Change Healthcare has announced it is collaborating with Health Fidelity on a risk adjustment solution to provider greater accuracy for better compliance and more precise reimbursement.
Health Fidelity’s natural language processing and machine learning technology is embedded into Change’s risk adjustment coding. This is aimed at helping Medicare Advantage plans, Medicare ACO programs, Affordable Care Act plans and Medicaid payers to submit well-supported codes.
WHY THIS MATTERS
Health plans are increasingly under pressure to submit well-supported codes and the cost of non-compliance can be substantial.
The collaboration uses analytics to identify which medical charts to examine for which patients, chart retrieval, coding, and submission.
Health Fidelity’s NLP engine identifies ICD and HCC codes, enabling clients to realize a 20-30 percent increase in risk capture. Coders are better able to prioritize and review charts.
Risk adjustment still remains a predominantly manual and tedious process in which coding specialists review records for unreported risk conditions.
Change already offers payers risk adjustment coding on supporting data from member medical records to help improve risk scores.
The application of NLP technology during the coding phase provides greater accuracy, which in turn translates to better compliance and more precise reimbursement, Change said.
ON THE RECORD
“Risk adjustment requires a high level of clinical data acquisition and careful analysis of millions of medical records to assign the proper diagnosis code to claims,” said Doug Duskin, senior vice president and general manager, Clinical Review, Change Healthcare.
“This added capability is exciting as we expect it will lead to better gap closure and improved quality of care and outcomes for members.”
“The resulting combination of analytics, artificial intelligence, and services allows us to customize our solutions to meet customer goals and improve overall performance and outcomes,” Steve Whitehurst, Health Fidelity CEO.
Email the writer: email@example.com