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From Data to Improved Decisions: Operations Research in Healthcare Delivery

Author

Listed:
  • Muge Capan
  • Anahita Khojandi
  • Brian T. Denton
  • Kimberly D. Williams
  • Turgay Ayer
  • Jagpreet Chhatwal
  • Murat Kurt
  • Jennifer Mason Lobo
  • Mark S. Roberts
  • Greg Zaric
  • Shengfan Zhang
  • J. Sanford Schwartz

Abstract

Background . The Operations Research Interest Group (ORIG) within the Society of Medical Decision Making (SMDM) is a multidisciplinary interest group of professionals that specializes in taking an analytical approach to medical decision making and healthcare delivery. ORIG is interested in leveraging mathematical methods associated with the field of Operations Research (OR) to obtain data-driven solutions to complex healthcare problems and encourage collaborations across disciplines. This paper introduces OR for the non-expert and draws attention to opportunities where OR can be utilized to facilitate solutions to healthcare problems. Methods . Decision making is the process of choosing between possible solutions to a problem with respect to certain metrics. OR concepts can help systematically improve decision making through efficient modeling techniques while accounting for relevant constraints. Depending on the problem, methods that are part of OR (e.g., linear programming, Markov Decision Processes) or methods that are derived from related fields (e.g., regression from statistics) can be incorporated into the solution approach. This paper highlights the characteristics of different OR methods that have been applied to healthcare decision making and provides examples of emerging research opportunities. Examples . We illustrate OR applications in healthcare using previous studies, including diagnosis and treatment of diseases, organ transplants, and patient flow decisions. Further, we provide a selection of emerging areas for utilizing OR. Conclusions . There is a timely need to inform practitioners and policy makers of the benefits of using OR techniques in solving healthcare problems. OR methods can support the development of sustainable long-term solutions across disease management, service delivery, and health policies by optimizing the performance of system elements and analyzing their interaction while considering relevant constraints.

Suggested Citation

  • Muge Capan & Anahita Khojandi & Brian T. Denton & Kimberly D. Williams & Turgay Ayer & Jagpreet Chhatwal & Murat Kurt & Jennifer Mason Lobo & Mark S. Roberts & Greg Zaric & Shengfan Zhang & J. Sanford, 2017. "From Data to Improved Decisions: Operations Research in Healthcare Delivery," Medical Decision Making, , vol. 37(8), pages 849-859, November.
    • Handle: RePEc:sae:medema:v:37:y:2017:i:8:p:849-859
    DOI: 10.1177/0272989X17705636
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    References listed on IDEAS

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    Cited by:

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    2. Wesley J. Marrero & Mariel S. Lavieri & Jeremy B. Sussman, 2021. "Optimal cholesterol treatment plans and genetic testing strategies for cardiovascular diseases," Health Care Management Science, Springer, vol. 24(1), pages 1-25, March.
    3. Madhu Mazumdar & Jashvant V. Poeran & Bart S. Ferket & Nicole Zubizarreta & Parul Agarwal & Ksenia Gorbenko & Catherine K. Craven & Xiaobo (Tony) Zhong & Alan J. Moskowitz & Annetine C. Gelijns & Davi, 2021. "Developing an Institute for Health Care Delivery Science: successes, challenges, and solutions in the first five years," Health Care Management Science, Springer, vol. 24(1), pages 234-243, March.
    4. Todd H. Wagner & Jeffrey K. Jopling, 2017. "Déjà Vu: Introducing Operations Research to Health Care," Medical Decision Making, , vol. 37(8), pages 847-848, November.

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