1. Laser-sharp identification of Key Opinion Leaders (KOL)
KOLs are significant for life sciences commercial teams. They have access to huge audiences, wield substantial influence in the medical community, and create trust and credibility. Because of this, they can be highly effective at promoting brands for organizations. Aligning commercial strategies with the right KOLs is a sure-fire way for life sciences organizations to capture and sustain more customer mindshare. But this is no easy task. Scouting KOLs involves:
sifting through a copious amount of clinical literature, research publications, and medical journals published by hundreds of HCPs, analyzing several years of their data, and
understanding their national, regional, and local influence
...all of which is an extremely time-consuming process.
AI techniques simplify these tasks to a large extent. They speed up the process of spotting the right KOL influencers and ensure only the optimal targets for a specific therapeutic area are contacted and engaged.
By establishing pre-built AI connectors to public clinical literature libraries like PubMed and Scopus, organizations can simplify the task of manually searching and downloading publications on specific therapeutic areas (such as vaccines or immuno-oncology).
The output is sent to an AI engine that screens and validates the relevancy of all articles for a specific therapeutic area of interest - enabling companies to swiftly identify suitable KOLs, access their publications, and understand their research focus. Organizations can further augment this data by mining social media channels, such as Twitter and LinkedIn, to extract insights from relevant HCP discussions.
Since social media data is unstructured and fragmented in nature, it can be overwhelming for organizations to extract insights on time. This is where Natural Language Processing (NLP) plays a crucial role. NLP models convert large volumes of unstructured data into a structured format. They can be trained to recognize HCP sentiment in social conversations by identifying language patterns that reflect their opinions, interests, and expectations. Figure 3 breaks the NLP process down further.3, 4