Most inverse optimization models impute unspecified parameters of an objective function to
make an observed solution optimal for a given optimization problem with a fixed feasible set. …

Background: The capacity of general internal medicine (GIM) clinical teaching units has been
strained by decreasing resident supply and increasing patient demand. The objective of …

Cancer Care Ontario (CCO) has implemented multiple information technology solutions and
collected health-system data to support its programs. There is now an opportunity to …

The opioid epidemic remains a significant public health challenge in the US. A catalyst for
reducing the incidence of opioid-related harm could be the development and …

Aim To estimate the cost‐minimizing size and skill mix of a nursing resource team (NRT).
Background Nurse absences can be filled by an NRT at lower hourly cost than staffing …

Most inverse optimization models impute unspecified parameters of an objective function to
make an observed solution optimal for a given optimization problem. In this thesis, we …

The opioid epidemic remains a significant public health challenge in the US. A potential
catalyst for reducing the incidence of opioid-related harm is the development and …

This chapter discusses applications of analytics at the strategic level of health system planning
in the province of Ontario, Canada. To supplement the strategic priorities of the Ontario …

Robust optimization is a methodology for dealing with uncertainty in optimization problems.
In this thesis, we provide a deeper understanding of the properties and paradigms of robust …

Data science has always been an effective way of extracting knowledge and insights from
information in various forms. One industry that can utilize the benefits from the advances in …