This study was carried out in a tertiary hospital in Beijing with approximately 3,500 beds (including 201 beds in 11 ICUs); it performs 270 surgical operations each day, accepts 12,000 inpatients each month, with an average length of stay of 10 days. The hospital has the most advanced computer information system in China, including integrated hospital information system (IHIS), laboratory information system (LIS), imageology achieving system (RIS), and an anesthesia operation system (AOS).
The system uses data mining J2EE (Java 2 Platform, Enterprise Edition), AJAX (Asynchronous JavaScript and XML), and Flex computer technologies. It is operated with Oracle 10.0 database, Tomcat 6.0. The system automatically starts downloading data from the hospital information systems (e.g. IHIS, LIS, RIS, AOS) at 2.00 am, but data can also be downloaded manually at any time. The daily data can be collected and analyzed within 10 to 20 minutes.
For the purposes of managing inpatient data for this study, approval was obtained from the Medical Ethics Committee of the Chinese PLA General Hospital (approval number 11KMM51).
NI definitions
NIs were defined according to the Chinese NI diagnosis criterion published by the Ministry of Public Health in 2001 [10], which differs somewhat from the Centers for Disease Control and Prevention (1988) [11]. First, in China, the definition for respiratory tract infection (RTI) includes the CDC categories of ‘pneumonia’, ‘lower respiratory tract infection other than pneumonia’, and ‘upper tract respiratory infection’ (if the inpatient has a fever of ≥38C for more than 2 days and a sore throat). Second, patients ≤12 months are included in the general population rather than as a separate group.
NIs were classified as RTI, urinary tract infection (UTI), bloodstream infection (BSI), SSI, gastrointestinal tract infection (GTI), skin and soft tissue infection (STI) and other infections, including parotitis, chickenpox, and neurological infections. An infection was considered to be a NI if the patient was diagnosed ≥48 hours after admission, and had no evidence of subclinical infection at the time of admission. NIs were diagnosed based on clinical diagnosis and pathogens diagnosis, that is, the NIs were in accordance with clinical diagnosis definitions, but they did not necessarily need the pathogen/s identified, i.e. for upper tract respiratory infection cases.
RT-NISS NIs computer algorithm (Professional Suspicious NIs Screening Criteria)
The professional suspicious NIs screening strategy was based upon specific diagnostic criteria for certain infection sites; these took into consideration positive microbiological examinations, antibiotic administration, serological and molecular testing (e.g. C-reactive protein, calcitonin), positive imaging reports, temperature, invasive device use, and inpatient transfer data. Some screening strategies could be used for a variety of wards, but specific ones were needed for special wards. For example, the screening strategy for general SSI included: 1) continual fever (temperature ≥38.0C) for 2 days after surgery, but excluding a temperature within 72 hours after surgery (surgical fevers mostly occur during this time period); 2) positive microbiological examinations of surgical wound secretions; and 3) new antibiotic administration, excluding prophylactic use of antibiotics. Aside from these three conditions, the screening strategy for central nerve SSI after brain surgery included: 1) white blood cell (WBC) abnormality in cerebrospinal fluid (CSF); 2) antibiotic administration into the vertebral canal; and 3) increased demand for CSF testing post-surgery. All of the NIs algorithms were automatically performed on each inpatient; the system then alerted staff if the inpatients met any of the screening criteria.
RT-NISS real-time NIs prewarning alerts
The NI computer algorithm could prewarn staff of all suspicious NIs, but it could not warn about new NIs inpatients every day. In order to realize real-time prewarning, the system was able to record NI-related information and undertake time-serial alignments of these cases. On the next day, when the system had detected new suspicious NIs, it could automatically use information collected on the previous day (as baseline) to avoid repeat alerting; new suspicious NIs were alerted, but the previously confirmed NIs were not alerted again. For example, a similar positive microbiological examination from a patient did not trigger the system again when the patient had already been recognized as having a NI. But with regards to a previously excluded alert, if the infection conditions worsened and met the standard criteria again, a new alert was triggered. For example, an alert of “fever for 3 days” for an inpatient was excluded; if the patient’s temperature increased the next day (i.e. “fever for 4 days”), or his/her infectious condition met other screening criteria (such as positive microbiological examination), then an alert would be triggered for this patient on the same day. Otherwise, there would be no subsequent alert for this inpatient under such conditions.
RT-NISS NIs confirmation from prewarning alerts
Each morning the ICPs were able to deal efficiently with all of the suspicious alerts with the support of RT-NISS. Most of suspicious alerts were confirmed by ICPs, but some complicated or questionable ones were confirmed after discussion with the physicians through the “interactive communication platform” between RT-NISS and the physicians’ workstation (such as via mobile telephone messaging). Further, the physicians could communicate with the ICPs about NI-related questions. A flow chart of NI prewarning and confirmation is illustrated in Figure 1. All of the confirmed NIs were calculated to create an overall prevalence for the wider hospital. Some special cases (ie. SSI, CABSI, multi-drug resistant pathogen infection) were arranged into a special targeted module to enable the ICPs to pay closer attention to these cases, and conduct relevant clinical interventions.
RT-NISS NIs outbreak prewarning
Clusters and outbreaks were defined by the Chinese Ministry of Public Health in 2009 [12]. An NI ‘outbreak’ is identified as ≥3 NI cases with the same pathogenic infection (identified homologous) that occur in a single department during a short period (usually one week). The definition for ‘cluster’ is ≥3 cases with the same pathogenic infection detected in one department (usually within one week), or if the NI incidence of one department is higher than baseline levels when epidemiological surveys or homologous identification cannot be performed. The RT-NISS selected the prewarning threshold according to the type of cluster. The prewarning criteria included three parts: the same multi-drug resistant pathogen cluster (≥2 or ≥3 identical pathogens within 7 days on a single ward, according to the surveillance data history), diarrhea cluster (≥2 confirmed NIs with diarrhea within 7 days on one ward, or ≥3 patients with positive fecal testing within 3 days on one ward, excluding routine fecal testing at the time of admission, because the number of routine fecal tests would increase in special wards for viral diarrhea, which is indicative of a suspicious diarrhea outbreak), and SSI cluster (≥2 confirmed SSI inpatients within 7 to 21 days on one ward, according to the baseline SSI rates for that ward). In order to realize sensitivity around prewarning of outbreaks, the prewarning criteria combined NIs with community infections, and the microbiological reports included confirmed infections, colonization, or contamination; we considered that community infection is sometimes the source of outbreaks under certain circumstances, and the cross-spread of pathogens may cause infection, colonization or contamination in the hospital setting.
If a ward triggered the prewarning criteria, the system would highlight the name of the ward in red. The operator could obtain more detailed information about this alert by opening the file on the computer to access the inpatients’ ID, inpatients’ bed, infection pathogen, infected site, infection duration, admission time, attending physician, and such like. The ICPs were able to make preliminary judgments about the alert, as either dangerous or a false positive, and whether clinical investigations or interventions were required immediately for dangerous alerts.