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AI Case Study

MetroHealth predicts patient flow to improve operational decision making using machine learning

MetroHealth has been able to leverage Qventus's AI platform to predict patient flow and allocate resources accordingly improving patient care and staff productivity.



Healthcare Providers And Services

Project Overview

MetroHealth is particularly keen in applying AI in operations like insurance authorization, patient eligibility and moving the pieces to improve length of stay.

"MetroHealth is still in the process of implementing Qventus, but the solution has already made a tangible difference. The system's scheduling team is now equipped with real-time information to determine "this doctor can handle one more visit today or this doctor has too many patients scheduled," Mr. Botros said. "If that simplicity can be delivered behind the scenes, we will have unbelievable success."

Reported Results

Patient flow prediction allows hospitals to plan staffing and patient allocation




General Operations


MetroHealth is a single-hospital public health system with about 25 care locations in Ohio. The system reported $1 billion in total revenue in 2016. Analysis revealed 15 to 20 percent of their primary care physician base was under-utilized, primarily due to a high rate of no-shows.



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