RAAPID
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LOUISVILLE, Ky. - March 15, 2022 - (Newswire.com)
RAAPID, the world's first personalized AI assistant for risk capture, is providing healthcare providers and facilitators across the United States with access to the most powerful risk adjustment platform on the market. The platform, which was launched in February 2022, gives payers, providers, coders, and hospital executives the opportunity to accurately structure key data points to effectively determine patient risk scores and calculate premium totals.
Healthcare organizations, insurance providers, and insurance holders all rely on payers and coders to extract patient data. These individuals either act as freelancers working in conjunction with the healthcare organizations themselves, or as in-house staff members. In either instance, there is a common challenge that these individuals face as they look to extract critical patient information: the data itself is often unstructured.
Because this data is usually shared with the physicians, patients, or with the insurance companies directly, it is essential that it be both accurate and organized. For healthcare organizations, managing risk adjustment is crucial as it directly impacts their return on investment (ROI). Improper risk scores and inaccurate premium totals can be extremely detrimental to the financial health of any healthcare institution.
Through leveraging its innovative deep Knowledge Graph-based Natural Language Processing (NLP) technology, RAAPID reads charts just like any human coder would do, and automatically suggests risk adjustment-relevant codes, eliminating inefficiencies and inaccuracies in the process. By streamlining manual coding workflows through the platform, users can expect significant improvements in hierarchical condition category (HCC) Coding and risk adjustment factor (RAF) scores. Complete with integrated codebooks and comprehensive analytics and reporting, RAAPID offers the most efficient risk adjustment workflow automation capabilities available today.
RAAPID begins by collecting and organizing all relevant clinical documents, including unstructured notes, before converting all scanned images into text format. NLP technology is then used to extract clinical entities. The platform then utilizes patented knowledge graphs and user-adjusted custom rules to identify the entity relationships. After the code validation phase, a final risk score is calculated based on complete, compliant, and accurate codes (International Classification of Diseases 10 and HCC).
RAAPID users can also benefit from flexible deployment options and seamless interoperability, as well as features such as suspected condition recommendations and disease discovery tools in addition to the previously mentioned automatic code suggestions.
RAAPID's unique combination of advanced technologies allows organizations of all sizes to enhance productivity and achieve as high as 10x ROI. For more information about how RAAPID is quickly transforming the risk adjustment landscape, please visit https://www.raapid.ai/.
About RAAPID
We are reimagining risk adjustment for healthcare, insurance, and technology businesses.
With a comprehensive workforce of over 500 members who are industry veterans, based out of India and the U.S., RAAPID's pedigree is built upon Trust, Tech Competence, Stability and Tech Innovation.
Healthcare doesn't stop at 5 o'clock. Neither do we. We know this because it has been close to a decade now. We want your organization to be successful and have accurate reports when you need them. That's why we make Patient service a priority and ensure our technology stays up-to-date to avoid errors entirely.
Every Organization Is Unique and Deserves Personalized AI Solution, and RAAPID is a completely integrated, AI-powered disease understanding & extracting engine that operates on any patient data—including the free text, unstructured content—to capture complete Risk of the patients.
Contact Information. Suparna Das Gupta. suparna@raapid.ai
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Original Source: RAAPID Provides Critical Risk Adjustment Capabilities Nationwide
