Design and implementation of an end-to-end AI-driven colonoscopy recall workflow at scale.

Key Takeaways for GI Nurses

• Large language models (LLMs) can now automatically extract colonoscopy recall recommendations from unstructured physician reports and letters, reducing manual data entry and potential human errors in scheduling follow-up procedures
• This AI-driven workflow addresses a critical challenge during electronic health record transitions, where colonoscopy recall data must be accurately migrated to prevent patients from falling through scheduling cracks
• The technology demonstrates how artificial intelligence can be integrated into existing endoscopy unit workflows to improve efficiency in managing patient recall schedules and surveillance recommendations
• Implementation at enterprise scale suggests this approach could standardize recall management across multiple endoscopy centers and provider networks

Clinical Relevance

For endoscopy nurses who manage patient scheduling and follow-up care, this AI-driven approach represents a significant advancement in recall workflow efficiency. Currently, many units rely on manual processes to identify and schedule patients for surveillance colonoscopies based on physician recommendations buried within narrative reports. This system is prone to human error and can result in patients missing critical follow-up examinations for polyp surveillance or cancer screening intervals. The automated extraction of recall recommendations could substantially reduce the administrative burden on nursing staff while improving patient safety outcomes.

The timing of this research is particularly relevant as many healthcare systems undergo EHR transitions, a period when patient data migration often leads to scheduling gaps and lost follow-up opportunities. GI nurses frequently find themselves manually reconciling patient recall schedules during these transitions, a time-intensive process that diverts attention from direct patient care. An automated system that can accurately identify and migrate colonoscopy recall recommendations would allow nursing teams to focus on patient education, pre-procedure preparation, and clinical care rather than administrative data management.

From a quality improvement perspective, this technology could enhance compliance with established colonoscopy surveillance guidelines and reduce liability associated with missed follow-up examinations. For nurse managers and quality coordinators, automated recall systems provide better tracking capabilities and reporting metrics, enabling more effective oversight of patient safety initiatives and regulatory compliance in endoscopy units.

Bottom Line

This AI-powered colonoscopy recall workflow represents a practical solution to one of endoscopy nursing's most persistent challenges: ensuring patients receive timely follow-up care based on physician recommendations embedded in unstructured clinical documentation. By automating the extraction and scheduling of recall recommendations, particularly during EHR transitions, this technology could significantly reduce administrative workload for GI nurses while improving patient safety and surveillance compliance across healthcare systems.