Date(s) - February 05, 2021
Timothy C.Y. Chan, Ph.D.
Canada Research Chair in Novel Optimization and Analytics in Health
Professor, Mechanical and Industrial Engineering
Director, Centre for Healthcare Engineering
Director, Centre for Analytics and AI Engineering
University of Toronto
Abstract: An Inverse Optimization Approach to Measuring Clinical Pathway Concordance
Abstract: Clinical pathways outline standardized processes in the delivery of care for a specific disease. Patient journeys through the healthcare system, though, can deviate substantially from these pathways. Given the positive benefits of clinical pathways, it is important to measure the concordance of patient pathways so that variations in health system performance or bottlenecks in the delivery of care can be detected, monitored, and acted upon. This paper proposes the first data-driven inverse optimization approach to measuring pathway concordance in any problem context. Our specific application considers clinical pathway concordance for stage III colon cancer. We develop a novel concordance metric and demonstrate using real patient data from Ontario, Canada that it has a statistically significant association with survival. Our methodological approach considers a patient’s journey as a walk in a directed graph, where the costs on the arcs are derived by solving an inverse shortest path problem. The inverse optimization model uses two sources of information to find the arc costs: reference pathways developed by a provincial cancer agency (primary) and data from real-world patient-related activity from patients with both positive and negative clinical outcomes (secondary). Thus, our inverse optimization framework extends existing models by including data points of both varying “primacy” and “alignment”. Data primacy is addressed through a two-stage approach to imputing the cost vector, while data alignment is addressed by a hybrid objective function that aims to minimize and maximize suboptimality error for different subsets of input data.
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Meeting ID: 993 4735 4264