Surgical site infection (SSI) is a long-term issue in the hospital and contributes to patient deaths and disability. Also healthcare-associated SSI (HASSI) increases healthcare costs and extends hospitalization stay, so prevention of HASSI is one of the most important issues in infection control. However, current manual surveillance method might be error prone, labor-intensive and time-consuming. To improve the efficiency of HASSI surveillance and evaluate different detection rule using various data sources (antibiotic prescription, microbiology laboratory, discharge diagnosis codes, surgical report and symptoms & physical examination), the HASSI surveillance system was developed. Electronic medical records from September 2013 through February 2014 of 12,100 inpatients at the National Taiwan University Hospital were screened by five defined rules of HASSI. The data from 2013 was allocated to a train data set (for developing training model), and the rest, to a test data set (for testing model validity). The rule of antibiotic prescription, microbiology laboratory and symptoms & physical examination has higher odds in training model. The validation in training model by the test data set from 2014 has sensitivity of 100%, specificity of 78.38%, positive predictive value of 1.4%, negative predictive value of 100% and the area under receiver operating characteristic (ROC) curve is 0.957. The HASSI system decrease the number of patients need to evaluate by infection control personnel (ICP) and significantly improves the efficiency of HASSI detection.