Systematic Review Open Access
Like 0



Surveillance of healthcare-associated infections (HAI) is the basis of each infection control programme and, in case of acute care hospitals, should ideally include all hospital wards, medical specialties as well as all types of HAI. Traditional surveillance is labour intensive and electronically assisted surveillance systems (EASS) hold the promise to increase efficiency.


To give insight in the performance characteristics of different approaches to EASS and the quality of the studies designed to evaluate them.


In this systematic review, online databases were searched and studies that compared an EASS with a traditional surveillance method were included. Two different indicators were extracted from each study, one regarding the quality of design (including reporting efficiency) and one based on the performance (e.g. specificity and sensitivity) of the EASS presented.


A total of 78 studies were included. The majority of EASS (n = 72) consisted of an algorithm-based selection step followed by confirmatory assessment. The algorithms used different sets of variables. Only a minority (n = 7) of EASS were hospital-wide and designed to detect all types of HAI. Sensitivity of EASS was generally high (> 0.8), but specificity varied (0.37–1). Less than 20% (n = 14) of the studies presented data on the efficiency gains achieved.


Electronically assisted surveillance of HAI has yet to reach a mature stage and to be used routinely in healthcare settings. We recommend that future studies on the development and implementation of EASS of HAI focus on thorough validation, reproducibility, standardised datasets and detailed information on efficiency.


Article metrics loading...

Loading full text...

Full text loading...



  1. Zingg W, Holmes A, Dettenkofer M, Goetting T, Secci F, Clack L, et al. , systematic review and evidence-based guidance on organization of hospital infection control programmes (SIGHT) study group. Hospital organisation, management, and structure for prevention of health-care-associated infection: a systematic review and expert consensus. Lancet Infect Dis. 2015;15(2):212-24.  https://doi.org/10.1016/S1473-3099(14)70854-0  PMID: 25467650 
  2. Storr J, Twyman A, Zingg W, Damani N, Kilpatrick C, Reilly J, et al. , WHO Guidelines Development Group. Core components for effective infection prevention and control programmes: new WHO evidence-based recommendations. Antimicrob Resist Infect Control. 2017;6(1):6.  https://doi.org/10.1186/s13756-016-0149-9  PMID: 28078082 
  3. Du M, Xing Y, Suo J, Liu B, Jia N, Huo R, et al. Real-time automatic hospital-wide surveillance of nosocomial infections and outbreaks in a large Chinese tertiary hospital. BMC Med Inform Decis Mak. 2014;14(1):9.  https://doi.org/10.1186/1472-6947-14-9  PMID: 24475790 
  4. Brossette SE, Hacek DM, Gavin PJ, Kamdar MA, Gadbois KD, Fisher AG, et al. A laboratory-based, hospital-wide, electronic marker for nosocomial infection: the future of infection control surveillance? Am J Clin Pathol. 2006;125(1):34-9.  https://doi.org/10.1309/502AUPR8VE67MBDE  PMID: 16482989 
  5. Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gøtzsche PC, Ioannidis JP, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. BMJ. 2009;339(jul21 1):b2700.  https://doi.org/10.1136/bmj.b2700  PMID: 19622552 
  6. Bramer WM, de Jonge GB, Rethlefsen ML, Mast F, Kleijnen J. A systematic approach to searching: an efficient and complete method to develop literature searches. J Med Libr Assoc. 2018;106(4):531-41.  https://doi.org/10.5195/JMLA.2018.283  PMID: 30271302 
