Antimicrobial resistance is driven high volume, inappropriate prescribing. Antibiotic usage (AU) and antimicrobial resistance (AMR) data alone are incapable of reporting appropriate use of antimicrobials. There is a need to create deeper data driven analysis of appropriate antibiotic prescribing in real-time. Data readily available through electronic records could be mined and analysed to represent new measures of AU that better define or incorporate appropriate prescribing. Data could include: real-time AU, correlated AMR, filtered patient factors, filtered drug specific factors, relevant TDM where relevant, and associated observations. Models could be used to predict AU relative to new measures of appropriateness to guide decision making, policy making, and identify concerning trends.