  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.