  7. Bramer WM, Rethlefsen ML, Mast F, Kleijnen J. Evaluation of a new method for librarian-mediated literature searches for systematic reviews. Res Synth Methods. 2018;9(4):510-20.  https://doi.org/10.1002/jrsm.1279  PMID: 29073718 
  8. Bramer WM. Reference checking for systematic reviews using Endnote. J Med Libr Assoc. 2018;106(4):542-6.  https://doi.org/10.5195/JMLA.2018.489  PMID: 30271303 
  9. Centers for Disease Control and Prevention (CDC)/National Healthcare Safety Network (NHSN). Surveillance Definitions. Atlanta: CDC; January 2020. Available from: https://www.cdc.gov/nhsn/pdfs/pscmanual/pcsmanual_current.pdf
  10. Govindan M, Van Citters AD, Nelson EC, Kelly-Cummings J, Suresh G. Automated detection of harm in healthcare with information technology: a systematic review. Qual Saf Health Care. 2010;19(5):e11. PMID: 20671081 
  11. Apte M, Landers T, Furuya Y, Hyman S, Larson E. Comparison of two computer algorithms to identify surgical site infections. Surg Infect (Larchmt). 2011;12(6):459-64.  https://doi.org/10.1089/sur.2010.109  PMID: 22136489 
  12. Baker C, Luce J, Chenoweth C, Friedman C. Comparison of case-finding methodologies for endometritis after cesarean section. Am J Infect Control. 1995;23(1):27-33.  https://doi.org/10.1016/0196-6553(95)90005-5  PMID: 7762871 
  13. Bearman GML, Oppenheim MI, Mendonca EA, Hupert N, Behta M, Christos PJ, et al. A Clinical Predictive Model for Catheter Related Bloodstream Infections from the Electronic Medical Record. Open Epidemiology.2010;3(1):24-8.  https://doi.org/10.2174/1874297101003010024 
  14. Bellini C, Petignat C, Francioli P, Wenger A, Bille J, Klopotov A, et al. Comparison of automated strategies for surveillance of nosocomial bacteremia. Infect Control Hosp Epidemiol. 2007;28(9):1030-5.  https://doi.org/10.1086/519861  PMID: 17932822 
  15. Blacky A, Mandl H, Adlassnig KP, Koller W. Fully Automated Surveillance of Healthcare-Associated Infections with MONI-ICU: A Breakthrough in Clinical Infection Surveillance. Appl Clin Inform. 2011;2(3):365-72.  https://doi.org/10.4338/ACI-2011-03-RA-0022  PMID: 23616883 
  16. Bolon MK, Hooper D, Stevenson KB, Greenbaum M, Olsen MA, Herwaldt L, et al. , Centers for Disease Control and Prevention Epicenters Program. Improved surveillance for surgical site infections after orthopedic implantation procedures: extending applications for automated data. Clin Infect Dis. 2009;48(9):1223-9.  https://doi.org/10.1086/597584  PMID: 19335165 
  17. Bouam S, Girou E, Brun-Buisson C, Karadimas H, Lepage E. An intranet-based automated system for the surveillance of nosocomial infections: prospective validation compared with physicians’ self-reports. Infect Control Hosp Epidemiol. 2003;24(1):51-5.  https://doi.org/10.1086/502115  PMID: 12558236 
  18. Bouzbid S, Gicquel Q, Gerbier S, Chomarat M, Pradat E, Fabry J, et al. Automated detection of nosocomial infections: evaluation of different strategies in an intensive care unit 2000-2006. J Hosp Infect. 2011;79(1):38-43.  https://doi.org/10.1016/j.jhin.2011.05.006  PMID: 21742413 
  19. Branch-Elliman W, Strymish J, Itani KM, Gupta K. Using clinical variables to guide surgical site infection detection: a novel surveillance strategy. Am J Infect Control. 2014;42(12):1291-5.  https://doi.org/10.1016/j.ajic.2014.08.013  PMID: 25465259 
