Emergency Department Revisit Quality Review Screening: Exploring Human Decision-Making and Artificial Intelligence Support

An exploratory study investigated Emergency Department (ED) return visit quality assurance processes, specifically focusing on revisits within 1-14 days. Current practices often limit reviews to very short timeframes (e.g., 48-72 hours) to manage workload, potentially missing quality improvement opportunities. This research explored the use of clinicians and a large language model (GPT-4) to assess diagnosis pairs for their potential to warrant further quality assessment.

This research is strategically important as it addresses the efficiency and effectiveness of quality assurance processes in critical service delivery environments. Improving the identification of quality issues, potentially through AI augmentation, can lead to enhanced patient safety, better resource utilization, and sustained trust in institutional services. It points to a future where AI tools could significantly scale quality oversight without proportionally increasing human burden.

Existing ED quality assurance protocols for return visits are often constrained by narrow timeframes (e.g., 48-72 hours), which may restrict the identification of all relevant quality improvement areas.

Forward consideration, not a verified fact.