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The Department of Management Science and Engineering is proud to share that the paper, “Optimizing Surveillance Protocols for Hospital-Acquired Infections: Case of MRSA”, co-authored by our PhD student Esma Akgun, Associate Professor Safe Erenay, Associate Professor Sibel Alumur Alev, and Dr. William Ciccotelli (MD) of Grand River Hospital, has received an Honorable Mention (Top 4 Finalist) at the POMS College of Healthcare Operations Management (CHOM) Best Paper Award.

This year’s competition saw 55 submissions, with 46 eligible papers. Among these, four finalists were selected from prestigious institutions including Columbia University, Georgia Tech, Miami University, and our team. We are honored to have received an Honorable Mention in this prestigious competition.

About the Paper
The paper addresses the challenge of hospital-acquired Methicillin-resistant Staphylococcus aureus (MRSA), which affects approximately 20,000 Canadians annually. Due to its antibiotic resistance, MRSA outbreaks impose serious health risks and operational burdens on healthcare facilities. Our study develops a stochastic dynamic programming model to optimize testing and isolation strategies for individuals exposed to MRSA in hospitals. The model employs a Hidden Markov Chain embedded into an overall Markov decision process to dynamically incorporate stochastic disease progression and test results, imperfect test accuracies, and room settings to guide efficient surveillance.

Key contributions of the study include development of:

• An adaptable optimization framework for MRSA surveillance.
• Integration of real-world data from a Canadian community hospital.
• Evidence-based testing protocols reduce, loss of quality-adjusted life years by 18.9%, MRSA colonizations by 3.4%, and overall costs by 36.3% compared to current guidelines.

The framework also supports broader applications to other healthcare-associated infections, offering critical insights for policymakers and healthcare providers.

Professor Mehrdad Pirnia was recently featured in an interview on CBC Radio’s The Morning Edition – K-W. In the segment, Professor Pirnia discussed the promising future of short-range electric aircraft, including new research and battery testing that could make regional electric flights a reality sooner than expected.

A paper co-authored by Management Science and Engineering professor Lukasz Golab and his Data Science Master’s student Anastasiia Avksientieva won the best demo paper award at the 2025 International Conference on Extending Data Base Technology (EDBT).  This paper proposed a new data-driven method to assess bias in machine learning models.  A model is explicitly biased if it is more accurate for some subgroups than others.  For example, a biased healthcare model might generate more accurate diagnoses for younger or older individuals.  However, even an explicitly unbiased model may be implicitly biased if it is harder for some subgroups to flip the model's decision to a favourable one.  For example, what if married individuals whose loan applications were rejected would only need to increase their incomes by an average of ten percent to be approved, but single individuals would need 20 percent higher salaries?  In their paper, Golab and co-authors present a software tool that identifies implicit bias in prediction models, toward responsible deployment of AI models in practice.