  20. Branch-Elliman W, Strymish J, Kudesia V, Rosen AK, Gupta K. Natural language processing for real-time catheter-associated urinary tract infection surveillance: Results of a pilot implementation trial. Infect Control Hosp Epidemiol. 2015;36(9):1004-10.  https://doi.org/10.1017/ice.2015.122  PMID: 26022228 
  21. Broderick A, Mori M, Nettleman MD, Streed SA, Wenzel RP. Nosocomial infections: validation of surveillance and computer modeling to identify patients at risk. Am J Epidemiol. 1990;131(4):734-42.  https://doi.org/10.1093/oxfordjournals.aje.a115558  PMID: 2180283 
  22. Brown C, Richards M, Galletly T, Coello R, Lawson W, Aylin P, et al. Use of anti-infective serial prevalence studies to identify and monitor hospital-acquired infection. J Hosp Infect. 2009;73(1):34-40.  https://doi.org/10.1016/j.jhin.2009.05.020  PMID: 19647890 
  23. Chalfine A, Cauet D, Lin WC, Gonot J, Calvo-Verjat N, Dazza FE, et al. Highly sensitive and efficient computer-assisted system for routine surveillance for surgical site infection. Infect Control Hosp Epidemiol. 2006;27(8):794-801.  https://doi.org/10.1086/506393  PMID: 16874638 
  24. Chang YJ, Yeh ML, Li YC, Hsu CY, Lin CC, Hsu MS, et al. Predicting hospital-acquired infections by scoring system with simple parameters. PLoS One. 2011;6(8):e23137.  https://doi.org/10.1371/journal.pone.0023137  PMID: 21887234 
  25. Choudhuri JA, Pergamit RF, Chan JD, Schreuder AB, McNamara E, Lynch JB, et al. An electronic catheter-associated urinary tract infection surveillance tool. Infect Control Hosp Epidemiol. 2011;32(8):757-62.  https://doi.org/10.1086/661103  PMID: 21768758 
  26. Claridge JA, Golob JF Jr, Fadlalla AM, D’Amico BM, Peerless JR, Yowler CJ, et al. Who is monitoring your infections: shouldn’t you be? Surg Infect (Larchmt). 2009;10(1):59-64.  https://doi.org/10.1089/sur.2008.056  PMID: 19250007 
  27. de Bruin JS, Adlassnig KP, Blacky A, Mandl H, Fehre K, Koller W. Effectiveness of an automated surveillance system for intensive care unit-acquired infections. J Am Med Inform Assoc. 2013;20(2):369-72.  https://doi.org/10.1136/amiajnl-2012-000898  PMID: 22871398 
  28. De Bus L, Diet G, Gadeyne B, Leroux-Roels I, Claeys G, Steurbaut K, et al. Validity analysis of a unique infection surveillance system in the intensive care unit by analysis of a data warehouse built through a workflow-integrated software application. J Hosp Infect. 2014;87(3):159-64.  https://doi.org/10.1016/j.jhin.2014.03.010  PMID: 24856115 
  29. Evans RS, Burke JP, Classen DC, Gardner RM, Menlove RL, Goodrich KM, et al. Computerized identification of patients at high risk for hospital-acquired infection. Am J Infect Control. 1992;20(1):4-10.  https://doi.org/10.1016/S0196-6553(05)80117-8  PMID: 1554148 
  30. Evans RS, Larsen RA, Burke JP, Gardner RM, Meier FA, Jacobson JA, et al. Computer surveillance of hospital-acquired infections and antibiotic use. JAMA. 1986;256(8):1007-11.  https://doi.org/10.1001/jama.1986.03380080053027  PMID: 3735626 
  31. Gerbier-Colomban S, Bourjault M, Cêtre JC, Baulieux J, Metzger MH. Evaluation study of different strategies for detecting surgical site infections using the hospital information system at Lyon University Hospital, France. Ann Surg. 2012;255(5):896-900.  https://doi.org/10.1097/SLA.0b013e31824e6f4f  PMID: 22415422 
  32. Graham PL 3rd, San Gabriel P, Lutwick S, Haas J, Saiman L. Validation of a multicenter computer-based surveillance system for hospital-acquired bloodstream infections in neonatal intensive care departments. Am J Infect Control. 2004;32(4):232-4.  https://doi.org/10.1016/j.ajic.2003.07.008  PMID: 15175620 
