Prevention and control of healthcare-associated infection in information age_West China Medical Journal

Authors：

 GE Maojun ^1,2

1. Department of Surgery, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, P. R. China;
2. Institute of Traditional Chinese Medicine Information, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, P. R. China;

Corresponding author：

GE Maojun, Email: maojun.ge@sgyy.cn

Keywords：

Information technology; Surveillance; Healthcare-associated infection; Big data; Machine learning

DOI：

10.7507/1002-0179.202002006

Video：

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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.

Citation： GE Maojun. Prevention and control of healthcare-associated infection in information age. West China Medical Journal, 2020, 35(3): 280-284. doi: 10.7507/1002-0179.202002006 Copy

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2.	Gastmeier P, Behnke M. Electronic surveillance and using administrative data to identify healthcare associated infections. Curr Opin Infect Dis, 2016, 29(4): 394-399.
3.	Emori TG, Edwards JR, Culver DH, et al. Accuracy of reporting nosocomial infections in intensive-care-unit patients to the National Nosocomial Infections Surveillance System: a pilot study. Infect Control Hosp Epidemiol, 1998, 19(5): 308-316.
4.	Lin MY, Hota B, Khan YM, et al. Quality of traditional surveillance for public reporting of nosocomial bloodstream infection rates. JAMA, 2010, 304(18): 2035-2041.
5.	Schröder C, Behnke M, Gastmeier P, et al. Case vignettes to evaluate the accuracy of identifying healthcare-associated infections by surveillance persons. J Hosp Infect, 2015, 90(4): 322-326.
6.	Haut ER, Pronovost PJ. Surveillance bias in outcomes reporting. JAMA, 2011, 305(23): 2462-2463.
7.	Russo PL, Barnett AG, Cheng AC, et al. Differences in identifying healthcare associated infections using clinical vignettes and the influence of respondent characteristics: a cross-sectional survey of Australian infection prevention staff. Antimicrob Resist Infect Control, 2015, 4: 29.
8.	Mayer J, Greene T, Howell J, et al. Agreement in classifying bloodstream infections among multiple reviewers conducting surveillance. Clin Infect Dis, 2012, 55(3): 364-370.
9.	Evans RS, Larsen RA, Burke JP, et al. Computer surveillance of hospital-acquired infections and antibiotic use. JAMA, 1986, 256(8): 1007-1011.
10.	Lin MY, Trick WE. Informatics in infection control. Infect Dis Clin North Am, 2016, 30(3): 759-770.
11.	Jackson SS, Leekha S, Pineles L, et al. Improving risk adjustment above current centers for disease control and prevention methodology using electronically available comorbid conditions. Infect Control Hosp Epidemiol., 2016, 37(10): 1173-1178.
12.	Moore GE. Cramming more components onto integrated circuits. Electronics, 1965, 38: 114-117.
13.	Woeltje KF, Butler AM, Goris AJ, et al. Automated surveillance for central line-associated bloodstream infection in intensive care units. Infect Control Hosp Epidemiol, 2008, 29(9): 842-846.
14.	van Mourik MSM, Perencevich EN, Gastmeier P, et al. Designing surveillance of healthcare-associated infections in the era of automation and reporting mandates. Clin Infect Dis, 2018, 66(6): 970-976.
15.	Trick WE, Zagorski BM, Tokars JI, et al. Computer algorithms to detect bloodstream infections. Emerg Infect Dis, 2004, 10(9): 1612-1620.
16.	Hota B, Lin M, Doherty JA, et al. Formulation of a model for automating infection surveillance: algorithmic detection of central-line associated bloodstream infection. J Am Med Inform Assoc, 2010, 17(1): 42-48.
17.	Boonstra A, Broekhuis M. Barriers to the acceptance of electronic medical records by physicians from systematic review to taxonomy and interventions. BMC Health Serv Res, 2010, 10: 231.
