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Surveillance, Reporting, Automation, and Interventional Epidemiology

Published online by Cambridge University Press:  02 January 2015

John P. Burke
John P. Burke*
Affiliation:
Department of Clinical Epidemiology and Infectious Diseases, LDS Hospital, and the Department of Medicine, University of Utah School of Medicine, Salt Lake City, Utah
*
LDS Hospital Medical Office Building, Suite 204, 370-9th Avenue, Salt Lake City, UT 84103
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Abstract

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Type
Editorials
Information
Infection Control & Hospital Epidemiology , Volume 24 , Issue 1 , January 2003 , pp. 10 - 12
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2003

References

1.Gaynes, RP, Richards, C, Edwards, J, et al. Feeding back surveillance data to prevent hospital-acquired infections. Emerg Infect Dis 2001;7:295–298.Google Scholar
2.Eickhoff, TC. General comments on the Study on the Efficacy of Nosocomial Infection Control (SENIC Project). Am J Epidemiol 1980:111:465–469.Google Scholar
3.Haley, RW, Quade, D, Freeman, HE, et al. Study on the Efficacy of Nosocomial Infection Control (SENIC Project): summary of study design. Am J Epidemiol 1980;111:472–485.CrossRefGoogle Scholar
4.Haley, RW, Shachtman, RH. The emergence of infection surveillance and control programs in US hospitals: an assessment, 1976. Am J Epidemiol 1980;111:574–591.Google Scholar
5.Haley, RW, Culver, DH, White, JW, et al. The efficacy of infection surveillance and control programs in preventing nosocomial infections in US hospitals. Am J Epidemiol 1985;121:182–205.CrossRefGoogle ScholarPubMed
6.Leape, LL. Reporting of adverse events. N Engl J Med 2002:347:1633–1638.Google Scholar
7.Hierholzer, WJ. Health care data, the epidemiologist's sand: comments on the quantity and quality of data. Am J Med 1991;91(suppl 3B):3B–21S-3B-26S.CrossRefGoogle ScholarPubMed
8.Centers for Disease Control and Prevention. Monitoring hospital-acquired infections to promote patient safety: United States, 1990-1999. MMWR 2000;49:149–153.Google Scholar
9.Gaunt, PN, Phillips, I. Computers and hospital infection. J Hosp Infect 1987;9:106–109.Google Scholar
10.Brown, SM, Benneyan, JC, Theobald, DA, et al. Use of binary cumulative sums and moving averages in nosocomial infection cluster detection. Emerg Infect Dis 2002. Available at www.cdc.gov/ncidod/EID/vol8nol2/01-0514.htm.Google Scholar
11.Institute of Medicine. To Err Is Human. Washington, DC: National Academy Press; 1999.Google Scholar
12.Fridkin, SK, Steward, CD, Edwards, JR, et al. Surveillance of antimicrobial use and antimicrobial resistance in United States hospitals: Project ICARE phase 2. Clin Infect Dis 1999;29:245–252.CrossRefGoogle ScholarPubMed
13.Obasanjo, O, Perl, T. Cost-benefit and effectiveness of nosocomial surveillance methods. Curr Clin Top Infect Dis 2001:21:391–406.Google Scholar
14.Institute of Medicine. The Computer-Based Patient Record: An Essential Technology for Health Care. Washington, DC: National Academy Press; 1991.Google Scholar
15.Benson, T. Why general practitioners use computers and hospital doctors do not: Part 1. Incentives; Part 2. Scalability. BMJ 2002;325:1086–1093.Google Scholar
16.Best, WR, Khuri, SF, Phelan, M, et al. Identifying patient preoperative risk factors and postoperative adverse events in administrative databases: results from the Department of Veterans Affairs National Surgical Quality Improvement Program. J Am Coll Surg 2002;194:257–266.Google Scholar
17.Burke, JP, Pestotnik, SL. Antibiotic resistance: the combat zone. Curr Opin Infect Dis 1998;11:441–443.Google Scholar
18.Calfee, DP, Farr, BM. Infection control and cost control in the era of managed care. Infect Control Hosp Epidemiol 2002;23:407–410.Google Scholar
19.Langmuir, AD. Evolution of the concept of surveillance in the United States. Proc R Soc Med 1971;64:681–684.Google Scholar
20.Platt, R. Progress in surgical-site infection surveillance. Infect Control Hosp Epidemiol 2002;23:361–363.Google Scholar
21.Eickhoff, TC. Nosocomial infections—a 1980 view: progress, priorities and prognosis. Am J Med 1981;70:381–388.Google Scholar
22.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;24:51–55.Google Scholar
23.Jenders, RA, Shah, A. Challenges in using the Arden syntax for computer-based nosocomial infection surveillance. J Am Med Inform Assoc 2002;9(suppl 6):S98–S101.Google Scholar
24.Peterson, LR, Brossette, SE. Hunting health care-associated infections from the clinical microbiology laboratory: passive, active, and virtual surveillance. J Clin Microbiol 2002;40:14.Google Scholar
25.Brossette, SE, Sprague, AP, Hardin, JM, Waites, KB, Jones, WT, Moser, SA. Association rules and data mining in hospital infection control and public health surveillance. J Am Med Inform Assoc 1998;5:373–381.Google Scholar
26.Ford-Jones, EL, Mindorff, CM, Pollock, E, et al. Evaluation of a new method of detection of nosocomial infection in the pediatric intensive care unit: the infection control sentinel sheet system. Infect Control Hosp Epidemiol 1989;10:515–520.Google Scholar