Personalized Treatment for Opioid Use Disorder

59 Pages Posted: 12 Jun 2019 Last revised: 27 Feb 2021

See all articles by Kyra Gan

Kyra Gan

Carnegie Mellon University

Alan Andrew Scheller-Wolf

Carnegie Mellon University

Sridhar R. Tayur

Carnegie Mellon University - David A. Tepper School of Business

Date Written: May 16, 2019

Abstract

Wearable devices have the potential to revolutionize treatments for opioid use disorder (OUD) by measuring patient responses to different treatment regimens in real-time, enabling the development of personalized treatments. A variety of wearable devices with different features, sensitivities, and costs are available. Whether such devices are practical and cost-effective to incorporate in treatments for OUD, and if so how they should be used, are critical questions. To investigate these questions, we build a finite-horizon, non-stationary constrained partially observable Markov decision process (CPOMDP). To facilitate the solution of our model, we provide a novel budget reformulation that finds all optimal solutions lying on the original formulation’s solution convex hull. We then show that our reformulation can be solved using a binary search in conjunction with an exact POMDP algorithm. Applying those elements and using parameters estimated from past literature, we perform a numerical study to investigate the values of different wearables in OUDtreatments, where we consider different levels of budget, wearable precision, and patient treatment adherence(TA). We find that wearables can be valuable when patients react differently to treatments across the entire population. Furthermore, this value is the largest at low or moderate budgets for patients with low or moderate TA. Outside of these settings, the marginal benefit of wearables is negligible relative to their cost.

Keywords: POMDP, CPOMDP, Budget Constraint, Personalized Treatment, Opioid Use Disorder

Suggested Citation

Gan, Kyra and Scheller-Wolf, Alan Andrew and Tayur, Sridhar R., Personalized Treatment for Opioid Use Disorder (May 16, 2019). Available at SSRN: https://ssrn.com/abstract=3389539 or http://dx.doi.org/10.2139/ssrn.3389539

Kyra Gan (Contact Author)

Carnegie Mellon University ( email )

Pittsburgh, PA 15213-3890
United States

Alan Andrew Scheller-Wolf

Carnegie Mellon University ( email )

Pittsburgh, PA 15213-3890
United States

Sridhar R. Tayur

Carnegie Mellon University - David A. Tepper School of Business ( email )

5000 Forbes Avenue
Pittsburgh, PA 15213-3890
United States

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