitations and future directions, including synthetic digital twins and LLM-driven structured-clinical generation. [1][2][3][5][8]
Keywords Synthetic Data, Healthcare Analytics, QA Automation, Generative Models, Privacy-Preserving Simulation, ETL Testing, Statistical Disclosure Control.
Field Engineering
Published In Volume 5, Issue 3, May-June 2023
Published On 2023-06-03
DOI https://doi.org/10.36948/ijfmr.2023.v05i03.66457
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E-ISSN 2582-2160
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