Applied Clinical Informatics, Table of Contents Appl Clin Inform 2013; 04(02): 212-224DOI: 10.4338/ACI-2012-12-RA-0053 Research Article Schattauer GmbH Diagnostic Performance of Electronic Syndromic Surveillance Systems in Acute Care A Systematic Review M. Kashiouris 1 Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA 2 M.E.T.R.I.C., Mayo Clinic, Rochester, Minnesota, USA , J.C. O’Horo 1 Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA 2 M.E.T.R.I.C., Mayo Clinic, Rochester, Minnesota, USA , B.W. Pickering 2 M.E.T.R.I.C., Mayo Clinic, Rochester, Minnesota, USA 3 Department of Anesthesiology, Division of Critical Care Medicine, Mayo Clinic, Rochester, Minnesota, USA , V. Herasevich 2 M.E.T.R.I.C., Mayo Clinic, Rochester, Minnesota, USA 3 Department of Anesthesiology, Division of Critical Care Medicine, Mayo Clinic, Rochester, Minnesota, USA › Author Affiliations Recommend Article Abstract Full Text PDF Download Keywords KeywordsAlert - decision support systems - EMR - false - IC - monitor - sniffers - computer systems - computer-assisted - medical informatics applications - computerized medical records systems - surveillance - diagnostic References References 1 Langmuir AD. The surveillance of communicable diseases of national importance. N Engl J Med 1963; 268: 182-192. 2 Teutsch SM, Thacker SB. Planning a public health surveillance system. Epidemiol Bull 1995; 16: 1-6. 3 Chaudhry B. et al. Systematic review: impact of health information technology on quality, efficiency, and costs of medical care. Ann Intern Med 2006; 144: 742-752. 4 Lenert L, Sundwall DN. Public health surveillance and meaningful use regulations: a crisis of opportunity. Am J Public Health 2012; 102: e1-e7. 5 Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Ann Intern Med 2009; 151: 264-269 W64. 6 Higgins JPT, Green S. Cochrane Collaboration.. Cochrane handbook for systematic reviews of interventions. Chichester, England; Hoboken, NJ: Wiley-Blackwell; 2008 7 Martus P. A measurement model of disease severity in absence of a gold standard. Methods Inf Med 2001; 40: 265-271. 8 Fàbregas N. et al. Clinical diagnosis of ventilator associated pneumonia revisited: comparative validation using immediate post-mortem lung biopsies. Thorax 1999; 54: 867-873. 9 Dwamena BA. MIDAS: A program for Meta-analytical Integration of Diagnostic Accuracy Studies in Stata. Division of Nuclear Medicine, Department of Radiology. University of Michigan Medical School, Ann Arbor; Michigan: 2007 10 Berlin A, Sorani M, Sim I. A taxonomic description of computer-based clinical decision support systems. J Biomed Inform 2006; 39: 656-667. 11 Whiting P, Harbord R, Kleijnen J. No role for quality scores in systematic reviews of diagnostic accuracy studies. BMC Med Res Methodol 2005; 5: 19. 12 Hooper MH. et al. Randomized trial of automated, electronic monitoring to facilitate early detection of sepsis in the intensive care unit. Crit Care Med 2012; 40: 2096-2101. 13 Azzam HC. et al. Validation study of an automated electronic acute lung injury screening tool. J Am Med Inform Assoc 2009; 16: 503-508. 14 Koenig HC. et al. Performance of an automated electronic acute lung injury screening system in intensive care unit patients. Crit Care Med 2011; 39: 98-104. 15 Schmickl CN. et al. Decision support tool for early differential diagnosis of acute lung injury and cardiogenic pulmonary edema in medical critically ill patients. Chest 2012; 141: 43-50. 16 Garcia-Esquirol O. et al. Validation of an automatic continuous system to detect expiratory asynchronies during mechanical ventilation. Intensive Care Medicine 2010; 36: S349. 17 Mojoli F. et al. Automatic detection of patient-ventilator asynchronies during pressure support ventilation. Intensive Care Medicine 2010; 36: S111. 18 Herasevich V. et al. Limiting ventilator-induced lung injury through individual electronic medical record surveillance. Crit Care Med 2011; 39: 34-39. 19 Slooter AJC. et al. Seizure detection in adult ICU patients based on changes in EEG synchronization likelihood. Neurocritical Care 2006; 5: 186-192. 20 Kho A. et al. Utility of commonly captured data from an EHR to identify hospitalized patients at risk for clinical deterioration. AMIA Annu Symp Proc. 2007 Annual Symposium Proceedings/AMIA Symposium.: 404-408. 21 Pokorny L. et al. Automatic detection of patients with nosocomial infection by a computer-based surveil-lance system: A validation study in a general hospital. Infection Control and Hospital Epidemiology. 