Inverse Optimization on Hierarchical Networks: An Application to Breast Cancer Clinical Pathways

TL;DR

This paper introduces an inverse optimization method to measure how closely breast cancer patient pathways follow clinical guidelines, revealing significant links to patient survival and identifying key sources of pathway deviations.

Contribution

It develops a novel inverse shortest path model on hierarchical networks to quantify pathway concordance in complex diseases like breast cancer.

Findings

Concordance metric correlates significantly with patient survival.

Patients with additional clinical activities show higher pathway discordance.

Method effectively quantifies pathway deviations in real patient data.

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.