NLP-Targeted Second Level Coding Review
Is 95% Coding Accuracy Good Enough?
With increasing financial pressures and compliance audits from OIG and CMS, even health plans with 95-98% coding accuracy may be putting themselves at risk. In an era of uncertainty, health systems and healthcare providers are exploring technologies to decode medical records and deliver real time insights to improve the patient experience.
Natural language processing (NLP) can be used to support clinical decision making for providers looking for point of care solutions that simplify complex patient information. When it comes to coding accuracy, NLP and machine learning can be used to automatically extract risk factors associated with a medical record and help prioritize the most impactful add and delete codes.
Like any technology, NLP tools have their advantages and differences, so it is important to understand the challenges of integrating NLP technologies into a clinical care program. Many NLP applications intend on bringing order to unstructured data, but still many fall short.
Limitations of NLP in Healthcare:
Artificial Intelligence Meets Human Expertise
An effective NLP tool for second level coding review combines targeted machine learning capabilities with the power of real human coders. Which is why Episource developed a proprietary AI smart-engine that is trained by the industry’s largest bench of full-time coders who code over 10 million charts each year.
Now, healthcare providers and health systems can ensure full coding accuracy and compliance—all in a single SaaS platform.
EpiFinalCheck by Episource combines the power of best-in-class NLP technology with expert HCC coders to ensure an accurate and cost effective review of medical records.