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Specialist consultation delays are a pervasive source of prolonged inpatient stays and disrupted throughput. They remain difficult to anticipate because delay risk emerges across ordering, communication, workload, and completion steps. A predictive model could support earlier recognition of consults likely to exceed expected completion windows. Existing consultation monitoring often depends on retrospective reports, manual tracking, or informal escalation. These approaches miss the opportunity to intervene while the consultation is still unfolding. A real-time model could convert consult workflow events into actionable delay forecasts. This article proposes a sequence learning model that predicts the probability of specialist consultation completion delay at the time of order entry. The model would refine this probability after each subsequent event, including messages, assignment, escalation, note drafting, and completion. The objective is conceptual model development rather than experimental evaluation. The proposed approach uses an LSTM-, GRU-, or Transformer-based architecture to ingest consultation milestones and static context. Inputs include consultation type, patient location, ordering service, specialty workload, communication logs, and escalation history. The output is a dynamically updated delay probability intended for consult workflow management. Conceptually, the model would identify high-risk consults early, such as a complex weekend consultation for a critically ill patient with no timely response from an overloaded service. It would be expected to support targeted escalation, workload redistribution, and proactive communication. No empirical performance claims are made. A sequence learning model could help hospitals move from passive consultation tracking to proactive delay management. By combining temporal workflow events with operational context, the model could support more timely specialist input and reduce avoidable length-of-stay pressure. Future evaluation should focus on safety, fairness, usability, and workflow impact.

Repeat diagnostic imaging is a major driver of healthcare costs, radiation exposure, workflow burden, and downstream follow-up from incidental findings. Its occurrence often follows recognizable patterns shaped by prior imaging, persistent symptoms, specialist advice, recent results, and provider ordering habits. Current utilization management tools often respond after an imaging order has already been placed or rely on static appropriateness rules. They therefore miss opportunities to anticipate repeat ordering risk before the clinician reaches the final order-entry step. This manuscript proposes a sequence learning model that could predict the probability of a repeat diagnostic imaging order within a future clinical window. The model would integrate prior imaging history, symptom documentation, specialist recommendations, ordering physician behavior, and recent test results. The conceptual model would use a recurrent neural network or Transformer encoder to process temporally ordered imaging and clinical events. Structured radiology information system data would be combined with natural language processing features from clinical notes and consult documentation, physician-level ordering context, and recent laboratory or imaging-result signals. Conceptually, the model would forecast whether a repeat CT, MRI, ultrasound, or related diagnostic imaging order is likely to occur. It would also identify major contextual drivers so that the prediction could support pre-emptive review, alternative care suggestions, or guideline-aligned follow-up. A sequence learning model for repeat imaging prediction could function as a safety-and-value layer within radiology workflow. It could reduce unsupported imaging variation while preserving clinically indicated follow-up and surveillance.