Qventus brings artificial intelligence to the almost inhumanly complicated business of managing a large hospital. By constantly monitoring electronic medical record (EMR) and other data, the machine learning platform is able to predict potential problems and prescribe corrections to head them off, improving workflow for frontline staff, reducing wait times, and generating savings and better outcomes for patients. Within five months of employing Qventus, client Stanford Children’s Health decreased operating room case delays by 11% or 10 minutes on average, resulting in 520 hours of prevented operating room case delays. The emergency department at Mercy Fort Smith had a 30% decrease in patients who left without being seen, a 24-minute reduction in length of stay, and a 20% reduction in door-to-doc time, allowing the hospital to see 3,000 more patients in one year with the same staffing resources. Improvements such as these helped the hospital boost its patient-satisfaction rank from 29th out of 30 in the Mercy system to third, and to add an estimated $1.3 million in additional annual revenue and savings.