Advances in research on the application of artificial intelligence for healthcare-associated infections surveillance_West China Medical Journal

Authors：

REN Lihuan ¹ , LIU Chunhong ¹ , LIU Jia ² , YANG Lu ³ ,  SUN Ani ¹

1. Department of Hospital Infection Management, Weifang People’s Hospital, Weifang, Shandong 261000, P. R China;
2. Department of Gynecology, Weifang People’s Hospital, Weifang, Shandong 261000, P. R China;
3. Department of Oncology, Weifang People’s Hospital, Weifang, Shandong 261000, P. R China;

Corresponding?author：

SUN Ani, Email: 13780822852@163.com

Keywords：

Artificial intelligence; healthcare-associated infections; surveillance; hand hygiene

DOI：

10.7507/1002-0179.202510114

Video：

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Healthcare-associated infections (HAIs) represent a global public health issue characterized by high incidence rates and severe consequences. They significantly prolong hospital stays, increase risks of excess mortality and disability, exacerbate antimicrobial resistance, and impose a substantial burden on patients’ families and society. Surveillance is the cornerstone of effective HAI prevention and control. Conventional manual surveillance is not only labor-intensive and costly but also lacks standardization. With its powerful data processing and analytical capabilities, artificial intelligence (AI) can significantly reduce the incidence of HAIs, improve patient outcomes, decrease workload, and save costs, offering a new approach for cost-effective and efficient HAI surveillance. This review elaborates on advances in the application of AI in common types of HAIs including sepsis, hospital-acquired pneumonia, urinary tract infections, as well as hand hygiene monitoring, so as to promote the development and implementation of AI in the field of HAI prevention and control.

Citation： REN Lihuan, LIU Chunhong, LIU Jia, YANG Lu, SUN Ani. Advances in research on the application of artificial intelligence for healthcare-associated infections surveillance. West China Medical Journal, 2026, 41(7): 1213-1218. doi: 10.7507/1002-0179.202510114 Copy

