Medical institutions of China still face two challenges in hospital infections currently: one challenge is from infection, including infectious diseases, multidrug-resistant bacteria healthcare-associated infection (HAI), and classic HAI; the another challenge comes from the management of HAI in medical institutions, such as lack of full-time staff and insufficient capacity, inadequate infection control organizations, insufficient awareness of infection control among medical staff, and unbalanced development. To cope with these severe challenges, we must do the following three aspects: establishing the discipline of HAI, and improving people’s infection control ability through human-orienting; improving the management organization and system of HAI; improving the awareness of infection control among all medical staff, carrying out scientific and orderly infection prevention and control work in accordance with the law, and adhering to evidence-based infection control.
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.
Healthcare-associated infection management has advanced rapidly in recent years. With the development of more standards and guidelines, infection control measures become more standardized and evidence-based. Evidence-based measures are increasingly applied in infection control, which promote more studies on the prevention and control of healthcare-associated infections. Furthermore, more new ideas of infection control have emerged, with old ones being challenged. The hand hygiene reform, multidrug-resistant organisms, and surgical site infections become the hot topics in recent years. In addition, whole-genome sequencing also provides more bases for understanding pathogen transmission in hospitals. Based on the high-quality studies published in recent years, this opinion review discusses these hot topics in the prevention and control of healthcare-associated infections.
Healthcare-associated infection outbreaks are a serious threat to patient safety and often cause serious consequences. The use of genotyping methods to identify the source of infection and the route of transmission in outbreaks is a critical point in controlling outbreaks. Recently, the use of whole-genome sequencing (WGS) makes it faster and much more accurate. Compared with traditional methods, WGS can distinguish highly correlated pathogen lineages, track infection source accurately and help researchers understanding the propagation dynamics model, and even provide more target intervention information. The application of WGS technology in healthcare-associated infection outbreak investigation and control is reviewed in this paper, and its advantages and challenges are also evaluated.
With nearly four decades of progress in healthcare-associated infection prevention and control in China, the national quality control efforts in this field have been ongoing for the past ten years, advancing rapidly with significant achievements. Over the last decade, the team of infection control professionals involved in quality management and control in China has consistently expanded, accompanied by an enhancement of their skills. Management capabilities have steadily grown, and operational mechanisms have been continuously refined. As public hospitals transition into a new phase of high-quality development, emphasizing refined management models and intrinsic development of medical quality, it becomes crucial to further fortify the foundation and foster innovation in infection control work to ensure quality. This article provides an overview of the establishment and implementation of the National Center for Quality Control of Infection Prevention and Control, examines the current shortcomings and challenges in the field, and collectively explores the positioning and direction of the development of quality control efforts for infection prevention and control in China.
Objective To understand the current situation of healthcare-associated infection (HAI) in comprehensive hospitals with a number of beds≥900, and provide a reference for the next step in formulating HAI prevention and control measures. Methods The data on the prevalence rate of HAI in comprehensive hospitals with a number of beds≥900 of Yunnan Province between 2020 and 2022 were retrospective collected. The HAI situation and trend in each year were analyzed. Results A total of 119 comprehensive hospitals were included, with 166 745 patients surveyed and 3 237 cases of HAI. Lower respiratory tract infection and urinary tract infection were the most common sites. The department with the highest incidence of hospital infections was the intensive care unit, followed by neurosurgery and hematology. The prevalence rates of HAI showed a downward trend from 2020 to 2022 (2.08% vs. 1.99% vs. 1.79%, χ2=14.301, P<0.001). A total of 1 315 strains of hospital-acquired pathogens were detected, all of which were mainly Gram-negative bacteria, with Escherichia coli and Klebsiella pneumoniae being more common. The rate of antibiotics use and the rate of pathogen testing showed an upward trend from 2020 to 2022 (χ2=79.233, 23.866, P<0.001), the infection rate of incision site and the prophylactic use rate of antimicrobial drugs in patients with class Ⅰ surgery both showed a decreasing trend (χ2=15.551, 6.311, P<0.05). Conclusions The prevalence of infection in comprehensive hospitals of Yunnan Province is decreasing. But the supervision of key departments, the implementation of pathogen prevention and control measures, and the rational use of antibiotics in inpatients are still the focus of future work.
