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UBC Theses and Dissertations

Nursing workforce planning and radiation therapy treatment decision making : two healthcare operations research applications Lavieri, Mariel Sofia


This thesis discusses two applications of operations research to healthcare: nursing workforce planning and radiation therapy treatment decision-making. The first application describes a linear programming-based hierarchical planning tool that determines the optimal number of nurses to train, promote to managerial levels and recruit over a 20 year planning horizon to achieve nursing and managerial targets. The model is based on the age dynamics and attrition rates of the nursing workforce. The tool has been developed to assist policy makers in planning the British Columbia registered nurses workforce. Its simplicity of use makes it ideal for scenario and “What-If” analyses. The second application presents a novel approach to model individual disease progression of prostate cancer patients who receive neoadjuvant hormone therapy before radiation therapy. The model is used to help clinicians determine when to initiate radiation therapy based on a patient’s prostate specific antigen (PSA) dynamics. Each patient’s PSA dynamics is modeled by a log quadratic curve. Prior distributions for the curve parameters are obtained from population characteristics. The distribution of the time of the PSA nadir is derived from an approximation of the ratio of two correlated normal random variables. Using a dynamic Kalman filter model, parameter estimates are updated as new patient-specific information becomes available. Clustering is incorporated to improve prior estimates of curve parameters. The model trades off the risk of beginning radiation therapy too soon, before hormone therapy has achieved its maximum effect, against waiting too long to start therapy after there has been a potential increase in the number of tumor cells resistant to the treatment. We illustrate and validate our modeling approach by comparing clinically implementable policies on a cohort of prostate cancer patients, and show that our approach outperforms the current protocol by identifying earlier when radiation therapy should start for each patient. While both applications involve very different approaches, they incorporate dynamic decision-making in the field of healthcare. A deeper knowledge of the potential of the field is achieved by understanding the challenges faced and methodology used to guide decisions on a policy level as well as on a patient-specific level.

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