Intelligent Management of Elective Surgery Patient Flow
AffiliationSchool of Mathematics and Statistics
Document TypePhD thesis
Access StatusOpen Access
© 2019 Ashwani Kumar
Rapidly 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.
KeywordsHealthcare modelling; Patient flow; Elective surgery scheduling; Classifying patients; Predictive modelling; Dynamic scheduling; Master surgery scheduling; Stochastic scheduling; Hospital bed management; Capacity shortage prediction; Machine-learning; Mixed-integer programming; Regression tree analysis
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