With the modern technology inclusion in the health care ecosystem, payers will have to be coerced to adapt with many abatements coming sooner than later. Health care payers are redefining their role to deliver higher value-based care to members as they transition from a fee-for-service model to a value-based care model. Payers are also getting extra cautious in the audits and penalty sphere.
What is a retrospective audit?
A retrospective risk adjustment approach aims to dig deep for claims which were not passed, especially for those high-risk chronic conditions that can uncover reimbursements that were left out.
Health insurers use a cost-containment mechanism to determine whether overpayments on claims have been made to a particular physician practice.
But there is always the tug-of-war question: Do you want it fast or do you want it right?
This question is for value-based care payers who file for health care claims for payments.
Revenue cycle management health care companies need to ensure accurate patient chart review audits to keep the revenue streaming flow. Any delay or unrecorded diagnosis codes will automatically hamper your revenue stream flow.
CMS expects the accuracy of claims to be 95 percent!
However, would you consider timely submission at the expense of accuracy?
How is the retrospective risk adjustment audit solution helping Medicare organizations?
With the advancement of electronic health records and artificial intelligence, many health care payers can now perform more audits in less time with accuracy.
A retrospective audit helps health care revenue cycle management companies to collect, analyze, and communicate accurate claims or reimbursements.
So what’s the modern retrospective chart audit solution?
Natural language processing (NLP) powered by artificial intelligence solutions is the buzzword for a retrospective approach in the health care ecosystem.
Medicare organizations should be getting ready to enhance accuracy in risk score derivation – mostly the dependency here is on the coders or coding service providers, which creates mountains of overheads, paperwork, and administrative hustles.
Digitization and adaptation to AI will bring the desired accuracy, reduce costs and improve productivity. Many health care payers are exploring interest in AI solutions to further streamline the processes and free up human resources. Implementing such efficiencies will result in bottom-line savings.
The irrefutable goal of this digital shift is to break down the barriers and allow information to accredit a refined and enhanced healthcare framework.
Payers in healthcare have recognized the importance of modern AI technology to improve health care interoperability, data accessibility, and transparency in health care delivery services.
RAAPID. AI is offering a multi-purpose risk adjustment solution designed based on decade-long clinical trained knowledge graphs that ease the chart retrieval process and ensures enhanced chart review workflow for improved revenue cycle management of your health care organization.