In Today's Risk Adjustment Scenario, HCC Coding and NLP delivers Accuracy, Efficiency, and Control

Value-based care has evolved as an alternative to the conventional fee-for-service paradigm, emphasizing quality above quantity. Consequently, high-tech Risk Adjustment Solutions are becoming increasingly popular among health plans for premiums and overall financial performance, prompting them to seek approaches to improve the effectiveness and ROI of their risk adjustment plans.

Value-based care (also known as Accountable Care and Population Health Management) has gained popularity, owing to the fact that the value-based compensation model incentivizes clinicians to deliver the finest treatment at the lowest possible cost. As the term implies, the patients are getting more value for the money.

Risk Adjustment Solutions employ Hierarchical Condition Category (HCC Coding) and Natural Language Processing (NLP) to formulate a complete Risk Adjustment action plan that engages both clinicians and patients in a diverse range of value-based insurance coverage initiatives, such as ACOs, Direct Contracting (CMS), Comprehensive Primary Care Plus (CPC+), and many others.

The Use of HCC Coding Induces a Reimbursement Transition

HCC Coding is critical to the financial viability of a healthcare organization. When HCC codes are properly recorded, they generate an accurate representation of a patient's condition. Furthermore, the application of HCCs frequently results in appropriately increased remuneration to meet the expenses of providing care under value-based policies.

By using a patient's diagnostic coding history, the HCC Coding procedure generates an RAF score for a patient that indicates his or her health condition. This score is then multiplied by a base rate under Medicare Advantage to determine the Per Member Per Month (PMPM) capitated compensation for the near term of coverage. The fixed cost is estimated when this is averaged throughout an entire payer-defined demographic.

Enhancing Risk Adjustment With NLP

Natural language processing (NLP) can interpret unstructured patient information in useful medical information to assist healthcare organizations in efficiently identifying risk, care gaps, and improving both qualities of care and economic performance. Without a question, NLP-enabled new tech is becoming a valuable tool for achieving risk-adjustment success.

NLP-aided risk stratification adds significant value since it allows coders to target on the suitable members first and then navigate their way down the priority list.

NLP technology evolves coder efficiency as well as quality. The first pass review is performed by NLP, which provides coders with a collection of diagnosis codes to evaluate while decreasing the quantity of data they must first submit.

Through enhanced chase targeting and automated data extraction, NLP can expedite the data recovery procedure, eliminating or dramatically reducing chart chase difficulties and saving both sides time, expense, and irritation.