Predicting Eligibility Gaps in CHIP Using BigQuery ML and Snowflake External Functions
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V3I2P105Keywords:
CHIP, eligibility gaps, BigQuery ML, Snowflake, external functions, SQL-based ML, gap-filling models, predictive analytics, healthcare lapses, Medicaid churnAbstract
Using SQL-based ML technologies specifically, Google Big Query ML & Snowflake External Functions this study explores the latest approach for identifying & projecting their eligibility gaps in the Children's Health Insurance Program (CHIP). The primary goal is to enhance their public health projections by means of their proactive identification of those at risk of losing CHIP coverage resulting from administrative mistakes, changeable income, or inadequate documentation. By integrating demographic data and structured healthcare into Big Query, one may immediately train machine learning models within SQL environments, hence removing traditional data engineering limitations and accelerating model deployment. Concurrent with this, Snowflake External Functions enabled simple access to third-party APIs and cloud services, hence improving contextual insights and supporting dynamic rule application. By means of their combined usage, these systems provide a scalable and affordable method to expose trends and risk indicators often hidden within large-scale statistics. Our results suggest that this paradigm might effectively predict potential eligibility interruptions, hence allowing more timely interventions and legislative changes. The study emphasizes the increasing importance of SQL-based ML technology in public sector projects, especially in situations where time-sensitive decisions influence their vulnerable groups. By allowing data analysts to work within familiar environments, these technologies democratize their access to advanced analytics & thereby support fast & informed decision-making in healthcare systems. This work argues for data-native, ML-driven approaches in public health management, therefore improving a proactive, data-informed model of care continuity
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