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dc.contributor.advisorJang, Wooseungeng
dc.contributor.authorGoodson, Justineng
dc.date.issued2005eng
dc.date.submitted2005 Summereng
dc.descriptionThe entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file.eng
dc.descriptionTitle from title screen of research.pdf file viewed on (July 13, 2006)eng
dc.descriptionIncludes bibliographical references.eng
dc.descriptionThesis (M.S.) University of Missouri-Columbia 2005.eng
dc.descriptionDissertations, Academic -- University of Missouri--Columbia -- Industrial engineering.eng
dc.description.abstractThe need to properly assess, and thus, improve the quality of care in nursing homes is a growing concern. This research proposes Bayesian belief networks (BBNs) as a modeling tool to perform such assessments. Variables were selected for inclusion in each of 11 BBNs based on prior research pertaining to the quality of care in nursing homes, stepwise variable selection in a least squares regression model, and a Chi-square test of association with the quality of care. After learning the structure and parameters of the BBNs, each model was evaluated based on its ability to accurately assess the quality of care in nursing homes. The structure and parameters of the proposed model support previous research relating structure, process, and outcome measures to the quality of care. The proposed model successfully predicted the quality of care in 58% of the validation data. A larger dataset and consideration of additional measures of the overall quality of care are recommended to improve the accuracy of the proposed model. The major accomplishments of this work include the incorporation of a wide variety of variables into a quality of care assessment model, the attainment of previously unachieved quantitative and qualitative insight into the quality of care in nursing homes, the ability to perform quality of care assessments with incomplete information using the proposed model, the capacity to distinguish among the quality of care delivery in nursing homes with like classifications, and the utilization of the BBN environment as a means of integrating various research efforts.eng
dc.identifier.merlinb55879317eng
dc.identifier.urihttp://hdl.handle.net/10355/4286
dc.languageEnglisheng
dc.publisherUniversity of Missouri--Columbiaeng
dc.relation.ispartofcollectionUniversity of Missouri--Columbia. Graduate School. Theses and Dissertationseng
dc.subject.lcshBayesian statistical decision theoryeng
dc.subject.lcshNursing home careeng
dc.titleAssessing the quality of care in nursing homes through Bayesian belief networkseng
dc.typeThesiseng
thesis.degree.disciplineIndustrial engineering (MU)eng
thesis.degree.grantorUniversity of Missouri--Columbiaeng
thesis.degree.levelMasterseng
thesis.degree.nameM.S.eng


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