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Am J Infect Control. 1992 Feb;20(1):4-10.

Computerized identification of patients at high risk for hospital-acquired infection.

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1
Department of Medical Informatics, LDS Hospital, Salt Lake City, UT 84143.

Abstract

Surveillance for hospital-acquired infections is required in U.S. hospitals, and statistical methods have been used to predict the risk of infection. We used the HELP (Health Evaluation through Logical Processing) Hospital Information System at LDS Hospital to develop computerized methods to identify and verify hospital-acquired infections. The criteria for hospital-acquired infection are standardized and based on the guidelines of the Study of the Efficacy of Nosocomial Infection Control and the Centers for Disease Control. The computer algorithms are automatically activated when key items of information, such as microbiology results, are reported. Computer surveillance identified more hospital-acquired infections than did traditional methods and has replaced manual surveillance in our 520-bed hospital. Data on verified hospital-acquired infections are electronically transferred to a microcomputer to facilitate outbreak investigation and the generation of reports on infection rates. Recently, we used the HELP system to employ statistical methods to automatically identify high-risk patients. Patient data from more than 6000 patients were used to develop a high-risk equation. Stepwise logistic regression identified 10 risk factors for nosocomial infection. The HELP system now uses this logistic-regression equation to monitor and determine the risk status for all hospitalized patients each day. The computer notifies infection control practitioners each morning of patients who are newly classified as being at high risk. Of 605 hospital-acquired infections during a 6-month period, 472 (78%) occurred in high-risk patients, and 380 (63%) were predicted before the onset of infection. Computerized regression equations to identify patients at risk of having hospital-acquired infections can help focus prevention efforts.

PMID:
1554148
[Indexed for MEDLINE]
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