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dc.contributor.authorKumar, Ashwani
dc.date.accessioned2019-11-15T05:15:40Z
dc.date.available2019-11-15T05:15:40Z
dc.date.issued2019
dc.identifier.urihttp://hdl.handle.net/11343/230922
dc.description© 2019 Ashwani Kumar
dc.description.abstractRapidly growing demand and soaring costs for healthcare services in Australia and across the world are jeopardising the sustainability of government-funded healthcare systems. We need to be innovative and more efficient in delivering healthcare services in order to keep the system sustainable. In this thesis, we utilise a number of scientific tools to improve the patient flow in a surgical suite of a hospital and subsequently develop a structured approach for intelligent patient flow management. First, we analyse and understand the patient flow process in a surgical suite. Then we obtain data from the partner hospital and extract valuable information from a large database. Next, we use machine learning techniques, such as classification and regression tree analysis, random forest, and k-nearest neighbour regression, to classify patients into lower variability resource user groups and fit discrete phase-type distributions to the clustered length of stay data. We use length of stay scenarios sampled from the fitted distributions in our sequential stochastic mixed-integer programming model for tactical master surgery scheduling. Our mixed-integer programming model has the particularity that the scenarios are utilised in a chronologically sequential manner, not in parallel. Moreover, we exploit the randomness in the sample path to reduce the requirement of optimising the process for many scenarios which helps us obtain high-quality schedules while keeping the problem algorithmically tractable. Last, we model the patient flow process in a healthcare facility as a stochastic process and develop a model to predict the probability of the healthcare facility exceeding capacity the next day as a function of the number of inpatients and the next day scheduled patients, their resource user groups, and their elapsed length of stay. We evaluate the model's performance using the receiver operating characteristic curve and illustrate the computation of the optimal threshold probability by using cost-benefit analysis that helps the hospital management make decisions.
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dc.subjectHealthcare modelling
dc.subjectPatient flow
dc.subjectElective surgery scheduling
dc.subjectClassifying patients
dc.subjectPredictive modelling
dc.subjectDynamic scheduling
dc.subjectMaster surgery scheduling
dc.subjectStochastic scheduling
dc.subjectHospital bed management
dc.subjectCapacity shortage prediction
dc.subjectMachine-learning
dc.subjectMixed-integer programming
dc.subjectRegression tree analysis
dc.titleIntelligent Management of Elective Surgery Patient Flow
dc.typePhD thesis
melbourne.affiliation.departmentSchool of Mathematics and Statistics
melbourne.affiliation.facultyScience
melbourne.thesis.supervisornameMark Fackrell
melbourne.contributor.authorKumar, Ashwani
melbourne.thesis.supervisorothernamePeter Taylor
melbourne.thesis.supervisorothernameAlysson Machado Costa
melbourne.tes.fieldofresearch1010206 Operations Research
melbourne.tes.fieldofresearch2010401 Applied Statistics
melbourne.tes.fieldofresearch3010406 Stochastic Analysis and Modelling
melbourne.tes.confirmedtrue
melbourne.accessrightsOpen Access


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