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An Inverse Optimization Approach to Measuring Clinical Pathway Concordance

Author

Listed:
  • Timothy C. Y. Chan

    (Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario M5S 3G8, Canada)

  • Maria Eberg

    (Department of Statistics, IQVIA, Kirkland, Quebec H9H 5M3, Canada)

  • Katharina Forster

    (Disease Pathway Management, Ontario Health (Cancer Care Ontario), Toronto, Ontario M5G 2C1, Canada)

  • Claire Holloway

    (Disease Pathway Management, Ontario Health (Cancer Care Ontario), Toronto, Ontario M5G 2C1, Canada; Department of Surgery, University of Toronto, Toronto, Ontario M5S 3G8, Canada)

  • Luciano Ieraci

    (Data and Decision Sciences, Ontario Health (Cancer Care Ontario), Toronto, Ontario M5G 2C1, Canada)

  • Yusuf Shalaby

    (Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario M5S 3G8, Canada)

  • Nasrin Yousefi

    (Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario M5S 3G8, Canada)

Abstract

Clinical pathways outline standardized processes in the delivery of care for a specific disease. Patient journeys through the healthcare system, however, 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, whereas data alignment is addressed by a hybrid objective function that aims to minimize and maximize suboptimality error for different subsets of input data.

Suggested Citation

  • Timothy C. Y. Chan & Maria Eberg & Katharina Forster & Claire Holloway & Luciano Ieraci & Yusuf Shalaby & Nasrin Yousefi, 2022. "An Inverse Optimization Approach to Measuring Clinical Pathway Concordance," Management Science, INFORMS, vol. 68(3), pages 1882-1903, March.
  • Handle: RePEc:inm:ormnsc:v:68:y:2022:i:3:p:1882-1903
    DOI: 10.1287/mnsc.2021.4100
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    References listed on IDEAS

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    1. Timothy C. Y. Chan & Taewoo Lee & Daria Terekhov, 2019. "Inverse Optimization: Closed-Form Solutions, Geometry, and Goodness of Fit," Management Science, INFORMS, vol. 65(3), pages 1115-1135, March.
    2. Ravindra K. Ahuja & James B. Orlin, 2001. "Inverse Optimization," Operations Research, INFORMS, vol. 49(5), pages 771-783, October.
    3. Schmidt, Ingrid & Thor, Johan & Davidson, Thomas & Nilsson, Fredrik & Carlsson, Christina, 2018. "The national program on standardized cancer care pathways in Sweden: Observations and findings half way through," Health Policy, Elsevier, vol. 122(9), pages 945-948.
    4. Timothy C. Y. Chan & Tim Craig & Taewoo Lee & Michael B. Sharpe, 2014. "Generalized Inverse Multiobjective Optimization with Application to Cancer Therapy," Operations Research, INFORMS, vol. 62(3), pages 680-695, June.
    5. Jianzhong Zhang & Mao Cai, 1998. "Inverse problem of minimum cuts," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 47(1), pages 51-58, February.
    6. Marvin D. Troutt & Wan-Kai Pang & Shui-Hung Hou, 2006. "Behavioral Estimation of Mathematical Programming Objective Function Coefficients," Management Science, INFORMS, vol. 52(3), pages 422-434, March.
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    Cited by:

    1. Lindong Liu & Xiangtong Qi & Zhou Xu, 2024. "Stabilizing Grand Cooperation via Cost Adjustment: An Inverse Optimization Approach," INFORMS Journal on Computing, INFORMS, vol. 36(2), pages 635-656, March.

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