Digital Twin Simulation of Enterprise Healthcare Integration Environments: A Virtual Modeling Framework for Pre-Deployment Validation, Capacity Planning, and Failure Scenario Rehearsal
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V7I2P108Keywords:
Digital Twin, Simulation Modeling, Discrete-Event Simulation, Healthcare Integration, Capacity Planning, Failure Rehearsal, Enterprise Middleware, Pre-Deployment Validation, HL7, FHIRAbstract
Enterprise healthcare integration environments are complex distributed systems comprising hundreds of interdependent channels, diverse transport protocols, heterogeneous message standards, and variable upstream/downstream system behaviors. Changes to these environments including new interface deployments, configuration modifications, volume increases, and infrastructure migrations carry inherent risk, as interaction effects between channels sharing common resources are difficult to predict analytically. Currently, no validated methodology exists for simulating the behavior of an entire integration environment prior to production changes. This paper introduces the Integration Environment Digital Twin (IEDT) framework a discrete-event simulation model that replicates the complete topology, resource allocation, message routing logic, and volumetric behavior of an enterprise healthcare integration engine. IEDT enables operators to execute what-if scenarios including new interface onboarding, volume surge simulations, infrastructure failure injections, and configuration change impact assessments in a sandboxed virtual environment before applying changes to production. Validation across three operationally representative scenarios demonstrates that IEDT predicts system-wide message throughput within 6.3% of observed values, identifies resource contention bottlenecks with 89% accuracy, and correctly models cascading queue backpressure across 11 of 12 affected channels during outage simulation. Thread pool exhaustion timing is predicted within 4 minutes of actual occurrence. IEDT provides a validated, low-risk methodology for pre-deployment impact analysis in enterprise healthcare integration environments.
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