Beyond Authenticity: Navigating Bias, Ethics, and Methodological Frontiers of Synthetic Data in Qualitative Healthcare Research - Judijanto, Loso (Yayasan Gema Bina Nusantara, 2025)
The increasing integration of synthetic data into qualitative healthcare research offers new avenues for narrative analysis while raising questions about validity, bias, and authenticity. This study investigates how synthetic data, particularly AI-generated narratives, can be utilised in qualitative healthcare research to enhance methodological rigour and overcome data access limitations. Using a qualitative literature review approach, this research synthesises findings from diverse peer-reviewed articles, policy briefs, and empirical case studies. Data were collected through thematic extraction from existing literature and critically analysed using interpretative thematic synthesis. The results indicate that synthetic data can replicate core thematic structures commonly found in authentic patient interviews, including experiences of chronic illness, emotional distress, and healthcare navigation. However, challenges remain in addressing embedded algorithmic bias, particularly regarding cultural specificity, linguistic diversity, and representation of marginalised populations. Innovations such as reflexive prompt engineering, synthetic member-checking, and hybrid validation techniques have shown promise in mitigating these biases and improving narrative fidelity. The study concludes that when applied ethically and methodologically, synthetic data can serve as a valuable supplement to traditional qualitative methods in healthcare research. This integration can enhance thematic saturation, increase data accessibility, and offer new perspectives in digital ethnography. Future research should focus on developing standardized evaluation criteria, participatory prompt design, and long-term assessment of synthetic data applications in diverse healthcare settings.