2006; 27: 500-503. 22 Leth RA, Moller JK. Surveillance of hospital-acquired infections based on electronic hospital registries. J Hosp Infect 2006; 62: 71-79. 23 Bellini C. et al. Comparison of automated strategies for surveillance of nosocomial bacteremia. Infection Control & Hospital Epidemiology 2007; 28: 1030-1035. 24 Schurink CAM. et al. A Bayesian decision-support system for diagnosing ventilator-associated pneumonia. Intensive Care Medicine 2007; 33: 1379-1386. 25 Woeltje KF. et al. Automated surveillance for central line-associated bloodstream infection in intensive care units. Infection Control & Hospital Epidemiology 2008; 29: 842-846. 26 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. Infection Control & Hospital Epidemiology 2008; 29: 31-37. 27 Claridge JA. et al. Who is monitoring your infections: shouldn‘t you be?. Surg Infect 2009; 10: 59-64. 28 Wright MO, Komutanon V, Peterson LR, Robicsek A. Automated central line-associated bloodstream infection surveillance. American Journal of Infection Control 2009; 37: E176. 29 McGrane T. et al. Electronic SIRS alert facilitating recognition of sepsis by housestaff. Crit Care Med 2010; 38: A170. 30 Koller W. et al. Electronic surveillance of healthcare-associated infections with MONI-ICU –a clinical breakthrough compared to conventional surveillance systems. Stud Health Technol Inform 2010; 160: 432-436. 31 Hota B. et al. Electronic algorithmic prediction of central vascular catheter use. Infection Control & Hospital Epidemiology 2010; 31: 4-11. 32 Thiel SW. et al. Early prediction of septic shock in hospitalized patients. J Hosp Med 2010; 5: 19-25. 33 Choudhuri JA. et al. An electronic catheter-associated urinary tract infection surveillance tool. Infection Control & Hospital Epidemiology 2011; 32: 757-762. 34 Woeltje KF. et al. Electronic surveillance for healthcare-associated central line-associated bloodstream infections outside the intensive care unit. Infection Control & Hospital Epidemiology 2011; 32: 1086-1090. 35 Bouzbid S. et al. Automated detection of nosocomial infections: evaluation of different strategies in an intensive care unit 2000-2006. J Hosp Infect 2011; 79: 38-43. 36 van Gils M. et al. Using artificial neural networks for classifying ICU patient states. IEEE Eng Med Biol Mag 1997; 16: 41-47. 37 Helfenbein ED. et al. An algorithm for continuous real-time QT interval monitoring. J Electrocardiol 2006; 39: S123-S127. 38 Gharaviri A, Teshnehlab M, Moghaddam HA. Ischemia detection via ECG using ANFIS. Conf Proc IEEE Eng Med Biol Soc 2008; 2008: 1163-1166. 39 Aboukhalil A. et al. Reducing false alarm rates for critical arrhythmias using the arterial blood pressure waveform. Journal of Biomedical Informatics 2008; 41: 442-451. 40 Jonsbu J. et al. Prospective evaluation of an EDB-based diagnostic program to be used in patients admitted to hospital with acute chest pain. Eur Heart J 1993; 14: 441-446. 41 Kennedy RL. et al. An artificial neural network system for diagnosis of acute myocardial infarction (AMI) in the accident and emergency department: evaluation and comparison with serum myoglobin measurements. Comput Methods Programs Biomed 1997; 52: 93-103. 42 Eshelman LJ. et al. Development and evaluation of predictive alerts for hemodynamic instability in ICU patients. AMIA Annu Symp Proc 2008; 379-383. 43 Lorenzoni R. et al. A computer protocol to evaluate subjects with chest pain in the emergency department: a multicenter study. J Cardiovasc Med (Hagerstown) 2006; 7: 203-203. 44 Govindan M. et al. Automated detection of harm in healthcare with information technology: a systematic review. Qual Saf Health Care 2010; 19: e11. 45 Deeks JJ, Altman DG. Diagnostic tests 4: likelihood ratios. BMJ 2004; 329: 168-169. 46 HIMSS.. Overview of CDS five rights. AHRQ; 2009 [updated 2009; cited 2012 09/05/2012]; Available from: http://healthit.ahrq.gov/images/mar09_cds_book_chapter/ CDS_MedMgmnt_ch_1_sec_2_five_rights.htm. 47 Herasevich V. et al. Informatics infrastructure for syndrome surveillance, decision support, reporting, and modeling of critical illness. Mayo Clin Proc 2010; 85: 247-254. 48 Embi PJ, Leonard AC. Evaluating alert fatigue over time to EHR-based clinical trial alerts: findings from a randomized controlled study. JAMIA 2012; 19: e145-e148. Supplementary Material Supplementary Material Online Supplementary Material (PDF)