A behavioral operations framework to mitigate generic substitution through data-driven anti-switch strategies
DOI:
https://doi.org/10.64171/JAES.1.2.96-107Keywords:
Behavioral operations, Framework, Mitigate, Generic substitution, Data-driven, Anti-switch strategiesAbstract
Generic drug substitution, while instrumental in reducing healthcare costs and expanding access, poses strategic challenges for brand-name pharmaceutical manufacturers. Patients and prescribers often switch to generics due to cost incentives, formulary mandates, or perceived equivalence, resulting in diminished brand loyalty and potential treatment discontinuities. This introduces a behavioral operations framework designed to mitigate unwanted generic substitution through the deployment of data-driven anti-switch strategies. Drawing from the intersection of behavioral science, operations management, and predictive analytics, the proposed framework emphasizes proactive identification of switch-prone individuals and the strategic deployment of tailored interventions. At its core, the framework integrates multimodal data—electronic health records, pharmacy claims, digital interactions, and behavioral profiles—to construct predictive models that classify patients and prescribers according to their likelihood of switching. Behavioral segmentation informs targeted interventions such as personalized nudges, educational content, provider alerts, and loyalty-enhancing mechanisms. A feedback loop refines intervention effectiveness through continuous learning algorithms and real-time outcome monitoring. The framework is further contextualized through application in high-risk therapeutic areas—such as cardiovascular diseases, mental health, and oncology—where substitution decisions can have significant clinical and commercial implications. By aligning predictive analytics with behavioral insights, the approach transcends traditional marketing tactics, offering a more ethical and precise means of sustaining brand engagement. Importantly, this discusses regulatory boundaries, privacy safeguards, and ethical considerations to ensure compliance and public trust. This framework advances the behavioral operations discourse by illustrating how data-informed strategies can address complex decision-making patterns in healthcare. It calls for cross-sector collaboration among pharmaceutical strategists, data scientists, behavioral economists, and regulators. Future directions include the integration of AI-enabled real-time decision support, adaptive experimentation, and platform-based deployment models to scale and personalize interventions. This work highlights a critical opportunity to balance cost containment with brand sustainability in the evolving pharmaceutical landscape.
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