Presentation (CTI)

OBJECTIVES: Medical Science Liaisons (MSLs) are a critical conduit between pharmaceutical companies and healthcare professionals (HCPs), yet workflow inefficiencies: fragmented content retrieval, inconsistent HCP responses, and poor insight capture limit their effectiveness. This study evaluates the real-world value and outcomes of deploying a generative AI-driven MSL enablement platform (eMSL Toolkit) in China, assessing its impact on operational efficiency, knowledge quality, and medical affairs return on investment.
METHODS: An HEOR impact case methodology was applied to a multi-phase implementation across a pharmaceutical company's MSL team. The platform utilized a Large Language Model plus Retrieval-Augmented Generation architecture integrating three knowledge sources: internally approved medical content, a PubMed literature database, and major conference proceedings. Key performance indicators captured adoption (weekly active users), efficiency (HCP inquiry response time, visit preparation time), quality (answer accuracy rate, citation traceability), and financial return on investment. Benchmarking was conducted against a comparable Digital MSL deployment at a MNC company (reference case: 6 therapy areas, 3,755 internal documents, 14,000+ PubMed articles).
RESULTS: Phase 1 pilot results demonstrated measurable operational value: HCP inquiry response time 95%+reduction; WAU reached 95%+; MSL visit preparation time decreased by over 50%; answer accuracy rate exceeded 95% with 100% citation traceability. Phase 1 return on investment was estimated at approximately 500%. The three-phase deployment model — from visit assistance to insight structuring and HCP digital activation — demonstrated a scalable, compliance-governed framework for medical affairs AI integration.
CONCLUSIONS: AI-driven MSL enablement platforms deliver quantifiable, multi-dimensional value in pharmaceutical medical affairs: efficiency gains, quality standardization, and scalable HCP engagement. The compliance-embedded LLM+RAG architecture addresses key pharma-specific constraints including content governance, in-label/off-label access controls, and audit traceability. These outcomes suggest that HEOR frameworks for pharmaceutical operations should incorporate AI-enabled workflow transformation as a measurable value driver, with implications for medical affairs resourcing, HCP interaction quality, and ultimately, evidence-to-practice translation at scale.