Readmission is a major source of cost for healthcare systems. Hospital-specific readmission rates are considered an indicator of hospital performance and generate public interest regarding the health care quality. We aimed to identify those patients who are likely to be readmitted to the hospital. The iden- tified patients can then be considered by health care personnel for application of preventive alternative measures such as: providing intensive post-discharge care, managing the conditions of the most vul- nerable in their home, supporting self-care, and integrating health services and information technology systems to avoid unnecessary readmissions. Neural Network, Classification and Regression model and Chi-squared Automatic Interaction Detection models were used for the readmission prediction. All models were able to perform with an overall accuracy above 80%, with the latter two models having the advantage of providing the user with the opportunity of selecting different misclassification costs. We employed C5.0 algorithm to search for recurring pattern in the history or demographics of patients who have been readmitted and explored if a rule of thumb can be derived to predict those at risk of future readmissions. Moreover, the key variables influencing readmission were studied based on a large data set. The most important factors contributing to readmission were determined such as age, sex, number of previous prescriptions and length of previous stays, place of service, and number of previous claims. In a hospital, surgical supplies can be stored in multiple locations, each of which has limited space and different associated costs. The locations include central storage, where items are retrieved to build a cart of supplies for each procedure; sterile storage adjacent to the operating rooms; and within the operating rooms themselves. In practice, the decision on allocating items to these locations is often based on the staff's experience, rather than through optimization methods. In this research, we have identified the costs associated with each location to determine where each item should be stored and in what quantities. These costs include the cost of building the case cart, the cost of returning unused items to storage, the cost of picking items during a procedure, the cost of restocking and the cost of reviewing items to determine what needs to be replenished. Since the number of supplies required to perform a procedure is uncertain, we have developed a robust stochastic mixed-integer programming model to make the inventory allocation decision. The model also enables a hospital to assess the potential cost saving from optimization of the preference cards, which are used by surgeons to specify the requested supplies available on the case carts. The performance of the proposed model is evaluated through a case study. Three alternatives to the current configuration of the system are presented and reduction of inventory expenditure within each alternative is discussed. Finally, sensitivity analyses are performed to determine which cost parameters contribute to the model more significantly and how the model behaves against different levels of risk coefficient.