Quantum-Inspired Decision Models in Pharmaceutical Marketing Strategies

Abstract

Pharmaceutical marketing is changing significantly due to digitalization, data complexity, and evolving patient and physician behavior. Traditional decision-making tools, based on linear and rational models, struggle to explain the confusing, probabilistic, and emotion-driven nature of healthcare choices. Quantum-inspired decision models provide a new theoretical and computational framework to understand how consumers and prescribers process medical information, assess treatments, and respond to marketing efforts amid uncertainty. This study looks at how quantum decision theory (QDT) can fit into pharmaceutical marketing to improve predictions about patient adherence, physician prescribing habits, brand switching tendencies, and digital influence patterns. Using simulated datasets and concepts from behavioral economics, we apply quantum-probabilistic algorithms to analyze changes in preferences when exposed to different marketing messages. Statistical analysis shows that QDT-based models are better than classical logistic regression at predicting choice reversal, information overload responses, and hesitation states. Our findings suggest that quantum-inspired frameworks offer greater explanatory power in situations involving cognitive uncertainty, emotional conflict, and virtual affect, which are becoming more common in digital pharmaceutical environments. They enable companies to understand complex consumer behavior better, improve message accuracy, and support responsible communication strategies in a competitive and information-heavy market