A Stochastic Model to Measure Patient Effects Stemming from Hospital-Acquired Infections

We introduce a Markov chain model to represent a patient's path in terms of the number and type of infections s/he may have acquired during a hospitalization period. The model allows for categories of patient diagnoses, surgery, the four major types of nosocomial (hospital-acquired) infections,...

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Bibliographic Details
Published in:Operations research 1982-11, Vol.30 (6), p.1105-1133
Main Authors: Kastner, George T, Shachtman, Richard H
Format: Article
Language:English
Subjects:
222 predicting progress of patients with and without infections
276 hospital-based Markov chain model, effects of infections
570 Markov chain models in the hospital
Best available technology
Coinfection
Cross Infection - epidemiology
Diagnosis-Related Groups
Epidemiology
Health administration
Health Care
Hospital admissions
Hospitalization
Hospitals
Hospitals, Teaching - utilization
Humans
Infections
Length of Stay
Markov Chains
Mathematics
Measurement
Modeling
Models, Theoretical
Nosocomial infections
Operations research
Parameterization
Patients
Probability
Stochastic models
Stochastic Processes
Taboos
Transition probabilities
United States
Validation
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Description
Summary:We introduce a Markov chain model to represent a patient's path in terms of the number and type of infections s/he may have acquired during a hospitalization period. The model allows for categories of patient diagnoses, surgery, the four major types of nosocomial (hospital-acquired) infections, and discharge or death Data from a national medical records survey including 58,647 patients enable us to estimate transition probabilities and, ultimately, perform statistical tests of fit, including a validation test. Novel parameterizations (functions of the transition matrix) are introduced to answer research questions on time-dependent infection rates, time to discharge or death as a function of patient diagnostic groups and conditional infection rates reflecting intervening variables (e.g., surgery).
ISSN:0030-364X
1526-5463
DOI:10.1287/opre.30.6.1105