Optimizing MASLD Follow-up Strategies Under Adherence Uncertainty: A Game Theory and Signaling Approach
Author(s)
Artem T. Boltyenkov, MBA, PhD1, Pina C. Sanelli, MD, MPH2, Wanyi Chen, PhD3, Szu-Yu Kao, PhD4, Michael Eiswerth, DO5, Donald B Chalfin, MD6, Jeffrey Lazarus, PhD7, Kinpritma Sangha, MPH, PhD8, Jason J. Wang, PhD9.
1Head, Global HEOR, Siemens Healthcare Diagnostics Inc., Lexington, SC, USA, 2Northwell Health, Manhasset, NY, USA, 3Siemens Healthineers, Walpole, MA, USA, 4Siemens Healthineers, Vancouver, WA, USA, 5VCU Health, Richmond, VA, USA, 6Siemens Healthineers, Newark, DE, USA, 7Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain, 8Siemens Healthineers, Malvern, PA, USA, 9Northwell Health, Glen Cove, NY, USA.
1Head, Global HEOR, Siemens Healthcare Diagnostics Inc., Lexington, SC, USA, 2Northwell Health, Manhasset, NY, USA, 3Siemens Healthineers, Walpole, MA, USA, 4Siemens Healthineers, Vancouver, WA, USA, 5VCU Health, Richmond, VA, USA, 6Siemens Healthineers, Newark, DE, USA, 7Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain, 8Siemens Healthineers, Malvern, PA, USA, 9Northwell Health, Glen Cove, NY, USA.
OBJECTIVES: Metabolic dysfunction-associated steatotic liver disease (MASLD) is highly prevalent but asymptomatic in early stages, making adherence to lifestyle interventions difficult to predict and manage. We aimed to develop a game-theoretic model to help physicians optimize follow-up intensity by accounting for uncertainty in patient adherence and the potential value of outcome-based diagnostic testing.
METHODS: We constructed a non-cooperative Bayesian game model, enhanced with a signaling mechanism, to simulate physician-patient interactions under uncertainty. The model incorporates patient types (adherent vs. non-adherent), follow-up strategies (aggressive vs. conservative), and the use of non-invasive test outcomes (e.g., ELF, MRE) as behavioral signals. Payoffs were defined based on treatment success, resource use, and patient burden. We evaluated expected outcomes under varying adherence prevalence scenarios and with versus without signaling.
RESULTS: The model shows that the physician’s optimal follow-up strategy depends on their belief about the likelihood of patient adherence. Signaling through diagnostic testing allows belief updating and enables dynamic adjustment of follow-up strategies over time. Compared to static strategies based only on initial assumptions, signaling improves expected outcomes for both the physician and the patient by resolving behavioral uncertainty and better aligning care intensity with patient needs.
CONCLUSIONS: Game theory offers a novel framework for improving MASLD management by incorporating behavioral uncertainty into follow-up planning. This approach supports the use of non-invasive testing not only for clinical monitoring but also for informing personalized follow-up strategies. The model is particularly relevant in the context of expanding treatment options and increasing pressure on healthcare resource optimization.
METHODS: We constructed a non-cooperative Bayesian game model, enhanced with a signaling mechanism, to simulate physician-patient interactions under uncertainty. The model incorporates patient types (adherent vs. non-adherent), follow-up strategies (aggressive vs. conservative), and the use of non-invasive test outcomes (e.g., ELF, MRE) as behavioral signals. Payoffs were defined based on treatment success, resource use, and patient burden. We evaluated expected outcomes under varying adherence prevalence scenarios and with versus without signaling.
RESULTS: The model shows that the physician’s optimal follow-up strategy depends on their belief about the likelihood of patient adherence. Signaling through diagnostic testing allows belief updating and enables dynamic adjustment of follow-up strategies over time. Compared to static strategies based only on initial assumptions, signaling improves expected outcomes for both the physician and the patient by resolving behavioral uncertainty and better aligning care intensity with patient needs.
CONCLUSIONS: Game theory offers a novel framework for improving MASLD management by incorporating behavioral uncertainty into follow-up planning. This approach supports the use of non-invasive testing not only for clinical monitoring but also for informing personalized follow-up strategies. The model is particularly relevant in the context of expanding treatment options and increasing pressure on healthcare resource optimization.
Conference/Value in Health Info
2025-11, ISPOR Europe 2025, Glasgow, Scotland
Value in Health, Volume 28, Issue S2
Code
MSR158
Topic
Health Service Delivery & Process of Care, Methodological & Statistical Research, Patient-Centered Research
Disease
Diabetes/Endocrine/Metabolic Disorders (including obesity), No Additional Disease & Conditions/Specialized Treatment Areas