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Reducing the Price of Naïveté in return‐to‐play from sports‐related concussion

Author

Listed:
  • Gian‐Gabriel P. Garcia
  • Mariel S. Lavieri
  • Thomas W. McAllister
  • Michael A. McCrea
  • Steven P. Broglio
  • CARE Consortium Investigators

Abstract

Patient‐reported outcomes (PROs) play an increasingly important role in medical decision making. Yet, patients whose objectives differ from their physician's may strategically report symptoms to alter treatment decisions. For example, athletes may underreport symptoms to expedite return‐to‐play (RTP) from sports‐related concussion (SRC). Thus, clinicians must implement treatment policies that mitigate the Price of Naïveté, that is, the reduction in health outcomes due to naïvely believing strategically reported symptoms. In this study, we analyze dynamic treatment cessation decisions with strategic patients. Specifically, we formulate the Behavior‐Aware Partially Observable Markov Decision Process (BA‐POMDP), which optimizes the timing of treatment cessation decisions while accounting for known symptom‐reporting behaviors. We then analytically characterize the BA‐POMDP's optimal policy, leading to several practical insights. Next, we formulate the Behavior‐Learning Partially Observable Markov Decision Process (BL‐POMDP), which extends the BA‐POMDP by learning a patient's symptom‐reporting behavior over time. We show that the BL‐POMDP is decomposable into several BA‐POMDPs, allowing us to leverage the BA‐POMDP's structural properties for solving the BL‐POMDP. Then, we apply the BL‐POMDP to RTP from SRC using data from 29 institutions across the United States. We estimate the Price of Naïveté by comparing the BL‐POMDP to naïve benchmark policies. Accordingly, the BL‐POMDP reduces premature RTP by over 44% and provides up to 3.63 additional health‐adjusted athletic exposures per athlete compared to current practice. Overall, changing the interpretation of reported symptoms can better reduce the Price of Naïveté over adjusting treatment cessation thresholds. Therefore, to improve patients' health outcomes, clinicians must understand how strategic behavior manifests in PROs.

Suggested Citation

  • Gian‐Gabriel P. Garcia & Mariel S. Lavieri & Thomas W. McAllister & Michael A. McCrea & Steven P. Broglio & CARE Consortium Investigators, 2023. "Reducing the Price of Naïveté in return‐to‐play from sports‐related concussion," Production and Operations Management, Production and Operations Management Society, vol. 32(10), pages 3081-3099, October.
    • Handle: RePEc:bla:popmgt:v:32:y:2023:i:10:p:3081-3099
    DOI: 10.1111/poms.14024
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    References listed on IDEAS

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    1. Hui Zhang & Christian Wernz & Danny R. Hughes, 2018. "A Stochastic Game Analysis of Incentives and Behavioral Barriers in Chronic Disease Management," Service Science, INFORMS, vol. 10(3), pages 302-319, September.
    2. Schottmüller, Christoph, 2013. "Cost incentives for doctors: A double-edged sword," European Economic Review, Elsevier, vol. 61(C), pages 43-58.
    3. Steven M. Shechter & Matthew D. Bailey & Andrew J. Schaefer & Mark S. Roberts, 2008. "The Optimal Time to Initiate HIV Therapy Under Ordered Health States," Operations Research, INFORMS, vol. 56(1), pages 20-33, February.
    4. Schottmuller, C., 2011. "Cost Incentives for Doctors : A Double-Edged Sword," Other publications TiSEM 2aa2a734-eadb-4f0e-89b3-0, Tilburg University, School of Economics and Management.
    5. Greggory J. Schell & Gian-Gabriel P. Garcia & Mariel S. Lavieri & Jeremy B. Sussman & Rodney A. Hayward, 2019. "Optimal coinsurance rates for a heterogeneous population under inequality and resource constraints," IISE Transactions, Taylor & Francis Journals, vol. 51(1), pages 74-91, January.
    6. Gian-Gabriel P. Garcia & Mariel S. Lavieri & Ruiwei Jiang & Michael A. McCrea & Thomas W. McAllister & Steven P. Broglio & CARE Consortium Investigators, 2020. "Data-driven stochastic optimization approaches to determine decision thresholds for risk estimation models," IISE Transactions, Taylor & Francis Journals, vol. 52(10), pages 1098-1121, October.
    7. Aswani, Anil & Kaminsky, Philip & Mintz, Yonatan & Flowers, Elena & Fukuoka, Yoshimi, 2019. "Behavioral modeling in weight loss interventions," European Journal of Operational Research, Elsevier, vol. 272(3), pages 1058-1072.
    8. Jennifer E. Mason & Darin A. England & Brian T. Denton & Steven A. Smith & Murat Kurt & Nilay D. Shah, 2012. "Optimizing Statin Treatment Decisions for Diabetes Patients in the Presence of Uncertain Future Adherence," Medical Decision Making, , vol. 32(1), pages 154-166, January.
    9. 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.
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