1.	Cozzolino C, Mao S, Bassan F, et al. Are AI-based surveillance systems for healthcare-associated infections ready for clinical practice? A systematic review and meta-analysis[J]. Artif Intell Med, 2025, 165(6): 103137.
2.	Arzilli G, De Vita E, Pasquale M, et al. Innovative techniques for infection control and surveillance in hospital settings and long-term care facilities: a scoping review[J]. Antibiotics, 2024, 13(1): 77.
3.	Coker D, Phuong HTK, Nguyen LTP, et al. Establishing a standardized surveillance system for health care-associated infections in Vietnam[J]. Glob Health Sci Pract, 2022, 10(3): e2100284.
4.	Lin MY, Hota B, Khan YM, et al. Quality of traditional surveillance for public reporting of nosocomial bloodstream infection rates[J]. JAMA, 2010, 304(18): 2035-2041.
5.	Streefkerk HRA, Verkooijen RP, Bramer WM, et al. Electronically assisted surveillance systems of healthcare-associated infections: a systematic review[J]. Euro Surveill, 2020, 25(2): 1900321.
6.	van der Werff SD, van Rooden SM, Henriksson A, et al. The future of healthcare-associated infection surveillance: automated surveillance and using the potential of artificial intelligence[J]. J Intern Med, 2025, 298(2): 54-77.
7.	Dos Santos RP, Silva D, Menezes A, et al. Automated healthcare-associated infection surveillance using an artificial intelligence algorithm[J]. Infect Prev Pract, 2021, 3(3): 100167.
8.	Radaelli D, Di Maria S, Jakovski Z, et al. Advancing patient safety: the future of artificial intelligence in mitigating healthcare-associated infections: a systematic review[J]. Healthcare (Basel), 2024, 12(19): 1996.
9.	El Arab RA, Almoosa Z, Alkhunaizi M, et al. Artificial intelligence in hospital infection prevention: an integrative review[J]. Front Public Health, 2025, 13: 1547450.
10.	Sirago G, Zotti F, Mele F, et al. Artificial intelligence for hospital infection prevention and control: real-world implementation, impact, and the gap beyond model development[J]. J Hosp Infect, 2026, 30: S0195-6701(26)00158-1.
11.	Villani Júnior A, Freire MP, Lazar Neto F, et al. Prediction of bacterial and fungal bloodstream infections using machine learning in patients undergoing chemotherapy[J]. Eur J Cancer, 2025, 223: 115516.
12.	Pai KC, Wang MS, Chen YF, et al. An artificial intelligence approach to bloodstream infections prediction[J]. J Clin Med, 2021, 10(13): 2901.
13.	Garnica O, Gómez D, Ramos V, et al. Diagnosing hospital bacteraemia in the framework of predictive, preventive and personalised medicine using electronic health records and machine learning classifiers[J]. EPMA J, 2021, 12(3): 365-381.
14.	Roimi M, Neuberger A, Shrot A, et al. Early diagnosis of bloodstream infections in the intensive care unit using machine-learning algorithms[J]. Intensive Care Med, 2020, 46: 454-462.
15.	Parreco JP, Hidalgo AE, Badilla AD, et al. Predicting central line-associated bloodstream infections and mortality using supervised machine learning[J]. J Crit Care, 2018, 45: 156-162.
16.	Nemati S, Holder A, Razmi F, et al. An interpretable machine learning model for accurate prediction of sepsis in the ICU[J]. Crit Care Med, 2018, 46(4): 547-553.
17.	Peng Z, Varisco G, Long X, et al. A continuous late-onset sepsis prediction algorithm for preterm infants using multi-channel physiological signals from a patient monitor[J]. IEEE J Biomed Health Inform, 2023, 27(1): 550-561.
18.	Adams R, Henry KE, Sridharan A, et al. Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis[J]. Nat Med, 2022, 28: 1455-1460.
19.	李若潔, 吳貽樂, 胡小騫, 等. 安徽省醫院感染監測指標現況調查[J]. 中華醫院感染學雜志, 2025, 35(16): 2495-2498.
20.	Chang B, Sun Z, Peiris P, et al. Deep learning-based risk model for best management of closed groin incisions after vascular surgery[J]. J Surg Res, 2020, 254: 408-416.
21.	Wu G, Cheligeer C, Southern DA, et al. Development of machine learning models for the detection of surgical site infections following total hip and knee arthroplasty: a multicenter cohort study[J]. Antimicrob Resist Infect Control, 2023, 12: 88-98.