  33. Haas JP, Mendonça EA, Ross B, Friedman C, Larson E. Use of computerized surveillance to detect nosocomial pneumonia in neonatal intensive care unit patients. Am J Infect Control. 2005;33(8):439-43.  https://doi.org/10.1016/j.ajic.2005.06.008  PMID: 16216656 
  34. Hautemanière A, Florentin A, Hunter PR, Bresler L, Hartemann P. Screening for surgical nosocomial infections by crossing databases. J Infect Public Health. 2013;6(2):89-97.  https://doi.org/10.1016/j.jiph.2012.08.002  PMID: 23537821 
  35. FitzHenry F, Murff HJ, Matheny ME, Gentry N, Fielstein EM, Brown SH, et al. Exploring the frontier of electronic health record surveillance: the case of postoperative complications. Med Care. 2013;51(6):509-16.  https://doi.org/10.1097/MLR.0b013e31828d1210  PMID: 23673394 
  36. Hirschhorn LR, Currier JS, Platt R. Electronic surveillance of antibiotic exposure and coded discharge diagnoses as indicators of postoperative infection and other quality assurance measures. Infect Control Hosp Epidemiol. 1993;14(1):21-8.  https://doi.org/10.2307/30146509  PMID: 8432965 
  37. Hollenbeak CS, Boltz MM, Nikkel LE, Schaefer E, Ortenzi G, Dillon PW. Electronic measures of surgical site infection: implications for estimating risks and costs. Infect Control Hosp Epidemiol. 2011;32(8):784-90.  https://doi.org/10.1086/660870  PMID: 21768762 
  38. Hsu HE, Shenoy ES, Kelbaugh D, Ware W, Lee H, Zakroysky P, et al. An electronic surveillance tool for catheter-associated urinary tract infection in intensive care units. Am J Infect Control. 2015;43(6):592-9.  https://doi.org/10.1016/j.ajic.2015.02.019  PMID: 25840717 
  39. Inacio MC, Paxton EW, Chen Y, Harris J, Eck E, Barnes S, et al. Leveraging electronic medical records for surveillance of surgical site infection in a total joint replacement population. Infect Control Hosp Epidemiol. 2011;32(4):351-9.  https://doi.org/10.1086/658942  PMID: 21460486 
  40. Kaiser AM, de Jong E, Evelein-Brugman SF, Peppink JM, Vandenbroucke-Grauls CM, Girbes AR. Development of trigger-based semi-automated surveillance of ventilator-associated pneumonia and central line-associated bloodstream infections in a Dutch intensive care. Ann Intensive Care. 2014;4(1):40.  https://doi.org/10.1186/s13613-014-0040-x  PMID: 25646148 
  41. King C, Aylin P, Moore LS, Pavlu J, Holmes A. Syndromic surveillance of surgical site infections--a case study in coronary artery bypass graft patients. J Infect. 2014;68(1):23-31.  https://doi.org/10.1016/j.jinf.2013.08.017  PMID: 24001609 
  42. Klompas M, Kleinman K, Platt R. Development of an algorithm for surveillance of ventilator-associated pneumonia with electronic data and comparison of algorithm results with clinician diagnoses. Infect Control Hosp Epidemiol. 2008;29(1):31-7.  https://doi.org/10.1086/524332  PMID: 18171184 
  43. Klein Klouwenberg PM, van Mourik MS, Ong DS, Horn J, Schultz MJ, Cremer OL, et al. , MARS Consortium. Electronic implementation of a novel surveillance paradigm for ventilator-associated events. Feasibility and validation. Am J Respir Crit Care Med. 2014;189(8):947-55.  https://doi.org/10.1164/rccm.201307-1376OC  PMID: 24498886 
  44. Knepper BC, Young H, Jenkins TC, Price CS. Time-saving impact of an algorithm to identify potential surgical site infections. Infect Control Hosp Epidemiol. 2013;34(10):1094-8.  https://doi.org/10.1086/673154  PMID: 24018927 
