Adaptive AI Pipelines in Population Health Analytics for Enhanced Security and Data Governance

Abstract

The growing complexity and volume of health data have exposed critical limitations in traditional, static data processing systems, creating an urgent need for adaptive AI pipelines that can support scalable, secure, and governance-compliant population health analytics. This study investigates how adaptive AI architectures can enhance decision-making, data security, and regulatory accountability in healthcare systems, particularly within developing country contexts. Four publicly available datasets were analyzed using complementary quantitative techniques: the Medical Expenditure Panel Survey (MEPS) with fixed-effects regression to assess the relationship between adaptive AI adoption and healthcare utilization; the Nigeria Centre for Disease Control (NCDC) weekly epidemiological reports with CUSUM anomaly detection to identify systemic vulnerabilities; the Demographic and Health Survey (DHS 2018) with logistic regression to evaluate predictors of governance awareness; and WHO-derived Nigeria health indicators from Kaggle with Principal Component Analysis (PCA) to reveal latent systemic gaps in health governance and capacity. The results show that adaptive AI adoption significantly reduced healthcare utilization (β = –0.152,  p < 0.001), anomaly detection identified unusual patterns in 3 of 10 reporting weeks, and governance awareness was higher among educated and urban populations. PCA revealed governance gaps (42.7 % variance explained) as the most critical systemic weakness. The study concludes with recommendations for embedding governance-by-design, deploying real-time anomaly detection, promoting equity in governance frameworks, and strengthening infrastructure capacity. These findings have broad implications for policymakers seeking to advance trustworthy, transparent, and ethically aligned AI systems in population health management.