It wasn’t so long ago that the core challenge of natural language processing (NLP) was simply getting it right. The ask from users was straightforward: Can you build a platform that takes in textual data and delivers accurate, reliable, comprehensive data insights in return? In many cases, expectations were not met, and many users have been put off by the false promises. However, not all efforts at applying NLP and text mining to textual data have underperformed—in fact, in the case of Linguamatics NLP, we have consistently met expectations and continue to build on our success.
Throughout 20 years of expertise and delivery, Linguamatics has kept its finger on the pulse and continued to grow with our customers’ needs. It’s why we continue to be trusted by leading organizations to deliver transparent, high quality NLP across a diversity of customers, from 19 of the top 20 pharmaceutical companies to leading healthcare payers, providers and healthcare IT companies.
Now, I’m excited to share that we are once again raising the bar. Having supported the movement of NLP from a niche “nice-to-have” to an essential business capability, our customers are looking to use NLP not just in individual data silos or for independent use cases, but across their entire organization. As the volume of textual data that they sit on increases exponentially, they need a solution that can systematically and comprehensively surface and normalize features of interest at scale.
Hi, Perfectionism. Meet Pragmatism.
Enter, the Linguamatics NLP Data Factory, our NLP automation solution that mobilizes value at scale from the key data your organization has generated or invested in. With the NLP Data Factory, we have essentially taken our trusted NLP capabilities and paired them with enterprise-grade robust reliability. With automated NLP workflows that process millions of documents and data fields per hour, across a range of business lines, the NLP Data Factory has the power to transform efficiency and productivity at organizational scale.
There are a number of great features of the NLP Data Factory that deserve mention. The platform itself is incredibly flexible, embedding seamlessly into existing architectures and workflows. It can accept data from a broad range of formats and transform them to common, ready-to-use data standards. You can deploy the NLP Data Factory on-premise, in the cloud, or with a hybrid implementation—whatever best suits your organization’s needs. Plus, new queries can be created and deployed in the NLP Data Factory with minimal effort.
Another advantage is the platform’s architecture. Whereas other solutions are built for single-area use, the NLP Data Factory was built with scalability in mind. An internal orchestration component parallelizes incoming data, ensuring that scaling is effective and matches the availability of resources.
Use Cases for the NLP Data Factory
The core value of the NLP Data Factory is that it provides automatic mobilization of textual data at scale. Therefore, it can be used across many potential areas and applications, spanning everything from biomarker discovery to adverse event detection and oncology profiling. Take, for example, a clinical documentation improvement (CDI) company. The organization needed to scale human review of medical records to keep up with customer demand. They needed a configurable query engine that could plug in existing data and extract information at scale across a range of therapy areas.
The company selected and deployed the NLP Data Factory for their initial use case, successfully automating the extraction of congestive heart failure features to augment human review. Now, the company is adding new therapeutic areas to successfully establish a robust NLP Data Factory pipeline. Today, they are processing more than 500,000 documents per hour, saving clinicians from exhausting and error-prone full manual review.
Other users have deployed the NLP Data Factory to perform novel target intelligence, speed and improve safety case processing, and more. One pharmaceutical company is using the NLP Data Factory approach to handle customer call verbatims for better medical affairs insights into patient adherence, behaviors, and concerns. As calls come in, a suite of NLP queries annotate each verbatim to capture the topics and categories, including mentions of other products, switching, packaging and formulation, drug interactions and more. These are fed to downstream dashboards for easy analysis and visualization, and a machine-learning model assesses and flags novelty. The workflow saves this customer four full-time employee weeks per year for every drug product monitored. It also provides answers to key product questions for medical affairs.
Maybe you have leveraged and trusted Linguamatics’ NLP capabilities in the past, or perhaps you are new to NLP — either way, I invite you to visit our NLP Data Factory webpage, read our fact sheet, and reach out to us for a demo. We would love to share how the NLP Data Factory can catalyse value at scale for your business.
