https://www.selleckchem.com/products/ots514.html st allocation must be carried out in accordance with the surgical resource expenditure.  Reconstructive microsurgical procedures lead to a significant increase in revenue in interdisciplinary surgical cases. However, a significant increase in resource consumption is observed as well. Moreover, these additional costs are not always adequately reflected in the revenue of the DRG. This especially applies to DRGs with a high initial cost weight. To ensure modern, individual, patient-oriented and guideline-compliant patient care, there is, therefore, an urgent need to adapt the (G-)DRG system to the additional resource consumption. In addition, in the case of interdisciplinary surgical cases, a clear internal cost allocation must be carried out in accordance with the surgical resource expenditure. Hospital readmissions are a key quality metric, which has been tied to reimbursement. One strategy to reduce readmissions is to direct resources to patients at the highest risk of readmission. This strategy necessitates a robust predictive model coupled with effective, patient-centered interventions. The aim of this study was to reduce unplanned hospital readmissions through the use of artificial intelligence-based clinical decision support. A commercially vended artificial intelligence tool was implemented at a regional hospital in La Crosse, Wisconsin between November 2018 and April 2019. The tool assessed all patients admitted to general care units for risk of readmission and generated recommendations for interventions intended to decrease readmission risk. Similar hospitals were used as controls. Change in readmission rate was assessed by comparing the 6-month intervention period to the same months of the previous calendar year in exposure and control hospitals. Among 2,460 hospitalizations assesstificial intelligence to identify patients at the highest risk for readmission can reduce quality gaps when coupled with patient-ce