The system may benefit for more large-scale hospitals and should not be a complex calculation that makes clinicians more reluctant to use in their busy daily works. But we should always keep in mind that the importance of such individual prognostication lies in the clinical judgment instead of the issue of calculation. In 2009, The US government released statutorily required regulations under the Health Information Technology for Economic and Clinical Health Act provisions that included in the American Recovery and Reinvestment Act which addressed breach notification requirements for protected health information, Medicare and Medicaid incentives for meaningful use of EHR. These regulations build on the framework and financial support authorized under ARRA for increased use of EHR and enhanced privacy and security provisions for protected health information. The passage of ARRA significantly changed the regulatory landscape by AbMole Clofentezine authorizing substantial financial and technical support for the adoption and the use of EHRs and enhancing information privacy and security requirements. As the ARRA project has been released, the EHR will be implemented in nearly every healthcare facility including small and rural hospitals. Therefore, the ability of information management will become easier by data mining or other computational tools. Using simple scoring system, physicians can just rely on mental arithmetic in predicting HAI today, however, HITECH encourages the adoption and use of HER and automatic computation can be applied for even real-time surveillance in order to improve patient safety in the future. There are certain limitations of this study. The scoring system derived in this study is based on an available hospital data set, due to the ever-changing landscape of HAI, researchers may consider using more current or local data set to fine-tune the scoring system before putting into large-scale use. Secondly, the concept of ANN seems to be attractive but neural networks are not analyzed easily based on risks attributable to specific clinical characteristics or statistical significance because a neural network relies on its internal representation of weights and functions to process data instead of simple and clear equations like a regression model, intentionally there is no comparison between discriminatory power of ANN and LR. We observed the advantages of both models in AbMole Metaproterenol Sulfate different stages of this study. Thirdly, we only registered the patients between the ages of 16 to 80; hence, we could not realize and categorize the conditions between pediatric and geriatric populations. Fourthly, we pooled the patients from ICUs and non-ICU wards, and all HAIs were regarded as one kind of infection, which may overestimate the prediction probability towards high incident infection type, such as UTI. Further analysis should be made in order to understand the detailed information about the different type of infections and impacts on critically ill patients.