The research, led by Zihuan Li and Tian Wang with corresponding author Yisui Cen, was structured as a before-and-after analysis spanning nearly eight years of hospital operations. The investigators divided the study period into two distinct phases. The pre-intervention period, designated Period 1, ran from 2018 through 2022 and captured routine surveillance practices as they existed before the enhancement. The post-intervention period, Period 2, covered 2023 through 2025, after the hospital deployed its enhanced active surveillance program. During the intervention phase, a multidisciplinary infection prevention and control team conducted daily active surveillance using a novel intelligent early warning system, and provided real-time feedback to clinical staff on infection diagnosis, antimicrobial use, isolation practices, and environmental hygiene.

The scale of the dataset lends the findings considerable weight. In total, the researchers identified 2,600 HAI cases during the five-year pre-intervention period and 1,697 cases during the three-year post-intervention period. Rather than simply comparing crude averages between the two eras, the team employed interrupted time-series analysis, a statistical framework widely regarded as one of the strongest quasi-experimental designs for evaluating the impact of a policy change when a randomized trial is not feasible. This method models the underlying trend of an outcome over time, then tests whether the intervention produced an immediate level shift at the moment of implementation, a change in the slope of the trend, or both.

The headline result concerns timeliness of reporting. Before the intervention, 32.85 percent of HAI cases were reported late, a figure that fell to 28.23 percent afterward, a statistically significant decrease with a p-value of 0.001. The interrupted time-series analysis added granularity: during Period 2, the delayed reporting rate declined by an average of 1.240 percentage points per quarter, with a 95 percent confidence interval stretching from minus 2.408 to minus 0.078 percent, and the downward trend itself was significant at p equals 0.033. In practical terms, this means the improvement was not a one-time blip but a sustained, quarter-by-quarter erosion of the delayed reporting problem, exactly the pattern one would hope to see from a surveillance system that learns and embeds itself into daily workflow.

Perhaps the most striking finding was the jump in pre-discharge reporting, the proportion of infections identified and documented before the patient leaves the hospital. This rate rose from 74.54 percent to 89.69 percent, a highly significant improvement with p less than 0.001. The time-series model pinpointed an immediate increase of 13.453 percentage points at the intervention point itself, with a 95 percent confidence interval of 3.143 to 23.763 percent and a p-value of 0.012. That instantaneous level shift is telling. It suggests the intelligent early warning system began surfacing infections that would previously have slipped through the cracks almost as soon as it went live, likely because automated alerts flag suspicious clinical patterns, laboratory results, and antimicrobial prescriptions in real time rather than relying on clinicians to notice and report them at the end of a busy shift.

Speed mattered in the details as well. The median time of delayed reporting, among cases that were still reported late, dropped from 11 days, with an interquartile range of 7 to 18 days, to 8 days, with an interquartile range of 6 to 12 days, a difference significant at p less than 0.001. Even when the system could not prevent a delay entirely, it compressed the lag. The median interval from infection onset to discharge shortened from 11 days to 9 days, and the median time from reporting to discharge fell from 9 days to 7 days, both with p-values below 0.001. Shorter intervals between onset, detection, and discharge carry real clinical meaning: earlier recognition allows earlier isolation, earlier targeted therapy, and earlier discharge planning, all of which reduce the window in which a pathogen can spread to other patients on the ward.

What makes this study technically interesting is the architecture of the intervention itself. Active surveillance, in contrast to passive reporting, means that trained personnel actively hunt for infections rather than waiting for them to be reported. The Chinese team layered an intelligent early warning system on top of this human effort, presumably mining electronic health record data to generate alerts, although the published abstract does not detail the underlying algorithm. Crucially, the system did not operate in isolation. A multidisciplinary infection prevention and control team reviewed the alerts daily and closed the loop with real-time feedback covering four domains: whether the infection diagnosis was correct, whether antimicrobial use was appropriate, whether isolation practices were being followed, and whether environmental hygiene standards were being met. This combination of machine detection and human verification addresses a well-known weakness of purely automated surveillance, namely alert fatigue and false positives that erode clinician trust.

The findings arrive at a moment when health systems worldwide are under intensifying pressure to curb HAIs. Antimicrobial resistance has turned once-routine hospital infections into therapeutic nightmares, and regulatory bodies increasingly tie reimbursement and accreditation to infection metrics. Surveillance is the foundation of every infection prevention program because you cannot control what you cannot see, yet underreporting and delayed reporting remain endemic problems in hospitals everywhere. Traditional surveillance depends on infection control practitioners manually reviewing charts, a labor-intensive process that scales poorly and lags behind clinical reality. The Guangzhou results suggest that intelligent early warning systems, coupled with disciplined human follow-up, can close much of that gap, raising the pre-discharge reporting rate by roughly 15 percentage points and shaving days off critical time intervals.

Several caveats deserve honest acknowledgment. The study was conducted at a single tertiary hospital in China, and its retrospective design means the researchers could not control for concurrent changes in case mix, staffing, or the lingering effects of the COVID-19 pandemic era on hospital operations during the early period. Interrupted time-series analysis is robust, but it cannot fully exclude secular trends or co-interventions that happened to coincide with the 2023 implementation. The abstract also does not report whether the surveillance improvements translated into lower overall HAI incidence or reduced mortality, outcomes that matter most to patients. What the data do establish is a chain of process improvements: faster reporting, more complete reporting before discharge, and shorter times from infection onset and from report to discharge. Whether those process gains compound into harder clinical endpoints is the natural next question for future research.

Even with those limitations, the study offers a template that other hospitals can adapt. The core recipe, an automated early warning layer feeding a multidisciplinary team that delivers immediate, specific feedback on diagnosis, antimicrobials, isolation, and environmental hygiene, does not require exotic technology, only integration of existing electronic health record data with a well-resourced infection control workforce. The authors received no external funding for the work, and the study was approved by the hospital’s ethics committee with informed consent waived owing to the de-identified retrospective database. As hospitals everywhere grapple with resistant organisms and stretched infection control staff, the message from Guangzhou is clear: surveillance that watches actively, warns intelligently, and feeds back instantly can turn the slow, leaky pipeline of infection reporting into something approaching real time, and that shift alone may be worth days of hospital stay for every affected patient.

Subject of Research: Effectiveness of enhanced active surveillance with an intelligent early warning system for healthcare-associated infections

Article Title: Effectiveness analysis of enhanced active surveillance for healthcare-associated infections

Article References: Li, Z., Wang, T., Wu, H., Lin, G., Fan, C., Liu, B., Liu, D., & Cen, Y. (2026). Effectiveness analysis of enhanced active surveillance for healthcare-associated infections. BMC Infectious Diseases. https://doi.org/10.1186/s12879-026-14566-y

Image Credits: AI Generated

DOI: 10.1186/s12879-026-14566-y

Keywords: healthcare-associated infections, active surveillance, infection prevention and control, intelligent early warning system, interrupted time-series analysis, delayed reporting rate, pre-discharge reporting, hospital epidemiology, antimicrobial stewardship, patient safety, BMC Infectious Diseases, tertiary hospital