# An Inverse Optimization Approach to Measuring Clinical Pathway Concordance

## TL;DR

This paper introduces a novel inverse optimization method to measure how closely patient pathways follow clinical guidelines, demonstrating its effectiveness with real data and linking pathway concordance to patient survival.

## Contribution

It develops the first data-driven inverse optimization framework for pathway concordance, integrating multiple data sources and addressing data primacy and alignment.

## Key findings

- Concordance metric significantly correlates with patient survival.
- Method effectively distinguishes between aligned and deviated pathways.
- Real patient data validates the approach's practical utility.

## 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.