  45. Knepper BC, Young H, Reese SM, Savitz LA, Price CS. Identifying colon and open reduction of fracture surgical site infections using a partially automated electronic algorithm. Am J Infect Control. 2014;42(10) Suppl;S291-5.  https://doi.org/10.1016/j.ajic.2014.05.015  PMID: 25239724 
  46. Kulaylat AN, Engbrecht BW, Rocourt DV, Rinaldi JM, Santos MC, Cilley RE, et al. Measuring Surgical Site Infections in Children: Comparing Clinical, Electronic, and Administrative Data. J Am Coll Surg. 2016;222(5):823-30.  https://doi.org/10.1016/j.jamcollsurg.2016.01.004  PMID: 27010586 
  47. Leclère B, Lasserre C, Bourigault C, Juvin ME, Chaillet MP, Mauduit N, et al. , SSI Study Group. Matching bacteriological and medico-administrative databases is efficient for a computer-enhanced surveillance of surgical site infections: retrospective analysis of 4,400 surgical procedures in a French university hospital. Infect Control Hosp Epidemiol. 2014;35(11):1330-5.  https://doi.org/10.1086/678422  PMID: 25333426 
  48. Leth RA, Møller JK. Surveillance of hospital-acquired infections based on electronic hospital registries. J Hosp Infect. 2006;62(1):71-9.  https://doi.org/10.1016/j.jhin.2005.04.002  PMID: 16099539 
  49. Leth RA, Nørgaard M, Uldbjerg N, Thomsen RW, Møller JK. Surveillance of selected post-caesarean infections based on electronic registries: validation study including post-discharge infections. J Hosp Infect. 2010;75(3):200-4.  https://doi.org/10.1016/j.jhin.2009.11.018  PMID: 20381909 
  50. Lo YS, Lee WS, Liu CT. Utilization of electronic medical records to build a detection model for surveillance of healthcare-associated urinary tract infections. J Med Syst. 2013;37(2):9923.  https://doi.org/10.1007/s10916-012-9923-2  PMID: 23321977 
  51. Mann T, Ellsworth J, Huda N, Neelakanta A, Chevalier T, Sims KL, et al. Building and validating a computerized algorithm for surveillance of ventilator-associated events. Infect Control Hosp Epidemiol. 2015;36(9):999-1003.  https://doi.org/10.1017/ice.2015.127  PMID: 26072660 
  52. Mendonça EA, Haas J, Shagina L, Larson E, Friedman C. Extracting information on pneumonia in infants using natural language processing of radiology reports. J Biomed Inform. 2005;38(4):314-21.  https://doi.org/10.1016/j.jbi.2005.02.003  PMID: 16084473 
  53. Michelson JD, Pariseau JS, Paganelli WC. Assessing surgical site infection risk factors using electronic medical records and text mining. Am J Infect Control. 2014;42(3):333-6.  https://doi.org/10.1016/j.ajic.2013.09.007  PMID: 24406258 
  54. Moro ML, Morsillo F. Can hospital discharge diagnoses be used for surveillance of surgical-site infections? J Hosp Infect. 2004;56(3):239-41.  https://doi.org/10.1016/j.jhin.2003.12.022  PMID: 15003675 
  55. Nuckchady D, Heckman MG, Diehl NN, Creech T, Carey D, Domnick R, et al. Assessment of an automated surveillance system for detection of initial ventilator-associated events. Am J Infect Control. 2015;43(10):1119-21.  https://doi.org/10.1016/j.ajic.2015.05.040  PMID: 26164766 
  56. Perdiz LB, Yokoe DS, Furtado GH, Medeiros EA. Impact of an Automated Surveillance to Detect Surgical-Site Infections in Patients Undergoing Total Hip and Knee Arthroplasty in Brazil. Infect Control Hosp Epidemiol. 2016;37(8):991-3.  https://doi.org/10.1017/ice.2016.86  PMID: 27072598 
  57. Pokorny L, Rovira A, Martín-Baranera M, Gimeno C, Alonso-Tarrés C, Vilarasau J. Automatic detection of patients with nosocomial infection by a computer-based surveillance system: a validation study in a general hospital. Infect Control Hosp Epidemiol. 2006;27(5):500-3.  https://doi.org/10.1086/502685  PMID: 16671032 
