Health plans and payers rely on medical record review for many different business-critical processes, most notably for risk adjustment and NCQA HEDISTM quality measure reporting. There is also an increasing need for curation and annotation of large datasets used to feed predictive models and other machine learning (ML) applications. Doing all of this manually is immensely time consuming and costly.
Increasingly, payer organizations are looking to technologies such as natural language processing (NLP) to augment these manual processes, to reduce burden and cost, and increase quality of care, efficiency and revenue. Furthermore, with the Cures Act mandating interoperability of the entire medical record, and the flow of data between providers and payers increasing, payers are seeing that the use of technology can have far reaching impact. Despite plans for these data to be transferred over Fast Healthcare Interoperability Resource (FHIR), the free text within those transfers is complex, varied and unmanageable without NLP.
This White Paper will discuss the following topics:
- What is NLP?
- The key business areas that NLP can support in payer organizations
- The different options that are available to health plans and payers as they look to consolidate on an enterprise solution for NLP.