Predictive Analytics Framework for Multi-Generational Health Data: Architecture, Implementation, and Clinical Outcomes at Population Scale

Abstract

A substantial share of chronic and hereditary disease risk propagates through family units rather than individuals, yet the majority of clinical analytics frameworks treat patient records as independent observations without reference to family health context. This paper presents a practitioner-informed technical review of a production-grade predictive analytics framework designed to reorganize clinical data around family units, link records across multiple electronic medical record systems using a FHIR-based interoperability layer, and apply machine learning models to identify family-based disease risk at the population scale. The framework integrates graph-structured family relationship models, temporal feature engineering encoding health trajectories across generations, and real-time clinical decision support workflows delivering family risk scores to care coordinators. Drawing on direct implementation experience in large-scale population health programs, we describe the data architecture, identity resolution pipeline, ML model design, and deployment infrastructure required to operationalize multi-generational analytics in production. We evaluate methodological requirements for family-aware model validation, discuss governance challenges specific to family-level health data, including relational consent and genetic discrimination risk, and identify the equity implications of deploying genomically-informed family risk models in ancestrally diverse populations. We further discuss federated learning and social determinant integration as the most consequential near-term extensions of the framework. The paper is intended to bridge the gap between theoretical proposals for multi-generational health analytics and the architectural and governance realities of deploying such systems at the scale of millions of members.