Vomiting Prevention in Children With Cancer

The goal of this single arm trial is to learn if a machine learning (ML) model predicting the risk of vomiting within the next 96 hours will impact vomiting outcomes in inpatient cancer pediatric patients. The main questions it aims to answer are whether an ML model predicting the risk of vomiting within the next 96 hours will: Primary 1\. Reduce the proportion with any vomiting within the 96-hour window Secondary 1. Reduce the number of vomiting episodes 2. Increase the proportion receiving care pathway-consistent care 3. Impact on number of administrations and costs of antiemetic medications Newly admitted participants will have a ML model predict the risk of vomiting within the next 96 hours according to their medical admission information. The prediction will be made at 8:30 AM following admission. Pharmacists will be charged with bringing information about patients' vomiting risk to the attention of the medical team and implementing interventions.

Inclusion Criteria: * All pediatric patients admitted to the oncology service at SickKids Exclusion Criteria: * Pediatric patients admitted to the oncology service at SickKids that are discharged prior to prediction time