Healthcare professionals know the importance of working with quality personnel and adopting key technologies to ensure productivity and efficiency.
When it comes to selecting technology for the healthcare ecosystem, RAAPID is proving to be the next-gen AI-driven risk adjustment solution for both healthcare providers and insurance companies to achieve accurate risk adjustment factor (RAF) scores for Medicare enrollees within optimal timeframes.
RAAPID, the world’s first personalized and customized AI assistant for risk capture, leverages natural language processing (NLP) technology to help healthcare insurance companies, also known as payers, and medical coders improve the accuracy and delivery of RAF scores.
In addition, the modern risk adjustment solution helps healthcare payers to automate administration workflow and decrease the chart review timings.
With many medical coders and insurance companies that are used to conventional risk adjustment methods, RAAPID can be infused within an existing workflow to enable natural language processing (NLP) technology for identifying and validating Hierarchical Condition Category (HCC) and International Classification of Diseases (ICD) codes in real-time.
Backed up with a team carrying decades of industry experience, RAAPID is reimagining the risk adjustment process for healthcare, insurance, and technology businesses by allowing them to perform chart review workflow in just three clicks.
The SaaS-based solution which is also available in an API, automatically assesses and addresses the overall objective of a retrospective audit by the Centers for Medicare and Medicaid Services (CMS).
CMS’ overall objective of the retrospective audit is to:
- Review the quality of care provided to patients
- Educate providers on documentation guidelines
- Determine if organizational policies are current and effective
- Optimize revenue cycle management
- Ensure appropriate revenue is captured
- Defend against federal and payer audits, malpractice litigation, and health plan denials.
Before the start of each year, each MA organization submits bids to the CMS that reflect their estimate of the monthly revenue required to cover an enrollee with an average risk profile. The CMS compares each bid to a specific benchmark amount for each geographic area to determine the base rate that an MA organization is paid for each of its enrollees.
As a part of the risk adjustment program, CMS consolidates certain HCCs into related-disease groups. For each of these groups, CMS assigns an HCC for only high-risk diseases in a related-disease group. Thus, if MA organizations submit diagnosis codes for an enrollee that map to more than one of the HCCs in a related-disease group, only the most severe HCC will be taken into consideration for determining the enrollee’s risk score.
CMS multiplies the scores by the base rates, to sum up, the total monthly Medicare payment that an MA organization receives for each enrollee before applying the budget sequestration reduction.
For healthcare payers to perform risk adjustment without technology can lead to:
- Use of more manpower to meet CMS documentation guidelines and submission deadlines
- Missing on identifying those unreported diagnoses for RAF scoring opportunities
- Costly errors while performing those repetitive risk adjustment tasks.
A tech-driven RAF score derivation workflow is pivotal for healthcare organizations, insurance companies, medical coders, and health providers. RAAPID’s HCC risk adjustment solution will bring automation in chart retrieval, review, and validation of HCC and ICD codes. This way Medicare organizations can ensure a retrospective audit approach to determine whether selected diagnosis codes submitted to CMS for use in CMS’s risk adjustment program have complied with Federal requirements.
Why RAAPID?
RAAPID strongly believe that MA organizations should have access to their MA enrollee’s data anytime, from anywhere. The modern medical coding solution is also configurable based on the MA organization’s ever-growing needs.
Unlock the next-gen AI-based, NLP-powered risk adjustment solution and optimize reimbursements from the CMS.