Problem: Current echocardiogram scheduling at Duke University Hospital assigns uniform time slots without accounting for patient complexity, leading to resource inefficiencies, delays, and workflow bottlenecks. Complex cases, such as valve disease, require more time, causing disruptions and increased stress on sonographers, while patients experience delays in timely cardiovascular diagnostics and care.
Solution: This solution implements an AI-driven scheduling system that uses EMR data to predict scan complexity and allocate time slots based on patient needs. By dynamically adjusting for case difficulty and matching sonographer expertise, the system optimizes resource use, reduces delays, enhances patient care, and improves workflow efficiency in the echo lab.
Impact: This AI-driven scheduling system will improve echocardiogram efficiency, reduce patient wait times, and enhance resource allocation in the echo lab. Sonographers benefit from reduced stress and injury risk, while patients experience timely, higher-quality care. The system’s scalable design supports broader improvements across Duke Health’s diagnostic services.
