Managing the cost of care: a predictive study to identify critical care patients at risk for nosocomial pneumonia

Nosocomial infections represent a major health problem and can have a significant impact on the cost of treating a patient. Hospital-acquired pneumonia (HAP) is the second most common nosocomial infection in the United States and the leading cause of death due to a nosocomial infection. The high pre...

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Bibliographic Details
Published in:	Journal of health care finance Vol. 26; no. 3; p. 73
Main Authors:	Baker, J J, Lambert, R L, Poulos, K M, Feldman, J I
Format:	Journal Article
Language:	English
Published:	United States 2000
Subjects:	Adult Cost Control Critical Care Cross Infection - economics Cross Infection - epidemiology Health Care Costs Humans Pneumonia - economics Pneumonia - epidemiology Prospective Studies Risk Assessment United States - epidemiology United States
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Summary:	Nosocomial infections represent a major health problem and can have a significant impact on the cost of treating a patient. Hospital-acquired pneumonia (HAP) is the second most common nosocomial infection in the United States and the leading cause of death due to a nosocomial infection. The high prevalence of HAP and its significant impact on increased length of stay and incremental treatment costs identify nosocomial pneumonia (NP) as a key component in managing the total cost of care. The study's objective was to develop a predictive tool for identifying those adult patients in critical care (CC) who are at greatest risk of developing NP to better manage the costs of care. The authors also expected to determine the expected probability of a patient developing NP in CC. A prospective study of longer stay critical care unit (CCU) patients was performed in nine U.S. CCUs. There were no interventions in the study. Development was based on variables common to CC and specific patient profile risk factors. Twelve statistically significant and clinically meaningful risk factors were identified and placed in a sequential cascade fashion. The positive predictive value of the sequential decision process and corresponding tool was 87.03 percent.
ISSN:	1078-6767