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In the rapidly evolving landscape of digital pathology, the exponential growth of specimen data volumes poses significant challenges to timely and accurate diagnostic workflows. This conceptual manuscript introduces a novel prioritization framework designed to enhance the triage of high-risk specimens within queue-aware systems, ensuring that critical cases receive expedited review without compromising overall system integrity. Drawing on theoretical principles from systems architecture and healthcare analytics, we propose the specimen prioritization and queue intelligence network (SPQIN), a multi-layered orchestration model that integrates dynamic queue monitoring, risk assessment heuristics, and adaptive feedback topologies to mitigate bottlenecks in pathology laboratories. The framework emphasizes infrastructural resilience, incorporating interpretive formulas for risk propagation and resource allocation to optimize workflow efficiency theoretically. By synthesizing recent literature on artificial intelligence applications in digital pathology, we highlight how SPQIN addresses governance constraints, such as ethical prioritization and data modality integration, in clinical deployment environments. This work underscores the potential for queue-aware triage to transform high-risk specimen review, fostering a more responsive and equitable diagnostic ecosystem. While devoid of empirical validation, the conceptual design offers a blueprint for future infrastructural advancements in AI-driven healthcare systems, promoting theoretical discussions on scalability and interoperability.