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Laboratory turnaround time is a core operational metric linking laboratory performance to clinical decision-making. Delays arise from interacting pre-analytical, analytical, and post-analytical factors that vary by order priority, specimen pathway, workload, and analyzer status. Most turnaround time monitoring remains retrospective, aggregated, and focused on average performance. Such monitoring does not estimate whether an individual test order is likely to become delayed under current operational conditions. This manuscript develops a conceptual transformer-based prediction model for estimating the probability of turnaround delay for each incoming laboratory test order. The model uses order priority, specimen collection time, transport route, department workload, analyzer availability, and historical processing patterns. A transformer encoder ingests timestamped processing milestones from the laboratory workflow, including order entry, specimen collection, transport, accessioning, analyzer loading, result generation, and verification. Static features such as order priority, test type, and transport route are fused with dynamic sequence features through attention-based integration. Conceptually, the model could identify orders at elevated risk of delay as soon as they enter the pre-analytical or analytical pipeline. Its predictions would be expected to support proactive prioritisation, specimen routing, workload balancing, and analyzer assignment. A transformer-based turnaround delay model could shift laboratory operations from retrospective monitoring toward proactive delay prevention. Prospective implementation studies would be needed to evaluate safety, reliability, workflow fit, and operational usefulness.