AI-Driven Architectural Patterns for Scalable Real-Time Triage and Crisis Prediction in Public Health Systems

This paper discusses the most important bottlenecks in triage (the process of determining who gets medical help first) in public health emergency situations and how delays in providing data through batch processing can lead to higher death rates in resource-constrained areas. It proposes a scalable artificial intelligence architecture consisting of event-driven microservices, long short-term memory (LSTM) predictive layers, and Kubernetes auto-scaling, while ensuring adherence to ethical governance standards. The key contributions of this paper include an Operational Data Store (ODS) that consolidates multiple data streams from various sources a phased implementation framework that has been validated; and Federated Learning (which minimizes bias and adds privacy protection). The results show that there are significant improvements to the overall system performance (i.e. improvements to throughput and reductions to latency), as well as effective forecasting during times of crisis, all verified in real-world settings. Ultimately, the objective of the framework is to provide improved operational efficiency and allocation of resources, thereby increasing national health resilience with respect to the large population of India.

Batch Processing, Event-Driven Microservices, Long Short-Term Memory (Lstm), Kubernetes Auto-Scaling, Operational Data Store (ODS), Federated Learning.

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