18.	de Bruin JS, Seeling W, Schuh C. Data use and effectiveness in electronic surveillance of healthcare associated infections in the 21st century: a systematic review. J Am Med Inform Assoc, 2014, 21(5): 942-951.
19.	van Mourik MS, van Duijn PJ, Moons KG, et al. Accuracy of administrative data for surveillance of healthcare-associated infections: a systematic review. BMJ Open, 2015, 5(8): e008424.
20.	Marra AR, Alkatheri M, Edmond MB. Catheter-associated urinary tract infection: utility of the ICD-10 metric as a surrogate for the National Healthcare Safety Network (NHSN) surveillance metric. Infect Control Hosp Epidemiol, 2017, 38(4): 506-507.
21.	Sips ME, Bonten MJM, van Mourik MSM. Automated surveillance of healthcare-associated infections: state of the art. Curr Opin Infect Dis, 2017, 30(4): 425-431.
22.	Rhee C, Kadri S, Huang SS, et al. Objective sepsis surveillance using electronic clinical data. Infect Control Hosp Epidemiol, 2016, 37(2): 163-171.
23.	Arnold KE, Thompson ND. Building data quality and confidence in data reported to the National Healthcare Safety Network. Infect Control Hosp Epidemiol, 2012, 33(5): 446-448.
24.	Schneeweiss S. Learning from big health care data. N Engl J Med, 2014, 370(23): 2161-2163.
25.	Raghupathi W, Raghupathi V. Big data analytics in healthcare: promise and potential. Health Inf Sci Syst, 2014, 2: 3.
26.	Upshur RE. Looking for rules in a world of exceptions: reflections on evidence-based practice. Perspect Biol Med, 2005, 48(4): 477-489.
27.	Bi Q, Goodman KE, Kaminsky, et al. What is machine learning?A primer for the epidemiologist. Am J Epidemiol, 2019, 188(12): 2222-2239.
28.	Beam AL, Kohane IS. Big data and machine learning in health care. JAMA, 2018, 319(13): 1317-1318.
29.	Roth JA, Battegay M, Juchler F, et al. Introduction to machine learning in digital healthcare epidemiology. Infect Control Hosp Epidemiol, 2018, 39(12): 1457-1462.
30.	Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med, 2019, 380(14): 1347-1358.
31.	Althouse BM, Ng YY, Cummings DA. Prediction of dengue incidence using search query surveillance. PLoS Negl Trop Dis, 2011, 5(8): e1258.
32.	Signorini A, Segre AM, Polgreen PM. The use of twitter to track levels of disease activity and public concern in the U. S. during the influenza a H1N1 pandemic. PLoS One, 2011, 6(5): e19467.
33.	Oh J, Makar M, Fusco C, et al. A generalizable, data-driven approach to predict daily risk of clostridium difficile infection at two large academic health centers. Infect Control Hosp Epidemiol, 2018, 39(4): 425-433.
34.	Colborn KL, Bronsert M, Amioka E, et al. Identification of surgical site infections using electronic health record data. Am J Infect Control, 2018, 46(11): 1230-1235.
35.	Sanger PC, Granich M, Olsen-Scribner R, et al. Electronic surveillance for catheter-associated urinary tract infection using natural language processing. AMIA Annu Symp Proc, 2017, 2017: 1507-1516.
36.	Fujikura Y, Hamamoto T, Kanayama A, et al. Bayesian reconstruction of a vancomycin-resistant enterococcus transmission route using epidemiologic data and genomic variants from whole genome sequencing. J Hosp Infect, 2019, 103(4): 395-403.
37.	Liao YH, Wang ZC, Zhang FG, et al. Machine learning methods applied to predict ventilator-associated pneumonia with pseudomonas aeruginosa infection via sensor array of electronic nose in intensive care unit. Sensors (Basel), 2019, 19(8): E1866.
38.	Leetaru K. How Twitter corrupted microsoft’s tay: a crash course in the dangers of AI in the real world. (2017-09-27)[2020-02-01]. https://www.forbes.com/sites/kalevleetaru/2016/03/24/how-twitter-corrupted-microsofts-tay-acrash-course-in-the-dangers-of-ai-in-the-real-world/4f39fd926d2.