  58. Redder JD, Leth RA, Møller JK. Incidence rates of hospital-acquired urinary tract and bloodstream infections generated by automated compilation of electronically available healthcare data. J Hosp Infect. 2015;91(3):231-6.  https://doi.org/10.1016/j.jhin.2015.05.011  PMID: 26162918 
  59. Ridgway JP, Sun X, Tabak YP, Johannes RS, Robicsek A. Performance characteristics and associated outcomes for an automated surveillance tool for bloodstream infection. Am J Infect Control. 2016;44(5):567-71.  https://doi.org/10.1016/j.ajic.2015.12.044  PMID: 26899530 
  60. Rocha BH, Christenson JC, Pavia A, Evans RS, Gardner RM. Computerized detection of nosocomial infections in newborns. Proc Annu Symp Comput Appl Med Care. 1994;684-8. PMID: 7950013 
  61. Stamm AM, Bettacchi CJ. A comparison of 3 metrics to identify health care-associated infections. Am J Infect Control. 2012;40(8):688-91.  https://doi.org/10.1016/j.ajic.2012.01.033  PMID: 22727246 
  62. Stevens JP, Silva G, Gillis J, Novack V, Talmor D, Klompas M, et al. Automated surveillance for ventilator-associated events. Chest. 2014;146(6):1612-8.  https://doi.org/10.1378/chest.13-2255  PMID: 25451350 
  63. Streefkerk RHRA, Borsboom GJ, van der Hoeven CP, Vos MC, Verkooijen RP, Verbrugh HA. Evaluation of an algorithm for electronic surveillance of hospital-acquired infections yielding serial weekly point prevalence scores. Infect Control Hosp Epidemiol. 2014;35(7):888-90.  https://doi.org/10.1086/676869  PMID: 24915222 
  64. Streefkerk RHRA, Moorman PW, Parlevliet GA, van der Hoeven C, Verbrugh HA, Vos MC, et al. An automated algorithm to preselect patients to be assessed individually in point prevalence surveys for hospital-acquired infections in surgery. Infect Control Hosp Epidemiol. 2014;35(7):886-7.  https://doi.org/10.1086/676868  PMID: 24915221 
  65. Tanushi H, Kvist M, Sparrelid E. Detection of healthcare-associated urinary tract infection in Swedish electronic health records. Stud Health Technol Inform. 2014;207:330-9. PMID: 25488239 
  66. Trick WE, Zagorski BM, Tokars JI, Vernon MO, Welbel SF, Wisniewski MF, et al. Computer algorithms to detect bloodstream infections. Emerg Infect Dis. 2004;10(9):1612-20.  https://doi.org/10.3201/eid1009.030978  PMID: 15498164 
  67. Tseng YJ, Wu JH, Lin HC, Chen MY, Ping XO, Sun CC, et al. A Web-Based, Hospital-Wide Health Care-Associated Bloodstream Infection Surveillance and Classification System: Development and Evaluation. JMIR Med Inform. 2015;3(3):e31.  https://doi.org/10.2196/medinform.4171  PMID: 26392229 
  68. Tseng YJ, Wu JH, Lin HC, Chiu HJ, Huang BC, Shang RJ, et al. Rule-based healthcare-associated bloodstream infection classification and surveillance system. Stud Health Technol Inform. 2013;186:145-9. PMID: 23542986 
  69. van Mourik MS, Groenwold RH, Berkelbach van der Sprenkel JW, van Solinge WW, Troelstra A, Bonten MJ. Automated detection of external ventricular and lumbar drain-related meningitis using laboratory and microbiology results and medication data. PLoS One. 2011;6(8):e22846.  https://doi.org/10.1371/journal.pone.0022846  PMID: 21829659 
  70. van Mourik MS, Troelstra A, Moons KG, Bonten MJ. Accuracy of hospital discharge coding data for the surveillance of drain-related meningitis. Infect Control Hosp Epidemiol. 2013;34(4):433-6.  https://doi.org/10.1086/669867  PMID: 23466919 