22.	Flores-Balado á, Castresana Méndez C, Herrero González A, et al. Using artificial intelligence to reduce orthopedic surgical site infection surveillance workload: algorithm design, validation, and implementation in 4 Spanish hospitals[J]. Am J Infect Control, 2023, 51(11): 1225-1229.
23.	Verberk JDM, van der Kooi TII, Hetem DJ, et al. Semiautomated surveillance of deep surgical site infections after colorectal surgeries: a multicenter external validation of two surveillance algorithms[J]. Infect Control Hosp Epidemiol, 2023, 44(4): 616-623.
24.	Alshanqeeti S, Coffey K, Mcdermott K, et al. Comparing generative artificial intelligence vs. experts for detection of catheter-associated urinary tract infection[J]. Clin Infect Dis, 2026, 82(1): 148-150.
25.	Jakobsen RS, Nielsen TD, Leutscher P, et al. Clinically explainable machine learning models for early identification of patients at risk of hospital-acquired urinary tract infection[J]. J Hosp Infect, 2024, 154: 112-121.
26.	Jones N, Shih MC, Healey E, et al. Use of machine learning to assess the management of uncomplicated urinary tract infection[J]. JAMA Netw Open, 2025, 8(1): e2456950.
27.	Branch-Elliman W, Strymish J, Kudesia V, et al. Natural language processing for real-time catheter-associated urinary tract infection surveillance: results of a pilot implementation trial[J]. Infect Control Hosp Epidemiol, 2015, 36(9): 1004-1010.
28.	Sophonsri A, Lou M, Ny P, et al. Machine learning to identify risk factors associated with the development of ventilated hospital-acquired pneumonia and mortality: implications for antibiotic therapy selection[J]. Front Med (Lausanne), 2023, 10(15): 1268488.
29.	Abujaber AA, Yaseen S, Nashwan AJ, et al. Prediction of stroke-associated hospital-acquired pneumonia: machine learning approach[J]. J Stroke Cerebrovasc Dis, 2025, 34(2): 108200.
30.	Mendon?a EA, Haas J, Shagina L, et al. Extracting information on pneumonia in infants using natural language processing of radiology reports[J]. J Biomed Inform, 2005, 38(4): 314-321.
31.	Cabral SM, Goodman KE, Blanco N, et al. Comorbidity and severity-of-illness risk adjustment for hospital-onset Clostridioides difficile infection using data from the electronic medical record[J]. Infect Control Hosp Epidemiol, 2021, 42(8): 955-961.
32.	?tle? E, Balczewski EA, Keidan M, et al. Clostridioides difficile infection surveillance in intensive care units and oncology wards using machine learning[J]. Infect Control Hosp Epidemiol, 2023, 44(11): 1776-1781.
33.	Tang S, Shepard S, Clark R, et al. Guiding Clostridioides difficile infection prevention efforts in a hospital setting with AI[J]. JAMA Netw Open, 2025, 8(6): e2515213.
34.	Glowicz JB, Landon E, Sickbert-Bennett EE, et al. SHEA/IDSA/APIC practice recommendation: strategies to prevent healthcare-associated infections through hand hygiene: 2022 update[J]. Infect Control Hosp Epidemiol, 2023, 44(3): 355-376.
35.	Geilleit R, Hen ZQ, Chong CY, et al. Feasibility of a real-time hand hygiene notification machine learning system in outpatient clinics[J]. J Hosp Infect, 2018, 100(2): 183-189.
36.	Wiemken TL, Hainaut L, Bodenschatz H, et al. Hand hygiene compliance surveillance with time series anomaly detection[J]. Am J Infect Control, 2019, 47(12): 1449-1452.
37.	Wong SC, Ip SC, Kwok MO, et al. Promoting hand hygiene in a chemotherapy day center: the role of a robot[J]. Antimicrob Resist Infect Control, 2024, 13(1): 154.
38.	Singh A, Haque A, Alahi A, et al. Automatic detection of hand hygiene using computer vision technology[J]. J Am Med Inform Assoc, 2020, 27(8): 1316-1320.
39.	屈世豪. 基于人工智能圖像處理的多人洗手流程自動監管系統[J]. 智能物聯技術, 2022, 5(4): 36-42.
40.	Kim M, Choi J, Jo JY, et al. Video-based automatic hand hygiene detection for operating rooms using 3D convolutional neural networks[J]. J Clin Monit Comput, 2024, 38: 1187-1197.
41.	王朝暉, 任菁菁, 邱艷. 醫防融合視角下未分化疾病的早期識別與全周期管理策略研究進展[J]. 中華全科醫學, 2026, 24(5): 825-828.

