ArticleJAMIA open2026
Design and implementation of an end-to-end AI-driven colonoscopy recall workflow at scale.
Article in JAMIA open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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0 citing papers in PubMed.
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Authors and funding
16 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Objectives: To develop a large-language-model (LLM)-centric workflow flow extraction and migration of clinician-documented colonoscopy recall recommendations from unstructured reports and letters during an enterprise-wide electronic health record (EHR) transition. Materials and Methods: A multi-stage workflow [Optical Character Recognition (OCR) -> LLM -> structured fields] was built around a central GPT-4 Turbo inference step following prompt optimization. Validation was performed on a held-out set ( Results: Prompt optimization enabled GPT-4 Turbo to achieve perfect concordance with clinician review in a small test set (macro-F1 = 1.0; Discussion: An LLM-driven workflow safely migrated preventive-care data at population scale, with potential accuracy improvements over traditional rule-based NLP approaches and substantial reductions in time and cost relative to manual review. Conclusion: LLMs can play a valuable role in high-quality structuring of clinical data, preserving longitudinal care continuity during EHR modernization.
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