  71. van Mourik MSM, Moons KG, van Solinge WW, Berkelbach-van der Sprenkel JW, Regli L, Troelstra A, et al. Automated detection of healthcare associated infections: external validation and updating of a model for surveillance of drain-related meningitis. PLoS One. 2012;7(12):e51509.  https://doi.org/10.1371/journal.pone.0051509  PMID: 23236510 
  72. van Mourik MSM, Troelstra A, Berkelbach van der Sprenkel JW, van der Jagt-Zwetsloot MC, Nelson JH, Vos P, et al. Validation of an automated surveillance approach for drain-related meningitis: a multicenter study. Infect Control Hosp Epidemiol. 2015;36(1):65-75.  https://doi.org/10.1017/ice.2014.5  PMID: 25627763 
  73. Venable A, Dissanaike S. Is automated electronic surveillance for healthcare-associated infections accurate in the burn unit? J Burn Care Res. 2013;34(6):591-7.  https://doi.org/10.1097/BCR.0b013e3182a2aa0f  PMID: 24121803 
  74. Wald HL, Bandle B, Richard A, Min S. Accuracy of electronic surveillance of catheter-associated urinary tract infection at an academic medical center. Infect Control Hosp Epidemiol. 2014;35(6):685-91.  https://doi.org/10.1086/529079  PMID: 24799645 
  75. Woeltje KF, Butler AM, Goris AJ, Tutlam NT, Doherty JA, Westover MB, et al. Automated surveillance for central line-associated bloodstream infection in intensive care units. Infect Control Hosp Epidemiol. 2008;29(9):842-6.  https://doi.org/10.1086/590261  PMID: 18713052 
  76. Woeltje KF, McMullen KM, Butler AM, Goris AJ, Doherty JA. Electronic surveillance for healthcare-associated central line-associated bloodstream infections outside the intensive care unit. Infect Control Hosp Epidemiol. 2011;32(11):1086-90.  https://doi.org/10.1086/662181  PMID: 22011535 
  77. Yu TH, Hou YC, Lin KC, Chung KP. Is it possible to identify cases of coronary artery bypass graft postoperative surgical site infection accurately from claims data? BMC Med Inform Decis Mak. 2014;14(1):42.  https://doi.org/10.1186/1472-6947-14-42  PMID: 24884488 
  78. Bond J, Issa M, Nasrallah A, Bahroloomi S, Blackwood RA. Comparing administrative and clinical data for central line associated blood stream infections in Pediatric Intensive Care Unit and Pediatric Cardiothoracic Intensive Care Unit. Infect Dis Rep. 2016;8(3):58-62.  https://doi.org/10.4081/idr.2016.6275 
  79. Condell O, Gubbels S, Nielsen J, Espenhain L, Frimodt-Møller N, Engberg J, et al. Automated surveillance system for hospital-acquired urinary tract infections in Denmark. J Hosp Infect. 2016;93(3):290-6.  https://doi.org/10.1016/j.jhin.2016.04.001  PMID: 27157847 
  80. Gubbels S, Nielsen J, Voldstedlund M, Kristensen B, Schønheyder HC, Ellermann-Eriksen S, et al. National automated surveillance of hospital-acquired bacteremia in Denmark using a computer algorithm. Infect Control Hosp Epidemiol. 2017;38(5):559-66.  https://doi.org/10.1017/ice.2017.1  PMID: 28274300 
  81. Hebert C, Flaherty J, Smyer J, Ding J, Mangino JE. Development and validation of an automated ventilator-associated event electronic surveillance system: A report of a successful implementation. Am J Infect Control. 2018;46(3):316-21.  https://doi.org/10.1016/j.ajic.2017.09.006  PMID: 29132696 
  82. Leal JR, Gregson DB, Church DL, Henderson EA, Ross T, Laupland KB. The Validation of a Novel Surveillance System for Monitoring Bloodstream Infections in the Calgary Zone. Can J Infect Dis Med Microbiol. 2016;2016:2935870.  https://doi.org/10.1155/2016/2935870  PMID: 27375749 
  83. 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):506-7.  https://doi.org/10.1017/ice.2016.335  PMID: 28137321 
