Most healthcare organizations don’t have the time or resources needed to manually sift through mountains of clinical data. Natural language processing (NLP)-derived suggestions can alleviate this burden by helping coders identify miscoded conditions or conditions with no HCC attached that are supported by clinical documentation. But NLP isn’t just effective at closing coding gaps—it can also help predict gaps so providers can take proactive action.
Edifecs NLP Suspecting applies NLP, machine learning, and highly evolved clinical engines to highlight risk-adjustable conditions that are currently unconfirmed but are likely to exist based on advanced predictive modeling. Suspected conditions are then presented for clinical validation and review, enabling clinicians to intervene early and address these conditions in the next visit or with proactive patient outreach. By providing a comprehensive picture of patient health status, Edifecs NLP Suspecting helps health plans ensure complete and compliant risk capture and support better coding and documentation accuracy.
A Forward-Looking Approach to Risk Adjustment.
Learn how Edifecs can support more accurate documentation, more complete risk capture, and better care outcomes.
Confidence scoring determines the likelihood of a condition being present
NLP Suspecting has a 95% accuracy rate for risk category capture
Easily ingest clinical and claims data for faster review by clinical teams
