Pharmaco-economic evaluation of antibiotic therapy strategies in DRG-based healthcare systems - a new approach

Abstract The cost of treatments especially in conditions where multiresistant bacteria are involved are a major issue in times where in most developed countries in the world payment systems based on diagnoses-related-groups (DRG) are in place. There is great evidence that especially the length of stay in hospital (LOS), the time in the intensive care unit (ICU-days) and the hours of mechanical ventilation (HMV) are major cost drivers. While established methods of pharmacoeconomical analyses focus on the efficiency of drugs from healthcare system perspective, these data are often not sufficient for improving treatment strategies in a given hospital context. We developed a system that allows the analysis of patients with severe infections on the basis of routine data that is also used for reimbursement. These data contain a lot of information concerning the clinical conditions. By using the ICD-coding we developed an algorithm which allows the detection of patients with infections and gives information on the potential financial outcome of these patients. By using the analysis it is possible to identify subsets of infections and the patient records that had a potentially negative DRG-result, i.e. the costs are higher than the reimbursement. When identified the patient records undergo a peer review, where the clinical situation and the antibiotic therapy are reviewed by medical experts. In case simulations it is possible to find out if a different therapeutic approach, e.g. by different choices in initial (empirical) antibiotic treatment would have caused other outcomes. Data driven analyses together with peer reviews of patient records are a useful tool to examine antibiotic treatment strategies and to establish changes that again can be reviewed on a regular basis. Doing this a continous improvement process can be established in hospitals which can lead to a better balance of clinical and economical outcomes in patients with severe infections. Moreover these analyses are helpful in assessing the literature on economical benefits of new therapies.