This paper expounds the classification and characteristics of healthcare-associated infections (HAI) surveillance systems from the perspective of the informatization needs of HAI monitoring, explains the determination requirements of numerator and denominator in the surveillance statistical data, and introduces the regular verification for auditing the quality of HAI surveillance. The basic knowledge of machine learning and its achievements are introduced in processing surveillance data as well. Machine learning may become the mainstream algorithm of HAI automatic monitoring system in the future. Infection control professionals should learn relevant knowledge, cooperate with computer engineers and data analysts to establish more effective, reasonable and accurate monitoring systems, and improve the outcomes of HAI prevention and control in medical institutions.
