**5.1 Prospective scaling of CS-DI**

Our model has been used utilized prospectively to determine the risk of patients deteriorating into sepsis. The data was extracted from the Epic Caboodle Data Warehouse and pushed every 15 minutes to an S3 datastore and then to an Amazon Redshift Cloud Data Warehouse. The code to cleanse the data and run the features through our model was stored on docker containers to allow the data to be analyzed prospectively and at scale. The algorithm would calculate a percentage value from 0 to 100% and visually display a near-real time swim lane on an intuitive user interface in our command center. If patients neared a predefined intervention point, a protocol for escalation by the triaging Rapid Response Team (RRT) was initiated.

A crucial step in realizing the potential of ML algorithms is to work closely with the facility's IT department to integrate them into the clinical workflow while minimizing alertfatigue. Ultimately, the successful integration of ML algorithms should aim to enhance the productivity of clinical teams while avoiding any attempt to replace them entirely.
