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Designing Personalized Treatment Plans for Breast Cancer

Author

Listed:
  • Wei Chen

    (School of Business, George Washington University, Washington, District of Columbia 20052)

  • Yixin Lu

    (School of Business, George Washington University, Washington, District of Columbia 20052)

  • Liangfei Qiu

    (Warrington College of Business, University of Florida, Gainesville, Florida 32611)

  • Subodha Kumar

    (Fox School of Business, Temple University, Philadelphia, Pennsylvania 19122)

Abstract

Breast cancer remains the leading cause of cancer deaths among women around the world. Contemporary treatment for breast cancer is complex and involves highly specialized medical professionals collaborating in a series of information-intensive processes. This poses significant challenges to personalization and customization of treatment plans for individual patients. In this research, we follow the information systems design science paradigm and propose a novel framework for decision support of treatment planning for early stage breast cancer patients undergoing radiotherapy. The core of our framework consists of a predictive model that predicts patient outcome of a treatment plan based on clinical and patient characteristics, and an optimization model that optimizes the treatment plan based on predicted outcomes of different plans. Using a series of simulation experiments, we show that the treatment plans generated from our framework consistently outperform those from the existing practices in balancing the risk of local tumor recurrence and radiation-induced adverse effects, thereby reducing the treatment cost associated with these adverse effects. Our research contributes to the growing literature that examines the potential of healthcare information technologies in delivering cost-effective care. Further, we also contribute to healthcare practices by providing models and tools that have pragmatic value as part of the clinical care delivery system.

Suggested Citation

  • Wei Chen & Yixin Lu & Liangfei Qiu & Subodha Kumar, 2021. "Designing Personalized Treatment Plans for Breast Cancer," Information Systems Research, INFORMS, vol. 32(3), pages 932-949, September.
    • Handle: RePEc:inm:orisre:v:32:y:2021:i:3:p:932-949
    DOI: 10.1287/isre.2021.1002
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    References listed on IDEAS

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    1. Elisa F. Long & Gilberto Montibeller & Jun Zhuang, 2022. "Health Decision Analysis: Evolution, Trends, and Emerging Topics," Decision Analysis, INFORMS, vol. 19(4), pages 255-264, December.

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