Some Thoughts on Content and Data in Life Science
In my journey across the realms of content and data management systems, I've been fortunate to witness firsthand the transformative power of both data and content, particularly through my experiences at SDL (now RWS) and Veeva Systems. It's crucial to note that my current reflections are independent of my previous affiliations, offering a personal perspective not endorsed by Veeva Systems.
At SDL, the principle that 'Content is King' was a cornerstone belief, hinting at a future where content would have the capability to generate itself. This notion, once speculative, has found concrete realization in the advent of generative AI technologies. Veeva Systems immersed me in the biomedical domain, dealing with content and data's lifecycle management and their application in marketing/medical strategies for pharmaceuticals. While not claiming expertise in life sciences by any means, such experience has granted me insights into the synergistic role of data, Generative AI solutions, and their application across sectors.
Veeva's suite includes tools for R&D and Commercial, among which the CRM Suite, Data Cloud, and Content Management are particularly notable. Here, I focus on the Data and Content aspects, emphasizing their importance for the effective deployment of generative AI technologies. The Data Cloud, comprising OpenData and the Link platform, offers a robust foundation of healthcare professional data and real-time intelligence on Key Opinion Leaders, respectively. These tools are pivotal for pharmaceutical companies in maintaining up-to-date interactions with healthcare professionals.
The concept of unique IDs (Veeva IDs or OK IDs for IQVIA) emerges as a critical element in the context of generative AI. Such IDs are indispensable for accurately mapping and contextualizing interactions within the healthcare industry, significantly reducing the potential for data inaccuracies or 'hallucinations' in AI-generation. By utilizing unique IDs, pharmaceutical companies can effectively organize and analyze data, facilitating the generation of reliable insights and recommendations through AI.
Consider one use case where a pharmaceutical company employs a graph network to map the relationships and interactions between medical representatives and healthcare professionals. In this network, each node represents an individual—be it a sales rep or a doctor—and each edge signifies an interaction, such as a meeting or a discussion about a specific drug. Unique IDs are crucial in this scenario, serving as the definitive identifiers that link each node to its real-world counterpart. This precise mapping allows LLMs to traverse the graph, understanding the history and context of each relationship.
For example, an LLM could analyze the graph to identify which healthcare professionals have shown interest in a new Oncology medication but have not yet received detailed information. The LLM, leveraging the graph informed by unique IDs, could then generate personalized communication strategies for the sales reps to follow. This approach ensures that the information provided is not only relevant but also timely, based on the specific interactions and interests captured within the network. By grounding LLMs in the concrete data structure provided by graph networks, pharmaceutical companies can make more informed, accurate, and effective decisions in their outreach and educational efforts.
Envisioning the application of generative AI in pharmaceutical marketing, unique IDs allow for a nuanced understanding of interactions and preferences, enabling AI to offer targeted suggestions and personalized content. This capability is especially relevant in creating customized communication strategies, leveraging detailed data management and State of Art Generative models to optimize engagement with healthcare professionals.
Furthermore, the management of promotional content, through platforms like PromoMats, underscores the necessity of detailed tagging and metadata for AI to generate relevant suggestions and insights. This approach not only enhances the effectiveness of marketing strategies but also ensures compliance and alignment with industry standards. This would also increase adoption of existing content and measure effectiveness, helping decide which content to prioritise or archive.
The use of LLMs is not exclusive to Commercial functions in Biopharma. The exploration into Veeva Medical unveils another dimension of sophistication in content and data management, specifically through MedComms for Medical Information and MedInquiry for handling Medical Inquiries from healthcare professionals. These platforms are integral to managing the flow of scientific information and inquiries, effectively facilitating the engagement between medical science liaisons and Key Opinion Leaders (KOLs). The use of unique IDs for doctors and their inquiries becomes a pivotal strategy in this context, enabling a detailed mapping of recent interests and queries raised by healthcare professionals.
This granular approach to data management allows field medical teams to tailor their engagements with KOLs meticulously. By harnessing the insights gleaned from unique IDs associated with inquiries, medical representatives can present the most relevant scientific materials and messages, thus significantly enhancing the quality of their interactions. Such a strategy not only ensures that KOLs receive information that is timely and pertinent to their current interests and needs but also positions the pharmaceutical company as a valuable source of insights and solutions.
In summary, my explorations into the integration of data, content, and generative AI technologies underscore the significance of clean, structured, and relevant data as the foundation for successful AI implementations. The challenges associated with data management and AI 'hallucinations' highlight the importance of precision in data collection and application, ultimately driving the adoption and efficacy of AI solutions in various sectors.