Health care operational research under uncertainty and data variability

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The complexity and challenges associated with healthcare delivery has proved a fruitful area to practice operational research (OR) and industrial engineering for several decades. Research projects and outputs have advanced the science of these disciplines and improved healthcare operations. The importance of this partnership is poised to grow as healthcare providers invest more in data analytics (infrastructure and professional human resources) and continue to look to universities to help with their most complex challenges. With the explosion of data, many previously uncertain problem characteristics can now be quantified. How to take full advantage of this and how to make good decisions in the face of the high variability often found in this new data is the focus of this program of research. Specifically, the research objectives of this program of research are:****** To enhance our ability to extract information related to operational processes and patient populations from new, abundant, and disparate healthcare data sources*** To use this data and information for advancing healthcare analytics*** To use this data and information for less uncertain and improved OR models*** To compare and contrast solutions obtained from Analytics with OR models ******Once the underlying processes are understood, OR methods can be developed to make better decisions. Queueing theory and queueing network models will be used for strategic capacity planning and are well equipped to determine which investment (e.g. more resources, faster service times, rerouting of patients, etc.) will result in the most improvement. Simulation and simulation optimization will be used when there are large amounts of data and we are not limited to data described only by theoretical probability distributions (a common problem in many queueing network models). Markov models are also well suited for optimizing in uncertain settings and will be used when the underlying system dynamics are well modelled with Poisson processes. ******Analytics models are able to describe, predict and prescribe based on empirical data and without a need for a comprehensive understanding of the underlying structure. Stochastic OR models can likewise describe, predict and prescribe actions but to do so requires a more complete understanding of the underlying systems (and typically less data). As such, the objectives are similar, but the approach and the information needed is different. In studying the differences, similarities, challenges, and outputs of each, I aim to characterize problem types where each is most appropriate and to derive a unifying framework connecting OR methods and Analytics methods such that how they complement each other can be understood and used to advance both fields. Capitalizing on new data and the joint benefits of OR and Analytics methods will lead to improved healthcare delivery for Canadians.**