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dc.contributor.advisorSavage, Grant T. (Grant Theodore), 1954-eng
dc.contributor.advisorGong, Yang, Ph. D.eng
dc.contributor.authorAlafaireet, Patricia E.eng
dc.coverage.spatialUnited Stateseng
dc.date.issued2010eng
dc.date.submitted2010 Springeng
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 PDF of title page (University of Missouri--Columbia, viewed on August 3, 2010).eng
dc.descriptionIncludes bibliographical referenceseng
dc.descriptionVita.eng
dc.descriptionThesis advisor: Grant T. Savage.eng
dc.descriptionThesis advisor: Yang Gong.eng
dc.descriptionPh. D. University of Missouri-Columbia 2010.eng
dc.descriptionDissertations, Academic -- University of Missouri--Columbia -- health informatics.eng
dc.description.abstractNon-adherence to psychiatry visits costs the US mental health care system more than one hundred billion dollars annually. Non-adherent visits undermine improvements to patient care quality, erode patient well-being, and prevent the effective use of technology driven improvements to health care quality. Psychiatric visit non-attendance is often perceived as an intractable problem, because of the direction taken in previous studies of the problem. Previous research into the issue of visit non-adherence focus either on specific patient demographics or on redundant scheduling methods, neither of which addresses quality of care issues or the development of useful tools to decrease visit non-adherence. This formative study addressed the issue of visit non-adherence by leveraging readily available electronic billing and scheduling system data, as well as data from an EMR, to identify and analyze a set of determinants of visit non-adherence. Three strategies, statistical analysis, machine learning/data mining and model comparison, were utilized in the analysis. Results from this multi-phase study provide a parsimonious set of visit non-adherence determinants and a useful model based on those determinants capable of supporting the development of predictive tools suitable for use in ambulatory health care services delivery.eng
dc.format.extentii, 238 pageseng
dc.identifier.merlinb7742976xeng
dc.identifier.merlinb7742976xeng
dc.identifier.oclc653290662eng
dc.identifier.otherAlafaireetP-051410-D3194eng
dc.identifier.urihttp://hdl.handle.net/10355/8335eng
dc.languageEnglisheng
dc.publisherUniversity of Missouri-Columbiaeng
dc.relation.ispartofcommunityUniversity of Missouri-Columbia. Graduate School. Theses and Dissertationseng
dc.subject.meshMental Health Serviceseng
dc.subject.meshAppointments and Scheduleseng
dc.subject.meshPatient Complianceeng
dc.subject.meshQuality Assurance, Health Careeng
dc.subject.meshMedical Infomatics Computingeng
dc.subject.meshForecastingeng
dc.subject.meshModels, Organizationaleng
dc.titleDeveloping a model of psychiatric visit non-adherenceeng
dc.typeThesiseng
thesis.degree.disciplineHealth informatics (MU)eng
thesis.degree.grantorUniversity of Missouri--Columbiaeng
thesis.degree.levelDoctoraleng
thesis.degree.namePh. D.eng


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