1. Cozzolino C, Mao S, Bassan F, et al. Are AI-based surveillance systems for healthcare-associated infections ready for clinical practice? A systematic review and meta-analysis[J]. Artif Intell Med, 2025, 165(6): 103137.
2. Arzilli G, De Vita E, Pasquale M, et al. Innovative techniques for infection control and surveillance in hospital settings and long-term care facilities: a scoping review[J]. Antibiotics, 2024, 13(1): 77.
3. Coker D, Phuong HTK, Nguyen LTP, et al. Establishing a standardized surveillance system for health care-associated infections in Vietnam[J]. Glob Health Sci Pract, 2022, 10(3): e2100284.
4. Lin MY, Hota B, Khan YM, et al. Quality of traditional surveillance for public reporting of nosocomial bloodstream infection rates[J]. JAMA, 2010, 304(18): 2035-2041.
5. Streefkerk HRA, Verkooijen RP, Bramer WM, et al. Electronically assisted surveillance systems of healthcare-associated infections: a systematic review[J]. Euro Surveill, 2020, 25(2): 1900321.
6. van der Werff SD, van Rooden SM, Henriksson A, et al. The future of healthcare-associated infection surveillance: automated surveillance and using the potential of artificial intelligence[J]. J Intern Med, 2025, 298(2): 54-77.
7. Dos Santos RP, Silva D, Menezes A, et al. Automated healthcare-associated infection surveillance using an artificial intelligence algorithm[J]. Infect Prev Pract, 2021, 3(3): 100167.
8. Radaelli D, Di Maria S, Jakovski Z, et al. Advancing patient safety: the future of artificial intelligence in mitigating healthcare-associated infections: a systematic review[J]. Healthcare (Basel), 2024, 12(19): 1996.
9. El Arab RA, Almoosa Z, Alkhunaizi M, et al. Artificial intelligence in hospital infection prevention: an integrative review[J]. Front Public Health, 2025, 13: 1547450.
10. Sirago G, Zotti F, Mele F, et al. Artificial intelligence for hospital infection prevention and control: real-world implementation, impact, and the gap beyond model development[J]. J Hosp Infect, 2026, 30: S0195-6701(26)00158-1.
11. Villani Júnior A, Freire MP, Lazar Neto F, et al. Prediction of bacterial and fungal bloodstream infections using machine learning in patients undergoing chemotherapy[J]. Eur J Cancer, 2025, 223: 115516.
12. Pai KC, Wang MS, Chen YF, et al. An artificial intelligence approach to bloodstream infections prediction[J]. J Clin Med, 2021, 10(13): 2901.
13. Garnica O, Gómez D, Ramos V, et al. Diagnosing hospital bacteraemia in the framework of predictive, preventive and personalised medicine using electronic health records and machine learning classifiers[J]. EPMA J, 2021, 12(3): 365-381.
14. Roimi M, Neuberger A, Shrot A, et al. Early diagnosis of bloodstream infections in the intensive care unit using machine-learning algorithms[J]. Intensive Care Med, 2020, 46: 454-462.
15. Parreco JP, Hidalgo AE, Badilla AD, et al. Predicting central line-associated bloodstream infections and mortality using supervised machine learning[J]. J Crit Care, 2018, 45: 156-162.
16. Nemati S, Holder A, Razmi F, et al. An interpretable machine learning model for accurate prediction of sepsis in the ICU[J]. Crit Care Med, 2018, 46(4): 547-553.
17. Peng Z, Varisco G, Long X, et al. A continuous late-onset sepsis prediction algorithm for preterm infants using multi-channel physiological signals from a patient monitor[J]. IEEE J Biomed Health Inform, 2023, 27(1): 550-561.
18. Adams R, Henry KE, Sridharan A, et al. Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis[J]. Nat Med, 2022, 28: 1455-1460.
19. 李若潔, 吳貽樂, 胡小騫, 等. 安徽省醫院感染監測指標現況調查[J]. 中華醫院感染學雜志, 2025, 35(16): 2495-2498.
20. Chang B, Sun Z, Peiris P, et al. Deep learning-based risk model for best management of closed groin incisions after vascular surgery[J]. J Surg Res, 2020, 254: 408-416.
21. Wu G, Cheligeer C, Southern DA, et al. Development of machine learning models for the detection of surgical site infections following total hip and knee arthroplasty: a multicenter cohort study[J]. Antimicrob Resist Infect Control, 2023, 12: 88-98.
