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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.

Healthcare administration generates large volumes of textual data, including clinical notes, billing documentation, incident narratives, portal messages, and operational records. These data create administrative burden but also provide opportunities for natural language processing to support documentation, coding, communication, safety review, and workflow automation. This systematic review examined natural language processing models applied to healthcare administration from 2017 to 2024. The review focused on five domains: clinical documentation, coding support, incident reporting, patient communication, and workflow automation. A PRISMA 2020-compliant review was conducted using structured searches of PubMed, Scopus, IEEE Xplore, and Web of Science for studies published between 2017 and 2024. Eligible studies were screened by two reviewers, extracted using a structured template, and synthesized narratively by administrative domain, model type, evaluation approach, and implementation maturity. Transformer-based models were increasingly prominent across the reviewed literature, particularly in documentation support, automated coding, and clinical text classification. Clinical documentation and coding support had the strongest evidence base, while incident reporting, patient communication, and workflow automation remained less mature and less frequently evaluated in real-world settings. Natural language processing for healthcare administration is technically promising, but most applications remain at the retrospective or proof-of-concept stage. Prospective validation, workflow integration, governance, and human oversight are needed before widespread operational adoption.