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A decision-making framework for adaptive pain management

Author

Listed:
  • Ching-Feng Lin
  • Aera LeBoulluec
  • Li Zeng
  • Victoria Chen
  • Robert Gatchel

Abstract

Pain management is a critical international health issue. The Eugene McDermott Center for Pain Management at The University of Texas Southwestern Medical Center conducted a two-stage interdisciplinary pain management program that considers a wide variety of treatments. Prior to treatment (beginning of Stage 1), an evaluation records the patient’s pain characteristics, medical history and related health parameters. A treatment regime is then determined. At the midpoint of the program (beginning of Stage 2), an evaluation is conducted to determine if an adjustment in the treatment should be made. A final evaluation is conducted at the end of the program to assess final outcomes. We structure this decision-making process using dynamic programming (DP) to generate adaptive treatment strategies for this two-stage program. An approximate DP solution method is employed in which state transition models are constructed empirically based on data from the pain management program, and the future value function is approximated using state space discretization based on a Latin hypercube design and artificial neural networks. The optimization seeks for treatment plans that minimize treatment dosage and pain levels simultaneously. Copyright Springer Science+Business Media New York 2014

Suggested Citation

  • Ching-Feng Lin & Aera LeBoulluec & Li Zeng & Victoria Chen & Robert Gatchel, 2014. "A decision-making framework for adaptive pain management," Health Care Management Science, Springer, vol. 17(3), pages 270-283, September.
    • Handle: RePEc:kap:hcarem:v:17:y:2014:i:3:p:270-283
    DOI: 10.1007/s10729-013-9252-0
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    References listed on IDEAS

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    1. S. A. Murphy, 2003. "Optimal dynamic treatment regimes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 331-355, May.
    2. Oguzhan Alagoz & Lisa M. Maillart & Andrew J. Schaefer & Mark S. Roberts, 2004. "The Optimal Timing of Living-Donor Liver Transplantation," Management Science, INFORMS, vol. 50(10), pages 1420-1430, October.
    3. Victoria C. P. Chen & David Ruppert & Christine A. Shoemaker, 1999. "Applying Experimental Design and Regression Splines to High-Dimensional Continuous-State Stochastic Dynamic Programming," Operations Research, INFORMS, vol. 47(1), pages 38-53, February.
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    Cited by:

    1. Nida Shahid & Tim Rappon & Whitney Berta, 2019. "Applications of artificial neural networks in health care organizational decision-making: A scoping review," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-22, February.
    2. Abdullah Gökçınar & Metin Çakanyıldırım & Theodore Price & Meredith C. B. Adams, 2022. "Balanced Opioid Prescribing via a Clinical Trade-Off: Pain Relief vs. Adverse Effects of Discomfort, Dependence, and Tolerance/Hypersensitivity," Decision Analysis, INFORMS, vol. 19(4), pages 297-318, December.

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