Real-time automatic hospital-wide surveillance of nosocomial infections and outbreaks in a large Chinese tertiary hospital.
BACKGROUND
We aimed to develop a real-time nosocomial infection surveillance system (RT-NISS) to monitor all nosocomial infections (NIs) and outbreaks in a Chinese comprehensive hospital to better prevent and control NIs.


METHODS
The screening algorithm used in RT-NISS included microbiological reports, antibiotic usage, serological and molecular testing, imaging reports, and fever history. The system could, in real-time, identify new NIs, record data, and produce time-series reports to align NI cases.


RESULTS
Compared with a manual survey of NIs (the gold standard), the sensitivity and specificity of RT-NISS was 98.8% (84/85) and 93.0% (827/889), with time-saving efficiencies of about 200 times. RT-NISS obtained the highest hospital-wide monthly NI rate of 2.62%, while physician and medical record reviews reported rates of 1.52% and 2.35% respectively. It took about two hours for one infection control practitioner (ICP) to deal with 70 new suspicious NI cases; there were 3,500 inpatients each day in the study hospital. The system could also provide various updated data (i.e. the daily NI rate, surgical site infection (SSI) rate) for each ward, or the entire hospital. Within 3 years of implementing RT-NISS, the ICPs monitored and successfully controlled about 30 NI clusters and 4 outbreaks at the study hospital.


CONCLUSIONS
Just like the "ICPs' eyes", RT-NISS was an essential and efficient tool for the day-to-day monitoring of all NIs and outbreak within the hospital; a task that would not have been accomplished through manual process.
Mingmei
M
Du
Yubin
Y
Xing
Jijiang
J
Suo
Bowei
B
Liu
Na
N
Jia
Rui
R
Huo
Chunping
C
Chen
Department of Infection Management and Disease Control, Chinese PLA General Hospital, 28 Fuxing Road, Haidian District, Beijing 100853, China. yunxiliu6511@sohu.com.
Yunxi
Y
Liu
D000465:Algorithms; D002681:China; D003428:Cross Infection / Q000175:diagnosis / Q000453:epidemiology / Q000517:prevention & control; D057225:Data Mining / Q000706:statistics & numerical data; D004196:Disease Outbreaks / Q000517:prevention & control*; D006751:Hospital Information Systems / Q000592:standards*; D006801:Humans; D011159:Population Surveillance / Q000379:methods*; D012680:Sensitivity and Specificity; D062606:Tertiary Care Centers / Q000592:standards*; D013997:Time Factors