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The Effect of Budgetary Restrictions on Breast Cancer Diagnostic Decisions

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  • Mehmet U. S. Ayvaci

    (Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53706)

  • Oguzhan Alagoz

    (Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53706)

  • Elizabeth S. Burnside

    (Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin 53792)

Abstract

We develop a finite-horizon discrete-time constrained Markov decision process (MDP) to model diagnostic decisions after mammography where we maximize the total expected quality-adjusted life years (QALYs) of a patient under resource constraints. We use clinical data to estimate the parameters of the MDP model and solve it as a mixed-integer program. By repeating optimization for a sequence of budget levels, we calculate incremental cost-effectiveness ratios attributable to consecutive levels of funding and compare actual clinical practice with optimal decisions. We prove that the optimal value function is concave in the allocated budget. Comparing to actual clinical practice, using optimal thresholds for decision making may result in approximately 22% cost savings without sacrificing QALYs. Our analysis indicates short-term follow-ups are the immediate target for elimination when budget becomes a concern. Policy change is more drastic in the older age group with the increasing budget, yet the gains in total expected QALYs related to larger budgets are predominantly seen in younger women along with modest gains for older women.

Suggested Citation

  • Mehmet U. S. Ayvaci & Oguzhan Alagoz & Elizabeth S. Burnside, 2012. "The Effect of Budgetary Restrictions on Breast Cancer Diagnostic Decisions," Manufacturing & Service Operations Management, INFORMS, vol. 14(4), pages 600-617, October.
    • Handle: RePEc:inm:ormsom:v:14:y:2012:i:4:p:600-617
    DOI: 10.1287/msom.1110.0371
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    References listed on IDEAS

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    Cited by:

    1. Otten, Maarten & Timmer, Judith & Witteveen, Annemieke, 2020. "Stratified breast cancer follow-up using a continuous state partially observable Markov decision process," European Journal of Operational Research, Elsevier, vol. 281(2), pages 464-474.
    2. Robert Kraig Helmeczi & Can Kavaklioglu & Mucahit Cevik & Davood Pirayesh Neghab, 2023. "A multi-objective constrained partially observable Markov decision process model for breast cancer screening," Operational Research, Springer, vol. 23(2), pages 1-42, June.
    3. Elliot Lee & Mariel Lavieri & Michael Volk & Yongcai Xu, 2015. "Applying reinforcement learning techniques to detect hepatocellular carcinoma under limited screening capacity," Health Care Management Science, Springer, vol. 18(3), pages 363-375, September.
    4. Malek Ebadi & Raha Akhavan-Tabatabaei, 2021. "Personalized Cotesting Policies for Cervical Cancer Screening: A POMDP Approach," Mathematics, MDPI, vol. 9(6), pages 1-20, March.
    5. Mehmet A. Ergun & Ali Hajjar & Oguzhan Alagoz & Murtuza Rampurwala, 2022. "Optimal breast cancer risk reduction policies tailored to personal risk level," Health Care Management Science, Springer, vol. 25(3), pages 363-388, September.
    6. M. Reza Skandari & Steven M. Shechter & Nadia Zalunardo, 2015. "Optimal Vascular Access Choice for Patients on Hemodialysis," Manufacturing & Service Operations Management, INFORMS, vol. 17(4), pages 608-619, October.
    7. Wang, Fan & Zhang, Shengfan & Henderson, Louise M., 2018. "Adaptive decision-making of breast cancer mammography screening: A heuristic-based regression model," Omega, Elsevier, vol. 76(C), pages 70-84.
    8. Sait Tunç & Oguzhan Alagoz & Elizabeth S. Burnside, 2022. "A new perspective on breast cancer diagnostic guidelines to reduce overdiagnosis," Production and Operations Management, Production and Operations Management Society, vol. 31(5), pages 2361-2378, May.
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    11. Pinar Keskinocak & Nicos Savva, 2020. "A Review of the Healthcare-Management (Modeling) Literature Published in Manufacturing & Service Operations Management," Manufacturing & Service Operations Management, INFORMS, vol. 22(1), pages 59-72, January.

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