Transform Real World Data into Insights
Real world evidence (RWE) and Real World Data (RWD) can inform all phases of pharmaceutical drug development, commercialization, and drug use in healthcare settings. Many sources of real-world data contain large amounts of unstructured text (e.g. EHRs; patient-reported outcomes such as forums, social media).
Linguamatics Natural Language Processing (NLP) extracts the key facts, using relevant ontologies and focused queries, transforming real world data into actionable intelligence for decision making.
Understanding the real world (i.e., outside of clinical trials) impact of therapies on patients is critical for pharmaceutical and biotech companies. However, many RWD sources contain unstructured text, which prevents easy analysis. Linguamatics NLP text mining platform can unlock the value from real world sources such as medical records, claims data, adverse event reports, field-based medical affairs notes, patient forum posts, free text in surveys, and customer call transcripts. By using NLP to extract key facts from these diverse data sources, pharmaceutical companies, healthcare providers and payers can determine medication effectiveness, safety and cost benefits.
There are many challenges in creating value from real world data (RWD). These include:
Linguamatics NLP text mining platform addresses these challenges to get value out of RWD - extracting the key facts from these unstructured documents, using relevant ontologies and focused queries –transforming real world data into real world evidence, and actionable intelligence for decision making.
Organizations can use our platform to extract information on treatment patterns e.g. drug switching, discontinuation; or numbers such as lab values, dosage information; or patient information such as history of disease, problem list, demographics, social factors and lifestyle. The agile iterative nature of query development means that business rules can be encoded to suit the particular data set, whether you’re looking at sentiments from tweets or treatment pattern choices and resulting outcomes from EHRs.
Learn more about Real World Data for commercial pharmaceutical product insights in our blog.
Text mining allows organizations to extract unstructured data to inform key decisions and speed up the drug discovery pipeline. In this use case, AstraZeneca show how, through their collaboration with PatientsLikeMe (PLM), they were able to examine differences in Nausea adverse reactions (AR) frequencies between patient on-line self-reported data, and data from FDA Drug Product Labels extracted using Linguamatics NLP platform.
Novo Nordisk wanted to identify healthcare market trends and detect patterns in clinical trial protocol deviations, patient sentiment, compliance, routines, behaviors, and treatment satisfaction and outcomes, from disparate RWD sources such as call center feeds and information from medical liaisons and healthcare providers. Novo Nordisk built on previous successes in individual Linguamatics NLP text mining projects to create an automated Linguamatics NLP workflow for real world data. With the new system, they 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.
Kaiser Permanente and Linguamatics are working on developing a new model to tackle the excessive readmission rate issue and its financial penalties in hospitals. This model employs data from a comprehensive electronic medical record (EMR) and which could be instantiated in real-time.
Novo Nordisk have built a successful data and technology ecosystem to uncover competitive insights using text mining and natural language processing of news data using Linguamatics technology and Dow Jones DNA.
In this 40-minute webinar, RealHealthData and Linguamatics discuss the challenges of working with real world data (in this case, medical transcripts), and show the power of Linguamatics NLP platform to extract relevant data to answer critical questions around treatment outcomes for prostate cancer.