Learning more about drugs understanding in the market
How can pharma product managers efficiently learn how their drugs are faring with patients in the market?
Product managers and teams in pharmaceutical companies need to know what patients and healthcare professionals are reporting and asking about their drugs as they are used in the market, in order to discern trends and patterns and respond appropriately. Real world data (RWD) on drug usage and patient behaviours is available in multiple formats from myriad sources, but mining these disparate structured and unstructured sources with traditional manual search and curation is time-consuming and inefficient.
Novo Nordisk wanted to accelerate, automate and scale this process to provide enhanced access to the extracted information for superior and actionable insights.
Natural Language Processing-based Text Mining at Novo Nordisk
Novo Nordisk was already using the Linguamatics NLP platform in-house on multiple individual text mining projects with good success (e.g. reducing a publication gap analysis from three-to-four people for six weeks to a few hours). They wanted to capitalise on this success for real world data about their diabetes therapeutic products, from medical affairs team, healthcare professionals, and patients.
Mining RWD Sources to Extract Structured Information
Novo Nordisk used the Linguamatics NLP Platform to mine medical information requests, field medical affairs notes, and customer call center reports for real world evidence on drug usage and queries. NLP algorithms were developed relating to topics such as safety, efficacy, PK/PD, randomized controlled trials, patient populations, dosing, and devices. These resulted in standardised structured information in a format suitable for visualisation.
Medical Patient Dashboard
Novo Nordisk used Tableau to develop self-serve, interactive dashboards to search and review the extracted RWD. These enable queries such as “what efficacy questions are being asked about Product X?”, as well as interactive drill downs to explore who is asking the questions, where globally, and how often. Product managers can use the dashboards to explore commercial effectiveness, market development potential and gaps in product documentation or usage notes.
Migration to the Cloud
Novo Nordisk also wanted to migrate the new Linguamatics NLP workflow and Tableau dashboards to their Amazon Web Services (AWS)-based global big data and analytics platform. This would allow more frequent updates, provide access to a broader set of data sources, and address a global demand for the insights provided. They created a data pipeline to pull the RWD source files from the corporate data lake, run the Linguamatics NLP workflow and automatically deposit the extracted results back into the data lake for analysis and visualisation via Tableau.
With the migration to the AWS platform, the data pipeline saves significant time, and provides stakeholders with on-demand insights to enable evidence-based decision making.
Outcome
Novo Nordisk built on previous successes in individual Linguamatics NLP text mining projects to create an automated I2E NLP workflow for real world data. With the new system, Novo Nordisk have reduced manual work by FTEs, cut out external vendor manual work and spend, automated the process of generating insights, and significantly broadened access to these insights across a global team.
To learn more about this Novo Nordisk Linguamatics NLP project to extract value from RWD, its migration to the cloud, and the substantial time savings they accrued in this and a range of other NLP projects please email enquiries@linguamatics.com.
