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Surveillance for Quality Assessment: IV. Surveillance Using a Hospital Information System

Published online by Cambridge University Press:  21 June 2016

David C. Classen*
Affiliation:
Division of Infectious Diseases, LDS Hospital and The University of Utah School of Medicine, Salt Lake City, Utah
John F! Burke
Affiliation:
Division of Infectious Diseases, LDS Hospital and The University of Utah School of Medicine, Salt Lake City, Utah
Stanley L. Pestotnik
Affiliation:
Division of Infectious Diseases, LDS Hospital and The University of Utah School of Medicine, Salt Lake City, Utah
R. Scott Evans
Affiliation:
Division of Infectious Diseases, LDS Hospital and The University of Utah School of Medicine, Salt Lake City, Utah
Lane E. Stevens
Affiliation:
Division of Infectious Diseases, LDS Hospital and The University of Utah School of Medicine, Salt Lake City, Utah
*
Division of Infectious Diseases, LDS Hospital, 8th Avenue and C Streets, Salt Lake City, UT 84143

Extract

The first two articles in this series outlined the widespread use of hospital surveillance for infection control programs and the potential use of surveillance for monitoring noninfectious nosocomial events. The third article focused on quality indicators as potential targets for hospital surveillance. Surveillance has been defined as the collection, collation, analysis, and dissemination of data. Several methods have been developed to perform this task in hospitals; the traditional method includes collection of data through extensive chart review, a very time- and labor-intensive process. Computerized methods have been developed for hospital surveillance: several personal computer-based programs in infection control are available, including NOSO 3 (Epi Systematics, Inc., Ft. Myers, Florida) and AICE (ICPA, Inc., Austin, Texas). In addition, the Centers for Disease Control (CDC) offer an IDEAS software program to facilitate collection of hospital data for inclusion in the National Nosocomial Infection Surveillance System. These systems offer added efficiencies in the analysis of data, but not in the collection of data. As surveillance in hospitals is expanded from infection control to other areas, more efficient means of data collection will be essential. The development and implementation of comprehensive hospital information systems offer the potential for improving, enlarging, and conducting more efficient hospital-wide surveillance. This article will review the hospital surveillance programs conducted with a hospital information system currently in use at LDS Hospital in Salt Lake City, Utah.

Type
Topics in Clinical Epidemiology
Copyright
Copyright © The Society for Healthcare Epidemiology of America 1991