22. Flores-Balado á, Castresana Méndez C, Herrero González A, et al. Using artificial intelligence to reduce orthopedic surgical site infection surveillance workload: algorithm design, validation, and implementation in 4 Spanish hospitals[J]. Am J Infect Control, 2023, 51(11): 1225-1229.
23. Verberk JDM, van der Kooi TII, Hetem DJ, et al. Semiautomated surveillance of deep surgical site infections after colorectal surgeries: a multicenter external validation of two surveillance algorithms[J]. Infect Control Hosp Epidemiol, 2023, 44(4): 616-623.
24. Alshanqeeti S, Coffey K, Mcdermott K, et al. Comparing generative artificial intelligence vs. experts for detection of catheter-associated urinary tract infection[J]. Clin Infect Dis, 2026, 82(1): 148-150.
25. Jakobsen RS, Nielsen TD, Leutscher P, et al. Clinically explainable machine learning models for early identification of patients at risk of hospital-acquired urinary tract infection[J]. J Hosp Infect, 2024, 154: 112-121.
26. Jones N, Shih MC, Healey E, et al. Use of machine learning to assess the management of uncomplicated urinary tract infection[J]. JAMA Netw Open, 2025, 8(1): e2456950.
27. Branch-Elliman W, Strymish J, Kudesia V, et al. Natural language processing for real-time catheter-associated urinary tract infection surveillance: results of a pilot implementation trial[J]. Infect Control Hosp Epidemiol, 2015, 36(9): 1004-1010.
28. Sophonsri A, Lou M, Ny P, et al. Machine learning to identify risk factors associated with the development of ventilated hospital-acquired pneumonia and mortality: implications for antibiotic therapy selection[J]. Front Med (Lausanne), 2023, 10(15): 1268488.
29. Abujaber AA, Yaseen S, Nashwan AJ, et al. Prediction of stroke-associated hospital-acquired pneumonia: machine learning approach[J]. J Stroke Cerebrovasc Dis, 2025, 34(2): 108200.
30. Mendon?a EA, Haas J, Shagina L, et al. Extracting information on pneumonia in infants using natural language processing of radiology reports[J]. J Biomed Inform, 2005, 38(4): 314-321.
31. Cabral SM, Goodman KE, Blanco N, et al. Comorbidity and severity-of-illness risk adjustment for hospital-onset Clostridioides difficile infection using data from the electronic medical record[J]. Infect Control Hosp Epidemiol, 2021, 42(8): 955-961.
32. ?tle? E, Balczewski EA, Keidan M, et al. Clostridioides difficile infection surveillance in intensive care units and oncology wards using machine learning[J]. Infect Control Hosp Epidemiol, 2023, 44(11): 1776-1781.
33. Tang S, Shepard S, Clark R, et al. Guiding Clostridioides difficile infection prevention efforts in a hospital setting with AI[J]. JAMA Netw Open, 2025, 8(6): e2515213.
34. Glowicz JB, Landon E, Sickbert-Bennett EE, et al. SHEA/IDSA/APIC practice recommendation: strategies to prevent healthcare-associated infections through hand hygiene: 2022 update[J]. Infect Control Hosp Epidemiol, 2023, 44(3): 355-376.
35. Geilleit R, Hen ZQ, Chong CY, et al. Feasibility of a real-time hand hygiene notification machine learning system in outpatient clinics[J]. J Hosp Infect, 2018, 100(2): 183-189.
36. Wiemken TL, Hainaut L, Bodenschatz H, et al. Hand hygiene compliance surveillance with time series anomaly detection[J]. Am J Infect Control, 2019, 47(12): 1449-1452.
37. Wong SC, Ip SC, Kwok MO, et al. Promoting hand hygiene in a chemotherapy day center: the role of a robot[J]. Antimicrob Resist Infect Control, 2024, 13(1): 154.
38. Singh A, Haque A, Alahi A, et al. Automatic detection of hand hygiene using computer vision technology[J]. J Am Med Inform Assoc, 2020, 27(8): 1316-1320.
39. 屈世豪. 基于人工智能圖像處理的多人洗手流程自動監管系統[J]. 智能物聯技術, 2022, 5(4): 36-42.
40. Kim M, Choi J, Jo JY, et al. Video-based automatic hand hygiene detection for operating rooms using 3D convolutional neural networks[J]. J Clin Monit Comput, 2024, 38: 1187-1197.
41. 王朝暉, 任菁菁, 邱艷. 醫防融合視角下未分化疾病的早期識別與全周期管理策略研究進展[J]. 中華全科醫學, 2026, 24(5): 825-828.

West China Medical Journal

Advances in research on the application of artificial intelligence for healthcare-associated infections surveillance

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