An Antimicrobial Prescription Surveillance System that Learns from Experience

Inappropriate prescribing of antimicrobials is a major clinical concern that affects as many as 50 percent of prescriptions. To solve this problem, we have developed and deployed an automated antimicrobial prescription surveillance system that assists hospital pharmacists in identifying and reporting inappropriate prescriptions. Since its deployment, the system has improved antimicrobial prescribing and decreased antimicrobial use. As a remedy, we are developing a machine learning algorithm that combines instance-based learning and rule induction techniques to discover new rules for detecting inappropriate prescriptions from previous false alerts.