Implementing Real-Time ADT Event Processing for Case Management Triggering in Medicaid Populations
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V4I1P104Keywords:
ADT Events, Case Management, Medicaid, HL7, Kafka, FHIR, Real-Time Processing, Population HealthAbstract
Timely and proactive care coordination is essential for improving outcomes in Medicaid populations, who often experience high rates of chronic illness, care fragmentation, and social vulnerability. Admissions, discharges, and transfers (ADT) represent key care transition events that, if detected and acted upon in real-time, can trigger meaningful interventions by case management teams. This paper presents the design, implementation, and evaluation of a scalable, standards-based architecture for real-time ADT event processing, specifically tailored for Medicaid case management workflows. Our system ingests HL7 v2.x ADT messages via Apache Kafka, transforms them into enriched FHIR resources, and applies configurable trigger logic to determine actionable care events. A patient attribution layer and rule engine identify high-risk scenarios such as preventable discharges or repeat ED visits. We validate our pipeline using 50,000 synthetically generated ADT messages representing Medicaid-like populations. Key metrics—including end-to-end latency, trigger precision, and case initiation rates—are analyzed to assess system performance. The results demonstrate that real-time ADT processing significantly improves responsiveness, enabling same-day outreach and reducing missed interventions. This approach lays the groundwork for intelligent, event-driven care coordination under value-based care models
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