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.
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.
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.
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.
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 article provides a thorough interpretation of the recommendations for implementation research in healthcare-associated infection (HAI) prevention and control, jointly issued by the Society for Healthcare Epidemiology of America, the Infectious Diseases Society of America, and the Association of Professionals in Infection Control and Epidemiology. The recommendations elaborate on the concepts, strategies, determinants, and evaluation methods of implementation research, as well as the commonly used theories, models, and frameworks (TMF) in the field of HAI prevention and control. By expounding on these TMF, this article aims to guide readers in deeply considering the scientific issues related to the implementation of hospital infection prevention and control, and to provide guidance on selecting and applying appropriate resources in specific environments and situations. The release of these recommendations aims to promote the implementation of evidence-based guidelines in medical institutions and ultimately achieve the goal of reducing the incidence of hospital infections by promoting and guiding the conduct of implementation research in the field of HAI prevention and control.
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.
Objective To construct a quality evaluation index system for healthcare-associated infection (HAI) management, and conduct an empirical evaluation on the quality of HAI management in clinical departments. Methods The literature research method and panel discussion method were adopted to initially form the framework of HAI management quality evaluation index system, and the Delphi method and the analytic hierarchy process were used to establish the index system and determine the weights from January to December 2018. Eight comprehensive evaluation methods, such as osculating value method and technique for order preference by similarity to an ideal solution method, were used to evaluate the quality of HAI management in clinical departments of West China Hospital, Sichuan University in 2018. Kendall’s coefficient of concordance (W) was used to assess the consistency of the results. The clinical departments were ranked by the standardized total scores, which were the means of the normalized scores of the eight methods. Results A quality evaluation index system for HAI management with 3 first-level indicators and 15 second-level indicators was established finally. The results of the eight comprehensive evaluation methods for the quality evaluation of HAI management in 39 clinical departments of West China Hospital, Sichuan University were consistent (W=0.952, χ2=259.800, P<0.001). The standardized total score of Department 18 was 100, which ranked the first place. Conclusion The HAI management quality evaluation index system constructed in this study could be used in clinical departments to evaluate the quality of HAI management in combination with comprehensive evaluation methods.
ObjectiveTo measure and evaluate the economic burden of hospital infection in Sichuan, and provide a basis for targeted economic evaluation of healthcare-associated infection (HAI).MethodsIn hospitals participating in the 2016 Sichuan provincial prevalence survey of HAI, matched cases were used to extract cases and controls, and then a multi-center nested case-control study was conducted.ResultsA total of 225 pairs/450 patients were selected in 51 hospitals, and 175 pairs/350 patients were successfully matched. The median of the difference of hospitalization costs between matched-pairs were RMB 3 362.0, and the difference was statistically significant (Z=3.275, P<0.001).ConclusionsThe hospitalization costs caused by HAI should be given special attention in the current medical insurance reform. Efforts need to be taken to reduce the hospitalization costs caused by HAI.
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.