1. Woeltje KF, Lin MY, Klompas M, et al. Data requirements for electronic surveillance of healthcare-associated infections. Infect Control Hosp Epidemiol, 2014, 35(9): 1083-1091.
2. Gastmeier P, Behnke M. Electronic surveillance and using administrative data to identify healthcare associated infections. Curr Opin Infect Dis, 2016, 29(4): 394-399.
3. Emori TG, Edwards JR, Culver DH, et al. Accuracy of reporting nosocomial infections in intensive-care-unit patients to the National Nosocomial Infections Surveillance System: a pilot study. Infect Control Hosp Epidemiol, 1998, 19(5): 308-316.
4. Lin MY, Hota B, Khan YM, et al. Quality of traditional surveillance for public reporting of nosocomial bloodstream infection rates. JAMA, 2010, 304(18): 2035-2041.
5. Schröder C, Behnke M, Gastmeier P, et al. Case vignettes to evaluate the accuracy of identifying healthcare-associated infections by surveillance persons. J Hosp Infect, 2015, 90(4): 322-326.
6. Haut ER, Pronovost PJ. Surveillance bias in outcomes reporting. JAMA, 2011, 305(23): 2462-2463.
7. Russo PL, Barnett AG, Cheng AC, et al. Differences in identifying healthcare associated infections using clinical vignettes and the influence of respondent characteristics: a cross-sectional survey of Australian infection prevention staff. Antimicrob Resist Infect Control, 2015, 4: 29.
8. Mayer J, Greene T, Howell J, et al. Agreement in classifying bloodstream infections among multiple reviewers conducting surveillance. Clin Infect Dis, 2012, 55(3): 364-370.
9. Evans RS, Larsen RA, Burke JP, et al. Computer surveillance of hospital-acquired infections and antibiotic use. JAMA, 1986, 256(8): 1007-1011.
10. Lin MY, Trick WE. Informatics in infection control. Infect Dis Clin North Am, 2016, 30(3): 759-770.
11. Jackson SS, Leekha S, Pineles L, et al. Improving risk adjustment above current centers for disease control and prevention methodology using electronically available comorbid conditions. Infect Control Hosp Epidemiol., 2016, 37(10): 1173-1178.
12. Moore GE. Cramming more components onto integrated circuits. Electronics, 1965, 38: 114-117.
13. Woeltje KF, Butler AM, Goris AJ, et al. Automated surveillance for central line-associated bloodstream infection in intensive care units. Infect Control Hosp Epidemiol, 2008, 29(9): 842-846.
14. van Mourik MSM, Perencevich EN, Gastmeier P, et al. Designing surveillance of healthcare-associated infections in the era of automation and reporting mandates. Clin Infect Dis, 2018, 66(6): 970-976.
15. Trick WE, Zagorski BM, Tokars JI, et al. Computer algorithms to detect bloodstream infections. Emerg Infect Dis, 2004, 10(9): 1612-1620.
16. Hota B, Lin M, Doherty JA, et al. Formulation of a model for automating infection surveillance: algorithmic detection of central-line associated bloodstream infection. J Am Med Inform Assoc, 2010, 17(1): 42-48.
17. Boonstra A, Broekhuis M. Barriers to the acceptance of electronic medical records by physicians from systematic review to taxonomy and interventions. BMC Health Serv Res, 2010, 10: 231.
18. de Bruin JS, Seeling W, Schuh C. Data use and effectiveness in electronic surveillance of healthcare associated infections in the 21st century: a systematic review. J Am Med Inform Assoc, 2014, 21(5): 942-951.
19. van Mourik MS, van Duijn PJ, Moons KG, et al. Accuracy of administrative data for surveillance of healthcare-associated infections: a systematic review. BMJ Open, 2015, 5(8): e008424.
