Global healthcare company Novo Nordisk leverages generative AI features and capabilities in Dataiku to create predictive medical insights, automate scientific literature summaries, improve incentive compensation understanding through real-time insights for field sales teams, and more.
Throughout these projects, Dataiku has driven enhanced team efficiency, accelerated generative AI application development, ensured regulatory compliance, and offered upskilling opportunities in data science and AI. Dataiku’s solutions improved various aspects of Novo Nordisk’s operations, from empowering Field Medical Liaisons (FMLs) with data-driven insights to automating scientific literature analysis.
As their dedicated AI/ML workbench, Dataiku enabled the Novo Nordisk team to work on these diverse use cases in parallel, with robust collaboration, visibility and explainability into how models are built, and central governance across all data science work. The result was a more efficient and capable organization, better equipped to communicate scientific evidence and face modern healthcare challenges.
Novo Nordisk recognized a challenge in producing accurate cohort identification of Type 2 diabetes patients with substantial risk of developing comorbidities over specific time periods. In response, Novo Nordisk developed a predictive model and platform (“the Model”) to equip FMLs with local data insights that facilitate more informed scientific exchange with healthcare professionals. These insights focus on the volume of patient groups with Type 2 Diabetes who might be at risk of developing additional comorbidities.
Novo Nordisk used Dataiku to develop the Model, which leverages lab and healthcare claims data to identify potentially at-risk patient cohorts.
The insights from the Model were integrated into a Tableau dashboard, providing FMLs with predictive information about specified patient cohorts. This capability enabled FMLs to engage more effectively with healthcare professionals, ultimately promoting more informed medical decision making.
Implementation of the Model enhanced scientific exchange for Novo Nordisk. With the Model, FMLs were able to engage with healthcare professionals using innovative and actionable insights. This new capability led to more informed conversations on the importance of guideline-based care with healthcare professionals who treat patients with Type 2 diabetes.
Efficiently identifying and distilling key information from scientific papers can be time consuming for FMLs in the pharmaceutical industry. Novo Nordisk thus identified a need for an automated system to summarize scientific literature accurately and concisely. The goals of the system were to enhance research efficiency and to facilitate stakeholder updates on important scientific advancements.
To develop a solution, Novo Nordisk turned to Dataiku. The Dataiku team worked to create an automated, generative AI-driven system for summarizing complex scientific literature. This system provides Novo Nordisk’s FMLs with succinct, up-to-date summaries of relevant research findings.
After the solution was deployed, Novo Nordisk found both that research efficiency improved and that its teams were better informed about the latest developments in their fields. The enhanced learning opportunities available to these teams allowed them to facilitate more informed and productive scientific engagements.
When it came to analyzing a piece of literature for a specific case, Novo Nordisk knew it needed a system that could swiftly assess that scientific publication’s content to ascertain its relevance to different scenarios. Enter: LITGA AI.
The generative AI tool, built on Dataiku, enables efficient assessment of literature relevance, streamlining information retrieval and ensuring consideration of pertinent materials. This capability helped to facilitate more informed decision making and improved research outcomes, providing summaries that align with search criteria. Supported by LITGA AI’s ability to accurately analyze and assess publications, Novo Nordisk reports a reduction in manual effort, ultimately leading to time savings and improved productivity.
Historically, field sales representatives at Novo Nordisk had to manually search through extensive PDF documents to access information about Incentive Compensation (IC) business rules and processes. With IC ASSIST — a comprehensive retrieval-augmented generation (RAG) application built on Dataiku that delivers immediate and continuous support for field sales teams — the team could ensure availability of information whenever required. It is an accessible, user-friendly tool that facilitates access to crucial IC data and utilizes IC information to answer questions and provide insights into current IC questions. IC ASSIST also helps field sales representatives better navigate and understand IC-related data, and users can save their favorite answers for future reference. For reliability, IC ASSIST also provides the top three references for any provided answer.
By incorporating IC ASSIST, the Novo Nordisk AI/ML team enabled field sales representatives to seamlessly access their IC information. This streamlined access not only expedited the process of retrieving crucial IC details but also empowered the field sales team to have a clearer understanding of its performance and corresponding rewards. As a result, this initiative significantly improved the field sales team’s motivation and engagement, leading to enhanced productivity and efficiency.
Dataiku brings multifaceted value to this GenAI project. It enhances team efficiency, accelerates AI model development, and ensures regulatory compliance. Additionally, it offers comprehensive upskilling opportunities, fostering continuous learning and growth within the AI and data science domain. Developers are able to collaborate and scale up easily.Shihan He ML Engineer, Novo Nordisk
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