RWD from voice of the customer calls
Patient and customer call transcripts are rich with details on patient-reported outcomes, side effects, drug interactions, and other insights that greatly impact commercial business decisions and affect post-launch product marketing and planning.
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In order to gain insights into the real-world use of their drugs, another large biopharma company currently uses NLP-based text mining technology to annotate and categorize “voice of the customer” (VoC) call feeds. Researchers in the company’s predictive analytics group have built an end-to-end workflow for processing call transcripts and making sense of the unstructured feeds.
Using agile text-mining technology, researchers categorize and tag calls for key metadata, such as caller demographics and call reasons. By leveraging its use of NLP test-mining technology, the company has doubled the efficiency of their analysis and enabled longitudinal exploration of real-world patient concerns and outcomes.
Leveraging NLP-based text mining for better insights
The explosion of patient outcome data in many forms has created a trove of information that can be leveraged to advance the development and commercialization of drugs and therapies that ultimately improve the health of patients. Thanks to increasingly sophisticated technologies like NLP-based text mining, biopharma companies can take advantage of RWD to advance the innovation and delivery of their products.
Jane Reed is head of life science strategy at Linguamatics. She is responsible for developing the strategic vision for Linguamatics’ growing product portfolio and business development in the life science market. Jane has extensive experience in life science informatics. She has worked for more than 20 years in vendor companies supplying data products, data integration and analysis, and consultancy to pharma and biotech—with roles at Instem, BioWisdom, Incyte, and Hexagen. Before moving into the life science industry, Jane worked in academia with post-docs in genetics and genomics.
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