Simulation modeling and analysis of a multi-resource medical clinic.

Healthy for Life is a relatively new University of Louisville medical clinic which attempts to stem the epidemic of childhood obesity. This program offers a range of face-to-face services for overweight children. The main problem addressed by this research is the no show rate (nearly 50%) of the clinic. There are two goals of this thesis. One is to increase the staff utilization; the other is to decrease the waiting time. In this thesis, we study two potential methods to solve this problem. One involves using multiple resources for every visit; the other involves overbooking the patients. Two simulation models were developed for studying the system. One is an overbooking model in which the interarrival times are controlled for each type of patients. By increasing arrival rate of patients I the waiting time I the total number of served patients and the utilization of staff are increased. We need to trade off in order to choose the best arrival rate for the clinic. The second model involves using multiple resources for every visit. Each time a returning patient can visit one or two staff personnel depending on their willingness. We also change the interarrival time for patients in order to estimate the best values for these inputs

System Dynamics Modelling for Public Health: An Application in Childhood Obesity

Obesity remains a significant public health challenge in Australia and internationally. Addressing childhood obesity prevention is a crucial strategy for public health, as it increases the immediate health of youth and reduces the risk of developing chronic conditions in adulthood. The factors contributing to child and adolescent obesity are complex and intersect with various individual, familial, community and societal factors. The complexity of child and adolescent obesity creates challenges for evidence-based public health and decision-making. Systems science approaches are essential to explore the interactions between obesity drivers and public health policy, aiming to promote a healthier population. The primary aim of this thesis was to develop a system dynamics model capturing population-level determinants influencing child and adolescent obesity using existing data and expert knowledge. Using this model, the thesis investigated the impact of public health strategies to reduce or prevent child and adolescent obesity. Additional methodological aims included examining methods that verify and build confidence in public health system dynamics models, as well as exploring how data uncertainty affects system dynamics modelling and its role in decision support. This thesis contributes in two ways. Firstly, developing a system dynamic model has important implications for using and applying evidence-based public health methods for childhood obesity research, policy design and implementation. Secondly, this thesis offers a practical example of approaches to model validation and the quantification of model uncertainty that could be used in other forms of system dynamics modelling