HAI surveillance is a fundamental and essential aspect of infection prevention and control programs [6,7,8,9,10]. Traditional surveillance is a time-consuming, manual infection-finding and reporting program that involves infection control physicians reviewing lists of positive pathogen culture infection information on a daily basis and patient histories and clinician HAI case reports. In this study, the results showed low incidences of infection with IASS (1.48%) and certain sites of infection, including respiratory infections (0.93%), urinary tract infections (0.25%) , SSI (0.09%), BSI (0.14%) and skin and soft infections. tissue infection (0.12%), reported by traditional manual monitoring methods in period 1. These low reported incidences of infection echo the inaccurate reporting shown by other studies [9, 12]. Seifi’s study presented results of HAI under-reporting and over-reporting by traditional surveillance . Many hospitals have a large number of beds but an insufficient number of infection control doctors, and infection control doctors have to collect large amounts of data daily, which will lead to false negative infection reports. Many cases are excessive or unnecessary use of antibiotics will cause false positive infection results. Additionally, many cases are not ordered for pathogen culture to cause false negative infection reports. In addition, clinicians’ lack of knowledge regarding complicated infection cases will lead to under-reporting and misreporting of infectious events. Given their high workload and need to multitask, clinicians can make mistakes and run out of time to report infections. All of these factors make this type of manual monitoring unreliable and inaccurate. There is no doubt that this frustrating situation would have serious consequences, including lack of treatment for patients with under-reported HAIs, inappropriate treatment of patients infected with false positives, transmission of pathogens from patients with HAIs to other uninfected patients, and many HAIs and outbreaks that are not monitored. and uncontrolled. Therefore, this difficult situation catalyzed the development and implementation of RT-NISS.
Numerous studies in the past have demonstrated accurate and effective electronic automatic monitoring systems [11, 12, 14,15,16]. Our analysis showed higher incidences of infection from IAS and certain sites of infection, including respiratory infections, urinary tract infections, SSIs, BSIs, and skin and soft tissue infections, reported by RT-NISS (both in period 2 and period 3) compared to those reported by traditional surveillance in period 1 (both p
Another strength of our study was investigating the effect of daily RT-NISS implementation on IASS monitoring. After the implementation of RT-NISS in period 2, we analyzed the long-term data collected by RT-NISS and gained comprehensive knowledge about HAI risk factors. Then we implemented some interventions (Table 4). And for departments that have not implemented MDRO prevention and control measures and/or inappropriately administered antibiotics and/or without standard pathogen culture, we reported the monthly usage rate antibiotics and the rate of pathogen culture in each ward to keep them on the condition of having special education for healthcare workers and severe economic measures have been implemented for the serious situation. The incidences of hospital-acquired IASS and MDRO in period 3 were significantly lower than those in period 2 (0.41% versus 0.52%, p = 0.021). These results demonstrated that the adoption of RT-NISS coupled with the implemented interventions could reduce the rate of HAI. Moreover, regarding the MDRO results reported by the RT-NISS in period 2, we further found 3 errors associated with the use of the RT-NISS. 1. There were 24 other types of multidrug resistant Pseudomonas species misclassified as CRPA resulting in 24 CRPA FPs. 2. 14 CRE misclassified as ESBL resulted in 14 FP ESBL and 14 FN CRE. 3. 13 ESBL, 19 MRSA, 7 CRAB and 5 CPPA were under-reported due to LIS connection failure by RT-NISS. Based on these errors above, we checked and corrected different varieties of RT-NISS MDRO rule definitions, we also instructed RT-NISS engineers to regularly check the connection with other hospital information systems and timely correct the connection failure in the 3 period. After modifying the rule definition and timely correction of the input data, the number of FP and FN of 5 different varieties of MDRO, including ESBL, MRSA, CRAB, CRPA and CRE, in period 3 was reduced to 0, and the Se, Sp, PPV and NPV of 5 different varieties of MDRO in period 3 were both improved to 100%.
Our study still has a limitation. Since all patient information for period 1 (the manual monitoring phase) was collected only by manual monitoring, it is difficult to obtain all sufficient information from a large number of patients. Thus, whether the composition of the patient population or the specialty of the patients would affect the individual infection types in 3 periods of the present study is uncertain.