Is medical treatment of Alcohol Withdrawal Syndrome a Stag Hunt? Challenges and opportunities in managing risk and uncertainty in addiction cessation
Roger Lee Mendoza
School of Business, Wilmington University, New Castle, DE, USA
Purpose: While the individual and social costs of alcoholism or alcohol use disorder are well established, few are aware that medical problems can arise during detoxification, some of which can be life-threatening. This study determines if sustained treatment for Alcohol Withdrawal Syndrome (AWS) might be based on the strategic choices and expectations of patients and health care providers alike, as well as the risk mitigation options available to them.
Design/approach: AWS was modeled as a Stag Hunt to explain both risk and decision-making in medical treatments for detoxification, since it can deduce a set of equilibrium strategies available to both patient and provider. Modeling was based on a review of juried literature gathered from search engines with the use medical subject heading terms.
Main findings: While there is little evidence that decision-making is shared between patient and physician in AWS treatments, the outcomes of their interactions depend on utility-maximizing choices each makes in anticipation of the other. Payoff-dominant and risk-dominant treatment outcomes are equally likely and equally cost-efficient, as conditioned by the presence (or absence) of mutual trust and assurance in reciprocal transactions.
Conclusion/value: Simulation games, such as the Stag Hunt, offer a viable framework to understand patient and provider incentives and health-affecting behaviors during treatments for addiction cessation. If both anticipate indefinitely interacting in the absence of any predetermined or foreseeable final visit, they can maximize future payoffs from mutual cooperation and accountability, which fosters health promotion. However, this study suggests that the effect of cooperation is distinct from the effect of time in AWS and other addiction-cessation programs.
Keywords: health-affecting behaviors, healthcare provider, patient compliance, payoff, relapse, simulation game, treatment disruption/discontinuation
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