  84. Pindyck T, Gupta K, Strymish J, Itani KM, Carter ME, Suo Y, et al. Validation of an electronic tool for flagging surgical site infections based on clinical practice patterns for triaging surveillance: Operational successes and barriers. Am J Infect Control. 2018;46(2):186-90.  https://doi.org/10.1016/j.ajic.2017.08.026  PMID: 29031434 
  85. Sips ME, Bonten MJM, van Mourik MSM. Semiautomated Surveillance of Deep Surgical Site Infections After Primary Total Hip or Knee Arthroplasty. Infect Control Hosp Epidemiol. 2017;38(6):732-5.  https://doi.org/10.1017/ice.2017.37  PMID: 28366180 
  86. Streefkerk HRA, Lede IO, Eriksson JL, Meijling MG, van der Hoeven CP, Wille JC, et al. Internal and External Validation of a Computer-Assisted Surveillance System for Hospital-Acquired Infections in a 754-Bed General Hospital in the Netherlands. Infect Control Hosp Epidemiol. 2016;37(11):1355-60.  https://doi.org/10.1017/ice.2016.159  PMID: 27488723 
  87. Wenzel RP, Streed SA. Surveillance and use of computers in hospital infection control. J Hosp Infect. 1989;13(3):217-29.  https://doi.org/10.1016/0195-6701(89)90002-9  PMID: 2567751 
  88. Peterson LR, Brossette SE. Hunting health care-associated infections from the clinical microbiology laboratory: passive, active, and virtual surveillance. J Clin Microbiol. 2002;40(1):1-4.  https://doi.org/10.1128/JCM.40.1.1-4.2002  PMID: 11773083 
  89. Leal J, Laupland KB. Validity of electronic surveillance systems: a systematic review. J Hosp Infect. 2008;69(3):220-9.  https://doi.org/10.1016/j.jhin.2008.04.030  PMID: 18550211 
  90. Freeman R, Moore LS, García Álvarez L, Charlett A, Holmes A. Advances in electronic surveillance for healthcare-associated infections in the 21st Century: a systematic review. J Hosp Infect. 2013;84(2):106-19.  https://doi.org/10.1016/j.jhin.2012.11.031  PMID: 23648216 
  91. Penz JFE, Wilcox AB, Hurdle JF. Automated identification of adverse events related to central venous catheters. J Biomed Inform. 2007;40(2):174-82.  https://doi.org/10.1016/j.jbi.2006.06.003  PMID: 16901760 
  92. 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):942-51.  https://doi.org/10.1136/amiajnl-2013-002089  PMID: 24421290 
  93. Cato KD, Cohen B, Larson E. Data elements and validation methods used for electronic surveillance of health care-associated infections: a systematic review. Am J Infect Control. 2015;43(6):600-5.  https://doi.org/10.1016/j.ajic.2015.02.006  PMID: 26042848 
  94. Rijksinstituut voor Volksgezondheid en Milieu (RIVM). Referentiecijfers 2014 t/m 2017: Prevalentieonderzoek ziekenhuizen. [Reference numbers 2014 to 2017 included: hospital prevalence study]. Dutch. Bilthoven: RIVM; Nov 2018. Available from: https://www.rivm.nl/documenten/prezies-referentiecijfers-prevalentie-2017
  95. Olsen L, Aisner D, McGinnis JM, editors. The learning healthcare system: workshop summary; Roundtable on Evidence-Based Medicine. Washington, DC: The National Academies Press; 2007. Available from: https://www.nap.edu/read/11903/chapter/1
  96. Celi LA, Marshall JD, Lai Y, Stone DJ. Disrupting Electronic Health Records Systems: The Next Generation. JMIR Med Inform. 2015;3(4):e34-34.  https://doi.org/10.2196/medinform.4192  PMID: 26500106 
  97. Evans RS, Abouzelof RH, Taylor CW, Anderson V, Sumner S, Soutter S, et al. Computer surveillance of hospital-acquired infections: a 25 year update. AMIA Annu Symp Proc. 2009;2009:178-82. PMID: 20351845 

Data & Media loading...

Supplementary data

Submit comment
Comment moderation successfully completed
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error