Data-driven antimicrobial stewardship Using local data to reduce antimicrobial consumption

Antimicrobial resistance (AMR) poses a growing threat to human health around the world. As more bacteria become resistant to antibiotics, mortality due to antibiotic-resistant infections increases. The main driver of AMR is antimicrobial consumption. Therefore, strategies to reduce antimicrobial consumption are necessary. In this thesis, we propose a strategy to measure and develop interventions to reduce antimicrobial consumption and antibiotic related adverse events using local data. This strategy comprises a collaborative approach in which the Antimicrobial stewardship team (A-team) and prescribers together define knowledge gaps and/or barriers that preclude appropriate antibiotic use. After the joint formulation of research questions, the appropriate (local) data is collected and analysed. The research results are discussed and antimicrobial treatment policies are amended if necessary. After implementation of the intervention, the local antibiotic policy can be re-evaluated with regards to adherence and relevant clinical outcome measures. This cyclical process is referred to as “data-driven antimicrobial stewardship”. The availability of large amounts of data (i.e. electronic patient record, prescription, microbiological and diagnostic data) allows us to form a better picture of the epidemiology, aetiology and outcome of infectious diseases. These data can help us weigh the benefit of adequate empirical therapy and the harm of excessive (broad-spectrum) antibiotic use. Thus, we can optimise our antibiotic policies without compromising treatment effectiveness. In this thesis we show several examples of how the analysis of local data, together with all those involved in the treatment, can be used to reduce antibiotic use and prevent antibiotic related adverse events.