The focus of my research is healthcare operations management, although I also pursue any quantitative research question that I find intriguing. Currently, I am most interested in issues relating to healthcare-associated infections, including pathogen flows, the psychology of hand-hygiene compliance, and aligning incentives in a game theoretic setting. Game theory has also been the primary approach I have used to study ambulance diversions. In addition to such standard economic tools, I like to apply a variety of tools to best model any given phenomenon, including stochastics, optimization, optimal control, simulation, and network models.
Complementing the modeling aspect of healthcare operations management, I spend considerable time with healthcare professionals in order to gather ideas and data, and to ensure the research problems are significant. I have on-going empirical projects with two groups at the University of Alberta Hospital, as well as other hospitals in Alberta and the US. I have been involved with establishing the Centre for Effective Business Management of Addiction Treatment at the Alberta School of Business, and we are initiating work on the bottlenecks hindering methadone treatment for opiate dependency. My overall goal is to contribute to the body of knowledge that can make healthcare provision more rational, effective, and efficient, combining management science modeling techniques with statistical analysis of real-world data.
I teach data analysis and modeling, with a heavy emphasis on business statistics, at the MBA level. The primary difficulty students have at this level is making data analysis relevant to themselves and their careers, as well as understanding basic probability theory and the fundamental logic of inferential statistics. To tie the course into the real world, we have a structured discussion of articles, so that students can find the strengths and weaknesses of data analysis, and how they may relate such information to decisions they will be expected to make. The class has multiple problem sets to make sure students get ample training, and a hand-written, comprehensive, and challenging final exam. The key goal of the course is to provide students with the foundation required to succeed in the MBA program, and to become informed producers and consumers of statistics in their subsequent careers.
Synergy Between Research and Teaching
Since I teach statistics, it is easy to use examples from my research to illustrate problems with hypothesis testing, confidence intervals, or other inferential statistics. Conversely, students will sometimes provide ideas for me to test, or feedback about how they regard certain issues, which will then spark new research questions. More directly, as part of a team of three, I developed VBA modules to help teach statistics, tested the tools with the help of the classes, and published the results in three pedagogical papers.
This course is concerned with methods used to predict the uncertain nature of business trends in an effort to help managers make better decisions and plans. Such efforts often involve the study of historical data and manipulation of these data to search for patterns that can be effectively extrapolated to produce forecasts. This is a business statistics course that covers all aspects of business forecasting where the emphasis is on intuitive concepts and applications. Topics covered include the family of exponential smoothing methods, decomposition methods, dynamic regression methods, Box-Jenkins methods and judgmental forecasting methods (e.g. the Delphi method). Because forecasting is best taught through practice, the course contains numerous real, relevant, business oriented case studies and examples that students can use to practice the application of concepts. Prerequisites: MGTSC 312, MGTSC 352 or OM 352.Winter Term 2021
Normally restricted to third- and fourth- year Business students. Prerequisites: MGTSC 312 or consent of Department. Additional prerequisites may be required.Winter Term 2021
This course begins with a survey of graphical and numerical techniques available for studying and describing data. Following an introduction to probability distributions, an overview of statistical inference for means and proportions is provided. Regression, analysis of variance and decision analysis are then utilized to analyze data and support decision making. Time series models are also briefly discussed. The data and decisions analyzed throughout the course will be representative of those commonly encountered by managers. During the required lab sessions, spreadsheet analysis of data, Monte Carlo simulation and the use of software for statistical analysis will be presented. Not open to students who have completed MGTSC 511 and MGTSC 521.Fall Term 2020
The merging of massive data-sets with analytical tools from Statistics, Computer Science, and Operations Research has created the emerging field of analytics. Methods are developing rapidly based on statistical platforms such as SAS and R, or more general purpose programming tools such as Python. This course will build on the basis from MGTSC 501 to provide an overview of Big Data and analytics, and develop programming and methodological skills to acquire, analyze, and present analysis. Prerequisite: MGTSC 501.Winter Term 2021