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References

1. Crede, W, Hierholzer, WJ. Surveillance for quality assessment: I surveillance in infection control success reviewed. Infect Control Hosp Epidemiol. 1989;10:470474.CrossRefGoogle ScholarPubMed
2. McGeer, A, Crede, W, Hierholzer, WJ. Surveillance for quality assessment: II. surveillance for noninfectious processes: back to basics. Infect Control Hosp Epidemiol. 1990;11:3641.CrossRefGoogle ScholarPubMed
3. Crede, W, Hierholzer, WJ. Surveillance for quality assessment: III. the critical assessment of quality indicators. Infect Control Hosp Epidemiol. 1990;11:197201.CrossRefGoogle ScholarPubMed
4. Berg, R. Reviews: software. Am J Infect Control. 1986;14:139145.CrossRefGoogle Scholar
5. Bleich, HL, Beckly, RF, Horowitz, GI, et al. Clinical computing in a teaching hospital. N Engl /Med. 1985;312:756764.CrossRefGoogle ScholarPubMed
6. Pryor, TA, Gardner, RM, Clayton, PD, Warner, HR. The HELP System. J Med Syst. 1985;7:87102.CrossRefGoogle Scholar
7. Hammond, WE, Stead, WW. The evolution of a computerized medical information system. Proceedings of the Tenth Annual Symposium on Computer Applications in Medical Care. Washington, DC: Computer Society Press; 1986:147156.Google Scholar
8. McDonald, CJ, Blevins, L, Tierney, WM, Martin, DK. The Regen-strief medical records. M.D. Computing. 1988;5:3447.Google Scholar
9. Barnett, GO. The application of computer-based medical-record systems in ambulatory practice. N Engl J Med. 1984:310:16431650.CrossRefGoogle ScholarPubMed
10. Whiting-O'Keefe, QE, Simborg, DW, Epstein, WV, et al. A computerized summary medical record system can produce more information than the standard medical record. JAMA. 1985;254:11851192.CrossRefGoogle Scholar
11. Blum, BI. Clinical information systems-a review. West J Med. 1986;145:791797.Google ScholarPubMed
12. Barnett, GO, Cimino, JJ, Hupp, JA, Hoffer, EP. DXplain: an evolving diagnostic decision-support system. JAMA. 1987;258:6774.CrossRefGoogle ScholarPubMed
13. Miller, RA, McNeil, MA, Challinor, SM, Masarie, FE, Myers, JD. The Internist-l/Quick medical reference project: status report. West J Med. 1986;145:816822.Google ScholarPubMed
14. Yu, VL, Fagan, LM, Wraith, SM, et al. Antimicrobial selection by a computer. A blinded evaluation by infectious disease experts. JAMA. 1979;242:12791282.CrossRefGoogle Scholar
15. Blum, RL. Computer-assisted design of studies using routine clinical data. Ann Intern Med. 1986;104:858868.CrossRefGoogle ScholarPubMed
16. Shortliffe, EH. Computer programs to support clinical decision making. JAMA. 1987;258:6166.CrossRefGoogle ScholarPubMed
17. Tiemey, WM, Miller, ME, McDonald, CJ. The effect on test ordering of informing physicians of the charges for outpatient diagnostic tests. N Engl J Med. 1990;322:14991504.Google Scholar
18. Knaus, WA, Draper, EA, Wagner, DP, et al. APACHE II: a severity of disease classification system. Crit Cure Med. 1985;13:818829.CrossRefGoogle ScholarPubMed
19. Evans, RS, Gardner, RM, Bush, AR, et al. Development of a computerized infectious disease monitor. Comput Biomed Res. 1985;18:103113.CrossRefGoogle ScholarPubMed
20. Evans, RS, Larsen, RA, Burke, JP, et al. Computer surveillance of hospital-acquired infections and antibiotic use. JAMA. 1986;256:10071011.CrossRefGoogle ScholarPubMed
21. Classen, DC, Jacobson, JA, Burke, JP. Serious pseudomonas infections associated with endoscopic retrograde cholangiopan-creatography. Am J Med. 1988;84:590596.CrossRefGoogle Scholar
22. Kemodle, DS, Classen, DC, Burke, JP, Kaiser, AB. Failure of cephalosporins to prevent Staphylococcus aureus surgical wound infections. JAMA. 1990;263:961966.Google Scholar
23. Classen, DC, Burke, JP, Ford, CD, et al. Streptococcus mitis sepsis in bone marrow transplant patients receiving oral antimicrobial prophylaxis. Am J Med. 1990;89:441446.CrossRefGoogle ScholarPubMed
24. Villarrino, EB, Burke, JP, Jarvis, WE, et al. An outbreak of Xanthomonas maltophilia at a community hospital. Presented at the 90th Annual Meeting of The American Society for Microbiology. May 13-17,1990, Anaheim Calif. Abstract #L26.Google Scholar
25. Jacobson, JT, Johnson, DS, Ross, CA, Conti, MT, Evans, RS, Burke, JI? Adapting disease-specific isolation guidelines to a hospital information system. Infect Control. 1986;7:411418.CrossRefGoogle ScholarPubMed
26. Larsen, RA, Evans, RS, Burke, JP, Pestotnik, SL, Gardner, RM, Classen, DC. Improved perioperative antibiotic use and reduced surgical wound infections through use of computer decision analysis. Infect Control Hosp Epidemiol. 1989;10:316320.CrossRefGoogle ScholarPubMed
27. Evans, RS, Pestotnik, SL, Burke, JP, Gardner, RM, Larsen, RA, Classen, DC. Reducing the duration of prophylactic antibiotic use through computer monitoring of surgical patients. DZCP Ann Pharmacother. 1990;24:351354.Google ScholarPubMed
28. Pestotnik, SI, Evans, RS, Burke, JP, Gardner, RM, Classen, DC. Therapeutic antibiotic monitoring: surveillance using a computerized expert system. Am J Med. 1990;88:4348.CrossRefGoogle ScholarPubMed
29. Hulse, RK, Clark, SJ, Jackson, JC, Warner, HR, Gardner, RM. Computerized medication monitoring system. Am J Hosp Pharm. 1976;33:10611066.Google ScholarPubMed
30. Pestotnik, SL, Classen, DC, Stevens, LE, Evans, RS, Burke, JP. Prospective monitoring for seizures associated with imipenemcilastatin therapy. Presented at the 28th Interscience Conference on Antimicrobial Agents and Chemotherapy. October 23-26, 1988, Los Angeles, Calif.Google Scholar
31. Classen, DC, Pestotnik, SL, Evans, RS, Stevens, LE, Burke, JP. Midazolam use and associated respiratory arrest in a hospital population. Presented at the 4th International Conference on Pharmacoepidemiology. September 69, 1988, Minneapolis, Minn.Google Scholar
32. Joint Commission for Accreditation of Healthcare Organizations. Accreditation Manual for Hospitals. Chicago, Ill:JCAHO; 1990.Google Scholar
33. Classen, DC, Pestotnik, SL, Evans, RS, Stevens, LE, Bass, SB, Burke, JP. Concurrent monitoring of adverse drug events through the use of a hospital information system. Presented at the 6th International Conference on Pharmacoepidemiology. August 11, 1990, Anaheim, Calif.Google Scholar
34. Evans, RS, Burke, JP, Classen, DC, Goodrich, KM, Pestotnik, SL, Stevens, LE. Computerized identification of hospital acquired infections and high risk patients. Presented at the 3rd Decennial International Conference on Nosocomial Infections. July 31-August 3, 1990, Atlanta, Ga. Abstract #17.Google Scholar