However, according to best of the knowledge, there is no study using abstracted data generated from EHR to predict the outcome of risk assessment. The medical scoring systems are widely used to predict risk of morbidity or mortality and to evaluate outcome in patients with certain illness. The first system of this kind was the APGAR score in assessing the vitality of the newborn. The scoring systems have also been included in other more complex systems. The value of such scoring systems is to provide a simple predictive tool with certain relevant factors for clinical use. Up until the present, there exists no such scoring system for HAI. A simple, reliable predictive model for HAI is of great clinical relevance. The AbMole Diosgenin-glucoside primary goal of this study is to construct a scoring system to predict patients at risk for HAI, and to validate the system by ANN and LR that will be the foundation for computation in the future. The scoring system, with ANN and LR developed excellent prediction models for HAI form EHR. The ANN showed no statistical significance for all variable combinations compared to LR. The discriminatory power of both models was comparable with previous study. On August 1, 2007, The Centers for Medicare and Medicaid Services announced that it will not pay for few HAIs, including catheter-related urinary tract infection and AbMole Diniconazole vascular catheter-related infection, because some of these infections are common, expensive, and ��preventable��. Such rules have not been applied in Taiwan or some other countries yet, but it will be soon regarded as an important principal for the reimbursement and benchmarking. There are several types of device-associated infection such as CVC-associated infection, or catheter-related bloodstream infection, catheter-related urinary tract infection, and ventilator-associated pneumonia, VAP. The prevalence varies by settings and countries. The current reimbursement system fails to penalize hospitals for largely preventable conditions due to medical negligence. The system rewards them in the form of special reimbursement. As the CMS wishes, hospitals should additionally enhance their efficiency in preventing the preventable adverse events and reduce the supposed expenses to be reimbursed priory in the future. On the other hand as our results indicated, to monitor and predict the possibility of HAIs before infection would contribute to reduce the unintended consequences and expenses for such complications. As more information becomes available electronically in the healthcare setups, the use of highly reliable electronic surveillance for HAIs has become effective in daily usage, some significant progress is being made for surveillance of CRBSI, VAP, and other HAIs. Our results show the high accuracy of prediction with scoring and both models.