PartheasFlow

Partheas starts a POC roll out for patient flow processes, applying machine learning. Over time Partheas collected millions of historic data elements and different analysis axes on the different consultations. Not only does it allow us to data mine this information. More significantly, it allows us to learn and apply knowledge real-time. Partheas key objectives are to

• significantly improve certain algorithms that calculate information on waiting timings ;
• allow software to autonomously adjust its behaviour towards increasingly better results ;
• allow our software to make better future prognosis of time slots with higher and lower occupancy.

In a first phase, we want to benchmark the existing application and its traditional algorithms with a new AI engine.

Partheas already rounded up a first pre-study Q1 2020. We are currently starting up a new phase. However, we are still looking for hospitals who would be interested in participating. From Partheas’ end we are especially interested in making inventory of a broader scale of patient logistic processes.

Is your hospital interested in participating? Are you interesting in getting a glimpse of the progress and results? Don’t hesitate to ask more information.

UPDATE April 2020: AI for smart hospitals. Partheas joins a study lead by the German Fraunhofer institute.