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An industry forerunner delivering accurate, compliant, and efficient AI-powered risk adjustment technology: RAAPID.AI

An industry forerunner delivering accurate, compliant, and efficient AI-powered risk adjustment technology: RAAPID.AI

Healthcare broadly and significantly impacts every single person on Earth. RAAPID.AI believes that it can make it better…a lot better. The company’s goal is to consistently deliver solutions that allow healthcare organizations to meet the demands of the radically changing Risk adjustment landscape. The healthcare environment in the US is slowly transitioning from Fee - for -Service model to Value-Based care, and it genuinely addresses the disquietude of Payers, providers, and medical coding service companies.

With RAAPID.AI’s close to 2 decades of exposure and experience in maneuvering & managing the US medical records with its in-built, field-tested, and commercially successful clinical NLP solutions, the company is market-ready and motivated to build customized technology solutions for Risk adjustment solutions to embrace this shift to value-based care model. For over a decade, we have experienced - Healthcare needs a major shift - a major push to address the gross population health concern. Risk adjustment or Risk score derivation is not new in the US market, but RAAPID.AI, with its Deep knowledge and hands-on experience in NLP and AI, wishes to metamorphose and reimagine the Risk adjustment space to offer better, error-free *, less time-consuming structured data. Thus RAAPID was formed (RAAPID - Risk Adjustment API Delivered) on the 4th February 2022.

In conversation with Chetan Parikh, President of RAAPID.AI

Q. Can you explain your services in brief?

The ability to access the free-form text in clinical charts enables healthcare organizations to harness insights from unstructured data and understand the chronic conditions of members faster and more accurately. This gives a holistic view of population health so they can better target care and improve financial outcomes.

With RAAPID – Unlock the power of unstructured texts with access to the Right Data at the Right Time to the Right Person. RAAPID is a completely integrated, AI-powered disease understanding & extracting engine that operates on any patient data, including the free text and unstructured content, to capture the complete Risk of the patients.

Health systems and Insurance companies use RAAPID to understand the data, navigate to the accurate risk score and the compliant codes, and in particular, thrive in the ever-changing value-based care environment. Our technology is built to make customers’ experience positive, effective, and efficient. It works for you, and you do not have to work around the technology.

Our goal is to make human activities more meaningful. The idea is not to promote any manual reviews of basic information and Make human experts do what matters – Interpretations & Excellence of care. To sum up, overall we are targeting to provide the following to our Customers - Health care IT vendors, health plans, and providers:

  • Manage Risks
  • Improve operational efficiencies
  • Transform abstract dating to actionable insights
  • Support quality initiatives
  • Comply with regulations

Q. How does your solution align with the existing medical practice and affect health care costs?

Healthcare delivery is gradually Mutating! Over the past 20 years, the usage of computer-assisted coding systems has unremittingly increased across the healthcare industry to cope with the increasing intricacy of coding diagnosis and treatments. More recent versions of computer-assisted coding systems have incorporated state-of-the-art artificial intelligence methods to intensify the system’s ability to peruse the clinical documentation—charts and notes—and identify which codes are relevant to a particular case. This is the right more!

RAAPID’s AI-driven solutions are already helping risk adjustment chart retrieval and coding review organizations streamline multiple administrative tasks, including clinical natural language processing (NLP) for medical records retrieval and retrospective chart review risk adjustment for Payers (Insurance Companies) and Medical Coders.

RAAPID’s AI-enabled analytics can transform the chart review operations because of others’ accuracy when trained with large amounts of data. It modernizes the traditional chart review process by shifting the focus from the m volume of charts targeted to the o precision targeting of charts. The result is fewer chart retrieval requests, which lowers provider abrasion and increases the efficiency of each review. Hence, More productivity and better ROI.

Q. Experts predict that failure of trusted algorithms is bound to happen. How can you make your solution foolproof?

The Digital game is quite Superficial. The word ‘Hundred percent error-free delivery’ from a human or a machine or even from a robot is a hypothetic and subjective term. As every organization set up is Unique, the solutions offered by vendors are bound to be different. We have a unique Process to try to eradicate many errors as possible, starting from the discovery call, understanding the setup, its needs, and pain points then to figure out the ‘Math’ to help them address the issues. Our algorithm is designed in such a way that it observes coders/reviewers’ actions upon our AI/ML/NLP recommended codes evidence and it is a continuous learning and implementing process to achieve a successful “go live”

Q. Do you have any new services ready to be launched?

The healthcare fraternity is struggling with poor data quality and inaccuracy in Insurance premiums - which is nothing but the aftermath of using non-competent Coding tools for Risk score derivation. For any Medical payers, to Develop & support programs, Member Outreach, Rewards & Incentives for Providers & Members, adhering to best coding practices are the key pointers to achieving Productivity. RAAPID addresses these with its Retrospective chart review services which are enabled, easy to use with a 3 Click Dashboard with lesser human interventions, cost-effective, and most importantly, error-free. In addition to our SaaS application, we are also offering the solution as APIs, which could be used to integrate our solutions with our Customer’s workflow solution directly.

We are also reimagining the digitization of the Provider’s side as well. Our Engineering team is working proactively to launch and implement Prospective Risk adjustment solutions soon. In our prospective solution, as part of the pre-visit workflow, our NLP engine will analyze the medical records along with other documents like lab reports, pharmacy reports, supplemental, durable medical equipment, claims, and membership details; and it will identify the clinical entities. We are connecting the dots using the knowledge graphs and suspect that the patient may have conditions that are not yet captured & documented by the care team. During the patient’s next face-to-face visit with the physician, the suspect conditions list will help the physician to verify and confirm/reject the conditions, in addition to reviewing the treatment & the status for recaptured conditions and missing conditions.

Meet the leader behind the success of RAAPID.AI

Chetan Parikh, President of RAAPID.AI is a technology-driven dream chaser and an entrepreneur. At heart – he is a technologist, trying to make this world a better place by successfully ushering cutting-edge AI solutions to the healthcare market for almost two decades now. He is a Serial entrepreneur with experience in large-scale business development, revenue cycle management, and, Tech innovation, mainly in SaaS technology solutions, especially in the n healthcare and AI domain. RAAPID- the Next Gen NLP-powered risk adjustment solution is Chetan’s latest venture. RAAPID uses some of the world’s most sophisticated and advanced natural language processing technology. It has been tried and tested on millions of clinical data sets to ensure utmost accuracy.

“RAAPID’s Goal through its technology is not to replace human interventions but to make human interventions smarter so that patients have better healthcare”

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