20. Marra AR, Alkatheri M, Edmond MB. Catheter-associated urinary tract infection: utility of the ICD-10 metric as a surrogate for the National Healthcare Safety Network (NHSN) surveillance metric. Infect Control Hosp Epidemiol, 2017, 38(4): 506-507.
21. Sips ME, Bonten MJM, van Mourik MSM. Automated surveillance of healthcare-associated infections: state of the art. Curr Opin Infect Dis, 2017, 30(4): 425-431.
22. Rhee C, Kadri S, Huang SS, et al. Objective sepsis surveillance using electronic clinical data. Infect Control Hosp Epidemiol, 2016, 37(2): 163-171.
23. Arnold KE, Thompson ND. Building data quality and confidence in data reported to the National Healthcare Safety Network. Infect Control Hosp Epidemiol, 2012, 33(5): 446-448.
24. Schneeweiss S. Learning from big health care data. N Engl J Med, 2014, 370(23): 2161-2163.
25. Raghupathi W, Raghupathi V. Big data analytics in healthcare: promise and potential. Health Inf Sci Syst, 2014, 2: 3.
26. Upshur RE. Looking for rules in a world of exceptions: reflections on evidence-based practice. Perspect Biol Med, 2005, 48(4): 477-489.
27. Bi Q, Goodman KE, Kaminsky, et al. What is machine learning?A primer for the epidemiologist. Am J Epidemiol, 2019, 188(12): 2222-2239.
28. Beam AL, Kohane IS. Big data and machine learning in health care. JAMA, 2018, 319(13): 1317-1318.
29. Roth JA, Battegay M, Juchler F, et al. Introduction to machine learning in digital healthcare epidemiology. Infect Control Hosp Epidemiol, 2018, 39(12): 1457-1462.
30. Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med, 2019, 380(14): 1347-1358.
31. Althouse BM, Ng YY, Cummings DA. Prediction of dengue incidence using search query surveillance. PLoS Negl Trop Dis, 2011, 5(8): e1258.
32. Signorini A, Segre AM, Polgreen PM. The use of twitter to track levels of disease activity and public concern in the U. S. during the influenza a H1N1 pandemic. PLoS One, 2011, 6(5): e19467.
33. Oh J, Makar M, Fusco C, et al. A generalizable, data-driven approach to predict daily risk of clostridium difficile infection at two large academic health centers. Infect Control Hosp Epidemiol, 2018, 39(4): 425-433.
34. Colborn KL, Bronsert M, Amioka E, et al. Identification of surgical site infections using electronic health record data. Am J Infect Control, 2018, 46(11): 1230-1235.
35. Sanger PC, Granich M, Olsen-Scribner R, et al. Electronic surveillance for catheter-associated urinary tract infection using natural language processing. AMIA Annu Symp Proc, 2017, 2017: 1507-1516.
36. Fujikura Y, Hamamoto T, Kanayama A, et al. Bayesian reconstruction of a vancomycin-resistant enterococcus transmission route using epidemiologic data and genomic variants from whole genome sequencing. J Hosp Infect, 2019, 103(4): 395-403.
37. Liao YH, Wang ZC, Zhang FG, et al. Machine learning methods applied to predict ventilator-associated pneumonia with pseudomonas aeruginosa infection via sensor array of electronic nose in intensive care unit. Sensors (Basel), 2019, 19(8): E1866.
38. Leetaru K. How Twitter corrupted microsoft’s tay: a crash course in the dangers of AI in the real world. (2017-09-27)[2020-02-01]. https://www.forbes.com/sites/kalevleetaru/2016/03/24/how-twitter-corrupted-microsofts-tay-acrash-course-in-the-dangers-of-ai-in-the-real-world/4f39fd926d2.

West China Medical Journal

Prevention and control of healthcare-associated infection in information age

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