A Graph + NLP Framework to Identify Influential HCPs for Pharmaceutical Launches: Design, Deployment, and Managerial Impact

Abstract

Rapid and credible dissemination of scientific evidence is pivotal to modern medicine launches. Traditional Key-Opinion-Leader (KOL) programmes depend on narrow expert lists drawn from publication counts or prescribing volume, overlooking the relational and attitudinal pathways through which influence actually propagates. This article presents an end-to-end framework that fuses multi-layer network analytics built on medical-digital platforms, embeds a human-in-the-loop sentiment-surveillance pipeline, and ranks KOL sub-networks by propagative influence and sentiment trajectory. The innovative approach integrates three core technological components: multi-layer network analytics, human-in-the-loop sentiment surveillance, and propagative influence ranking. By assembling a comprehensive multi-layer graph incorporating diverse influence signals across scientific activity, professional collaboration, digital footprint, and real-world care delivery, the framework creates dynamic, evidence-optimized engagement maps. Empirical validation across international launches in immuno-oncology, antiviral therapy, and rare metabolic disease demonstrates consistent performance advantages in prescriber uptake velocity, field-team cost efficiency, and prescription generation. Implementation requires cross-functional governance, quarterly refresh cadence, integrated dashboards, and robust compliance safeguards to maximize impact while ensuring appropriate governance.