Using Machine Learning techniques and historical data, our system predicts the most accurated surgery duration reducing the under/over-use
A bad schedule leads to a large economical losses. We provide an automatic scheduler that computes the optimal scheduling, increasing in that way the usage time and reducing the economical losses.
All departments are interconnected though a set of applications which distribute the tasks in a more efficient way. All the gathered data is used to display the surgical suite state in RT.
A simple user-friendly data analysis framework has been developed to exploit the gathered data and to detect and fix the bottlenecks of the patient flow.