Posts from April 2020

Payer

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.

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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:

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