Reviewing patient chart data to identify potential missed conditions takes a lot of time—and clinical staff and providers are simply too busy to comb through all that data manually. Natural language processing (NLP)-derived suggestions can alleviate this burden by combing through historical clinical documentation to deliver a list of potential missed conditions for providers to evaluate.
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. These suspected conditions are presented for clinical review and validation, 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 providers deliver better care outcomes, improve coding and documentation accuracy, and ensure complete and compliant risk capture.
