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The value of NLP for payers and health plans

Health plans and payers rely on medical record review for multiple different business-critical processes. Manual review of the vast amounts of unstructured medical data is very labor-intensive, requiring significant staff and time investment - with related high costs - and generally, slow headway. However, the vital member insights that can be gained from medical records and other unstructured healthcare data sources are too important to be ignored. To address this challenge, payers have increasingly been assessing technology options to streamline the process of identifying and extracting these insights in a more efficient, cost-effective manner.

Natural Language Processing (NLP) as an AI (Augmented Intelligence) technique has become increasingly popular with payers and health plans in recent years. By using NLP to analyze unstructured data like PDF medical records, call center transcripts, and Electronic Health Record (EHR) exports, companies are now able to streamline business processes where manual review is needed - extracting key healthcare insights from medical records in a fraction of the time, at a fraction of the cost.

Key NLP application areas for payers and health plans

Business-critical processes requiring medical record review include NCQA HEDIS™ quality measure reporting, clinical review/medical necessity and Medicare risk adjustment. The more established use of NLP in disease coding, and especially risk adjustment, has paved the way for NLP to also be applied in new areas to enhance predictive models, identify high risk members, reduce manual chart review and streamline business audit processes that require extensive medical record review.


In the rapidly evolving fight against COVID-19, IQVIA is committed to deploying our resources and capabilities to help everyone in healthcare do what needs to be done, and to keep things moving forward. Pharmaceutical and healthcare organizations, governments, and the broader scientific communities around the world are working to assess the impact of the virus, and how this can be tackled.

As part of this effort, it’s critical to have access to the best evidence from a broad range of data, including scientific literature, clinical trials and other textual sources. For intelligence from unstructured text, Linguamatics can help. Our Natural Language Processing (NLP) technology enables fast, systematic, and comprehensive insight generation from unstructured text. These sources can include scientific literature, clinical trial records, preprints, internal sources, social media, and news. Capturing key information from these many sources and synthesizing into one place – an Evidence Hub – gives users a deeper understanding of everything that’s going on. This approach can speed answers to key questions to confront the COVID-19 pandemic, such as:


Pharmaceutical companies are increasingly recognizing that patients’ social media posts are a valuable source of insight into patient-reported outcomes, views, symptoms, use of competitive products and more. Analyzing social media posts has the potential to provide insights from a broad population of patients, healthcare professionals, and key opinion leaders.

The standard methodology to gather these insights is primary market research. Pharma companies work hard to establish and maintain focus groups for live interviews, questionnaires and other ways to gather insights from patients and providers. However, the research process is time-consuming and expensive, requiring pharma companies to invest a significant amount of resources; and the number of patients that can be targeted is small.

Analysis of social media has the potential to be more efficient, more cost-effective, and address much larger patient populations than primary market research. The problem in analyzing social media content, however, is that social platforms represent a constantly flowing firehose of noisy information, so separating the signal from the noise poses a significant challenge.

Text mining with Natural Language Processing (NLP) technology represents an alternative approach to unlocking the value of social media content. By enabling artificial intelligence-based NLP tools to gather and analyze social data, pharmaceutical companies can more swiftly and efficiently glean insights that will influence the drug development process.


We are pleased to announce that Linguamatics, an IQVIA company, and the leading Natural Language Processing (NLP) text analytics provider, has joined Accenture’s open partner ecosystem which is designed to help independent software vendors (ISVs) and life sciences companies team more effectively to accelerate drug discovery efforts and improve patient outcomes.

The INTIENT Network for Research is an integral part of Accenture’s cloud-based informatics research platform, which has been designed to help life sciences organizations improve productivity, efficiency and innovation in drug discovery. Accenture is currently working with a select number of ISVs and organizations—including Linguamatics— to integrate their technology and content into Accenture’s research platform. This will allow life sciences companies to give their researchers access to innovative capabilities, such as Linguamatics’ innovative Natural Language Processing-based AI for high-value knowledge discovery and decision support from text. The Linguamatics award-winning platform is proven across multiple real-world use cases to deliver actionable insights that address pressing bench-to-bedside challenges with quantifiable ROI.


Using Natural Language Processing (NLP), Agios Pharmaceuticals discovers new therapeutic candidate

As the search for novel anti-cancer agents continues apace, the biopharma industry struggles to make sense of the myriad studies that are published describing putative small molecule inhibitors, potential genetic targets, and possibly susceptible points of attack. One area of growing interest in oncology is cancer cell metabolism: companies are striving to develop compounds that can interrupt or inhibit the metabolic process, leading to tumor cell suppression or death. The dual challenges are to identify promising lead compounds, and to detect suitable genes implicated in the metabolic process and sensitive to chemical intervention.

Rather than starting from scratch with a blank structure-activity canvas and pursuing the traditional (and potentially lengthy and risky) lead identification/lead optimization route to a pre-clinical candidate, Agios Pharmaceuticals decided to short-circuit the process and build on previously published studies. They wanted to locate and source known inhibitors for use as tool compounds in their chemical genetics screens and to identify genes with “druggable Achilles’ heels” susceptible to chemical attack, and they chose to use NLP to quickly and effectively scour the literature.