Realtime prediction of C-section risk for laboring mothers

Over the last 10 years, Cedars-Sinai Health System has made significant efforts to bring reduce unnecessary C-sections and bring down variability in rates by provider. As part of that effort, providers in the Obstetrics department and data scientists from the Enterprise Data Intelligence team built a model to predict the likelihood that a laboring mother should have a C-section. The goal of the model is to reduce the number of unnecessary C-sections and also to identify necessary C-sections earlier in the course of labor.
Model overview
*Predictions are first made based on factors known within the first hour of admission, including pre-natal visit info, demographics and basic measurements
*The model is then updated every 10 minutes to include new information that may have come in during that time around lab values, cervical exam measurements, medications administered or other relevant events
*The admission based predictions achieved an AUC of 0.78, the continuous predictions have an AUC of 0.93 after the first 4 hours of predictions on validation data
*Models use streaming data to make predictions and return them within ~10 minutes of data being recorded