Motivating exemple: Wait time for patients.
No info except grabbing a nurse or a doctor
ML to help inform patients during their stay
Cold start problem: scarce data
Hospital expect accurate ML results from day one
No history when opening a new facility
Emergency departments varies a lot
Data regime change (think Covid)
Solution: a facility-agnostic model, predicting the wait percentile
Model use raw feature and aggregated features at the facility level
Instead of predicting in minutes, we predict in percentile of the historical wait CDF, of a given facility (see below that it can varies a lot for same percentile)
Implementing using Tecton
Feature service for all feature to normalize, indexed using a facility_id
Lambda periodically extract tecton features into Redshift
Redshift is the data source