Objective To use bibliometrics to identify research hotspots and emerging trends in the use of artificial intelligence (AI) in healthcare-associated infections (HAI), as well as to offer a resource for more relevant research. Methods The literature on AI and HAI from the Science Citation Index Expanded database of the Web of Science Core Collection was retrieved through computer searches, covering the period from January 1, 1994, to January 22, 2024. VOSviewer (v1.6.19) and CiteSpace (v6.1. R6) software were utilized for bibliometric analysis, creating knowledge maps that include research cooperation networks and keyword analysis. Results A total of 305 documents were included, and both the number of early publications and the frequency of citations were at a very low level for a long time before showing an annual increase trend after 2018. The United States had the most published documents among the 50 countries/regions from where they were sourced. Harvard University was the scientific research institution with the most publications, while Professor Evans HL of the Medical University of South Carolina was the scholar with the most publications. Research on AI in the field of HAI primarily focused on three aspects: AI algorithms and technologies, monitoring and prediction of HAI, and the accuracy of HAI diagnosis and prediction. These findings were based on keyword co-occurrence and clustering analysis. Conclusions A new phase of AI research in the subject of HAI has begun. More in-depth research can be done in the future for the hot direction, as there is still a gap between China’s academic accomplishments in this subject and the advanced level of the world.
The article summarized the national and international history and current situation of healthcare-associated infection control, and analyzed the tendency of new technique and progress in healthcare-associated infection control according to the experience in research and practice.
Objective To explore an artificial intelligence (AI)-based method for automated hand hygiene monitoring and to compare the effectiveness of three algorithms (UniFormerV2, TDN, C3D) in recognizing hand hygiene steps in surgical settings, thereby aiding hospital infection control. Methods From April to October 2024, we non-invasively collected 641 video recordings of healthcare staff performing hand hygiene at four-bay scrub sinks in two tertiary hospitals using overhead HD cameras. The dataset was annotated by five trained experts for model training and validation. Results Following training on 385 samples, internal validation (n=119) showed the C3D model achieved 81% accuracy, 87% recall, and an 83% F1-score. The TDN model achieved 93%, 91%, and 92% for the same metrics. The UniFormerV2 model outperformed both, with an accuracy, recall, and F1-score of 93%—an improvement of over 10 percentage points compared to traditional CNNs (TDN, C3D). It also achieved an 84% accuracy in external validation, demonstrating strong generalization. Conclusion The UniFormerV2 model is more accurate than CNN-based models for hand hygiene step recognition and shows robust performance in external validation. It presents a viable tool for healthcare facilities to enhance hand hygiene management, ultimately improving medical quality and patient safety.
ObjectiveTo investigate the incidence and trendency of healthcare-associated infections (HAIs) in a pediatric intensive care unit (ICU) of a hospital, identify the main objectives of infection control, and formulate corresponding preventive and control measures.MethodsA prospective targeted monitoring method was adopted to investigate HAIs in the pediatric ICU of a hospital from January 2013 to December 2018.ResultsFrom January 2013 to December 2018, the number of target ICU patients was 11 898, the number of patient-days was 55 159; 226 HAIs occurred, the HAI case rate was 1.90%, the incidence of HAI per 1 000 patient-days was 4.10‰, and the adjusted incidence of HAI per 1 000 patient-days was 1.21‰. The main infection site was respiratory tract [83 cases (36.7%)], with ventilator-associated pneumonia in 73 cases (32.3%); secondly, 69 patients (30.5%) had bloodstream infection, among which 48 (21.2%) had non-catheter-related bloodstream infection.ConclusionHospital targeted monitoring is helpful to grasp the situation and trend of HAIs, define the main target of infection control, and formulate corresponding preventive and control measures, which can effectively reduce the incidence of HAIs.