Inverse optimization on hierarchical networks: an application to breast cancer clinical pathways

Timothy C. Y. Chan (), Katharina Forster (), Steven Habbous (), Claire Holloway (), Luciano Ieraci (), Yusuf Shalaby () and Nasrin Yousefi ()
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Timothy C. Y. Chan: University of Toronto
Katharina Forster: Ontario Health (Cancer Care Ontario)
Steven Habbous: Ontario Health (Cancer Care Ontario)
Claire Holloway: Ontario Health (Cancer Care Ontario)
Luciano Ieraci: Ontario Health (Cancer Care Ontario)
Yusuf Shalaby: University of Toronto
Nasrin Yousefi: Queen’s University

Health Care Management Science, 2022, vol. 25, issue 4, No 5, 590-622

Abstract: Abstract Clinical pathways are standardized processes that outline the steps required for managing a specific disease. However, patient pathways often deviate from clinical pathways. Measuring the concordance of patient pathways to clinical pathways is important for health system monitoring and informing quality improvement initiatives. In this paper, we develop an inverse optimization-based approach to measuring pathway concordance in breast cancer, a complex disease. We capture this complexity in a hierarchical network that models the patient’s journey through the health system. A novel inverse shortest path model is formulated and solved on this hierarchical network to estimate arc costs, which are used to form a concordance metric to measure the distance between patient pathways and shortest paths (i.e., clinical pathways). Using real breast cancer patient data from Ontario, Canada, we demonstrate that our concordance metric has a statistically significant association with survival for all breast cancer patient subgroups. We also use it to quantify the extent of patient pathway discordances across all subgroups, finding that patients undertaking additional clinical activities constitute the primary driver of discordance in the population.

Keywords: Operations research; Inverse optimization; Hierarchical network; Clinical pathway concordance; Breast cancer; Survival analysis (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10729-022-09599-z

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