A Global Newswire Report projected that the market for natural language processing (NLP) in healthcare and life sciences was estimated to be worth $2.2 billion globally in 2022, and that it would only continue to grow at a compound annual growth rate of 27.1% between 2021 and 2027 — reaching 7.2 billion in 2027.

So, What Exactly Is NLP?

Natural language processing, a branch of artificial intelligence (AI), is the ability of computers to understand and respond to human speech or text. In simple words, it’s humanizing technology.

How Is NLP Used in Healthcare?

The majority of patient data is unstructured and takes the form of handwritten notes, transcripts, and other manually typed or printed materials. According to an article by Accenture, “structured health data represents only the tip of the industry’s digital iceberg.”

Natural language processing offers a way to easily navigate through vast amounts of these unstructured data sets. By using sophisticated algorithms, NLP technology has the ability to extract valuable insights that otherwise would have been overlooked. These insights can then be leveraged to improve clinical outcomes, lower costs, and deliver the best possible care to patients. For example, NLP can extract crucial information such as a patient’s risk factors to help provide targeted and timely treatment.

How Is NLP Beneficial for Physicians?

Although a physician’s primary responsibility is to work directly with patients, they often spend a bulk of their time manually inputting or writing notes into electronic health record (EHR) systems. These administrative tasks can be time-consuming and inefficient, contributing to widespread physician burnout.

So, how can NLP help reduce provider burden? NLP-powered computer coding technologies can automate arduous clinical documentation processes by condensing large amounts of patient data into just a few insights that are important to understanding a patient’s health status. This way, doctors can make better conclusions, diagnoses, and personalized care plans for their patients by leveraging this form of AI technology.

How Is NLP Used in Risk Adjustment?

Natural language processing is highly utilized during the coding stage of the risk adjustment process. Manual coding often takes too long, is error prone, and misses too many codes. A powerful NLP coding engine or tool ensures that patient diagnoses are coded to a high degree of accuracy and submitted in a timely manner. Here are three ways NLP can be implemented in a risk-bearing payer or providers’ coding operations:

• Pre-chart prep: Coding staff can leverage NLP-powered technology to proactively review a patient chart before a patient visit, making important information like HCC gaps available to the provider at the point-of-care. This minimizes provider abrasion, facilitates customized patient interventions, and reduces the number of retrospective audits that need to be performed.
• Primary coding review: Before a human coder even looks at a patient’s medical chart, an NLP coding engine can review the chart and surface suspected errors. This way, the number of codes missed by human oversight is minimized, increasing coding accuracy, and speed of code capture.
• Second-level review: A final, or second-level review can use natural language processing to automatically detect codes that were missed or substantiated during other vendors’ prior audits. With services like Final Check Review, each suspected code is manually validated against the Centers for Medicare & Medicaid Services (CMS) and client guidelines by in-house coding experts. In this process, any unsubstantiated codes are removed, creating incremental RAF lift for previously missed HCC codes. Our second-level review service achieves a coding accuracy rate beyond the industry standard of 95%.

To learn more about Episource’s end-to-end risk adjustment offerings, visit our Episource Clarity Platform page.