Objective To construct a cloud-based platform for healthcare-associated infection(HAI)management based on multi-source data fusion and data visualization,and evaluate its application effectiveness.Methods A ter-tiary first-class cancer hospital was selected as the research object.Aligned with graded hospital accreditation stan-dards and based on grid-based management and responsibility zone mode,a platform was constructed in 2023 with low-code technology,multi-source data fusion,and visualization function.The self-comparison method was adopted to compare occurrence of HAI before and after the operation of the platform.A questionnaire survey was adopted to assess the experience and workload reduction feelings of full-time and part-time infection surveillance and control profe-ssionals using the platform.Results 81.56％of the surveyed respondents believed that the platform could re-duce the workload of infection surveillance and control.Compared with before the trial operation,the hospital achieved an annual reduction of 11 200 yuan in paper costs,with associated labor savings of approximately 4 482.5 hours.The incidence of HAI cases in the whole hospital decreased from 0.67％to 0.45％.The pathogen detection rate before therapeutic use of antimicrobial agents increased from 51.26％to 71.54％.Differences were both statis-tically significant(both P＜0.05).The detection number and proportion of carbapenem-resistant Pseudomonas aeruginosa(CRPA)and extended-spectrum β-lactamase-producing Klebsiella pneumoniae(ESBL-KP)in HAI cases decreased(54.55％vs 36.47％and 51.14％vs 32.50％,respectively,both P＜0.05).Conclusion The construc-tion and application of smart HAI management cloud-based platform can reduce cost,improve management quality,and provide a theoretical basis and technical paradigm for the construction of smart HAI management system.

Objective To construct an intelligent recognition,prevention,and control system for infectious disea-ses and multidrug-resistant organism infections,aiming at improving the efficacy of full-process management,and to evaluate its application effects.Methods Based on personalized logic parsing rules that accurately reflect the infec-tion status and transmission risks in real-time,an intelligent recognition,prevention,and control system with func-tions of automatic recognition,dynamic labeling,real-time sharing,early warning,and visual guidance was estab-lished.Patients undergoing invasive diagnostic and therapeutic procedures in two departments of a tertiary first-class hospital from October 2023 to May 2024 were selected as the research subjects.The differences in recognition,pre-vention,and control efficacy before and after the application of the system were compared using a self-controlled method,with traditional manual management as the control group and intelligent system management as the experi-mental group.Results A total of 2 146 patients were included in the analysis.The recognition,prevention,and control rate and the accuracy rate of recognizing infected individuals using the intelligent system were enhanced sig-nificantly compared with those using manual mode(improved from 5.3％and 72.4％to 100％,respectively),with statistical significance(both P＜0.001).The median early warning time for infection information reached 85.20 days,with 100％early warning achieved.The average time spent by medical staff on infection information recogni-tion and management was reduced by 4.71 hours per day.Conclusion The intelligent system constructed in this study significantly improves the effectiveness of full-process management in recognition,prevention,and control of infectious diseases and multidrug-resistant organism infection,effectively reduces the risk of cross-infection,and en-hances the efficiency of diagnostic and therapeutic services.

Objective To construct a cloud-based platform for healthcare-associated infection(HAI)management based on multi-source data fusion and data visualization,and evaluate its application effectiveness.Methods A ter-tiary first-class cancer hospital was selected as the research object.Aligned with graded hospital accreditation stan-dards and based on grid-based management and responsibility zone mode,a platform was constructed in 2023 with low-code technology,multi-source data fusion,and visualization function.The self-comparison method was adopted to compare occurrence of HAI before and after the operation of the platform.A questionnaire survey was adopted to assess the experience and workload reduction feelings of full-time and part-time infection surveillance and control profe-ssionals using the platform.Results 81.56％of the surveyed respondents believed that the platform could re-duce the workload of infection surveillance and control.Compared with before the trial operation,the hospital achieved an annual reduction of 11 200 yuan in paper costs,with associated labor savings of approximately 4 482.5 hours.The incidence of HAI cases in the whole hospital decreased from 0.67％to 0.45％.The pathogen detection rate before therapeutic use of antimicrobial agents increased from 51.26％to 71.54％.Differences were both statis-tically significant(both P＜0.05).The detection number and proportion of carbapenem-resistant Pseudomonas aeruginosa(CRPA)and extended-spectrum β-lactamase-producing Klebsiella pneumoniae(ESBL-KP)in HAI cases decreased(54.55％vs 36.47％and 51.14％vs 32.50％,respectively,both P＜0.05).Conclusion The construc-tion and application of smart HAI management cloud-based platform can reduce cost,improve management quality,and provide a theoretical basis and technical paradigm for the construction of smart HAI management system.

Objective To construct an intelligent recognition,prevention,and control system for infectious disea-ses and multidrug-resistant organism infections,aiming at improving the efficacy of full-process management,and to evaluate its application effects.Methods Based on personalized logic parsing rules that accurately reflect the infec-tion status and transmission risks in real-time,an intelligent recognition,prevention,and control system with func-tions of automatic recognition,dynamic labeling,real-time sharing,early warning,and visual guidance was estab-lished.Patients undergoing invasive diagnostic and therapeutic procedures in two departments of a tertiary first-class hospital from October 2023 to May 2024 were selected as the research subjects.The differences in recognition,pre-vention,and control efficacy before and after the application of the system were compared using a self-controlled method,with traditional manual management as the control group and intelligent system management as the experi-mental group.Results A total of 2 146 patients were included in the analysis.The recognition,prevention,and control rate and the accuracy rate of recognizing infected individuals using the intelligent system were enhanced sig-nificantly compared with those using manual mode(improved from 5.3％and 72.4％to 100％,respectively),with statistical significance(both P＜0.001).The median early warning time for infection information reached 85.20 days,with 100％early warning achieved.The average time spent by medical staff on infection information recogni-tion and management was reduced by 4.71 hours per day.Conclusion The intelligent system constructed in this study significantly improves the effectiveness of full-process management in recognition,prevention,and control of infectious diseases and multidrug-resistant organism infection,effectively reduces the risk of cross-infection,and en-hances the efficiency of diagnostic and therapeutic services.