Anyone who has been involved in the healthcare system, patient, provider, or administrative staff, would agree its systems for payment, booking, and treatment are complex. People with first hand experience recognize healthcare as an inherently dynamic system, characterized by its ever-evolving treatments, regulations, and patient care models.

A regulation called the X12 5010 standard was mandated by the Accredited Standards Committee (ASC) in 2012 to standardize electronic data interchange (EDI) between healthcare providers, payers, and other entities. This covers exchanges of data like claims, payments, eligibility, enrollment, and billing.  While it was a leap forward in promoting consistent and reliable healthcare transactions involving patient eligibility and cost share, the X12 5010, over time, has come to represent a chokehold on the healthcare system’s natural dynamism and flow of information. Any added barrier to patient verification of benefits should be critically examined. Nirvana asks: is there a better way to deliver information to the populations seeking and providing care? How can we make healthcare payment system better? 

At Nirvana, we believe the solution to this chokehold in information is flexible technology built with Machine Learning capabilities. API solutions built with Artificial Intelligence (AI) and Machine Learning (ML) are the fastest, most reliable, and best path forward to ensure accurate information is captured and delivered to users instantaneously in a readable format. Patients and providers need to have access to accurate real-time cost estimates for care before receiving treatment to avoid un expected financial complications. Regulations should be in place to help patients and providers deliver the best care possible and not hinder them, as they often unintentionally do. 

The ASC X12 5010 Standard: The Paradox of Structure in a Dynamic System

The Benefits Of The X12 5010 Standard

When instituted, the X12 5010 standard improved the interchange of electronic data in healthcare administration by accommodating increased data requirements, improving code sets, and ensuring compliance with regulatory standards. It also sets a standard and continues to bring order to a chaotic environment where clearinghouses, insurance payers, hospitals and other healthcare entities all with their own format of information have to communicate with each other.

The Challenges of a Static X12 5010 Standard

While the X12 5010 Standard brought order to chaotic elements in the process, it has proved to be too rigid and narrow to capture the complexity of results in an inherently complex system with many different actors.  The healthcare ecosystem thrives on adaptation and innovation, and the inflexible format of X12 5010 stifles the flow of nuanced information leading healthcare providers and administrators to create workarounds that are time-consuming and cumbersome.

For example, alternative benefit structures, such as a copay only applicable after a deductible is met, introduce a level of detail that the X12 5010 Standard was not designed to handle, making it out of date. These intricate details are frequently condensed into a generic 'comments' section, which was never intended to carry such a load, leading to oversimplifications and frequent misunderstandings. 

This is the problem that clearinghouse solutions face. To account for the rigidity of the standard, they return nonspecific results because they call only pull predefined elements of data.  And when much of the real information that now matters is in a generic ‘comments’ section, this causes clearinghouses to fall short. 

Solutions in a dynamic system must be as dynamic as the problems they are solving and that is where most clearinghouses' reliance on rigid solutions dramatically limits their interpretations of results. 

The Solution To Outdated Healthcare Communication : Nirvana’s AI Technology Tailored for Digital Health Transactions

Recognizing the specific needs of healthcare providers, after more than two years of iteration and model retraining, our AI has been engineered to process and interpret the full complexity of coverage details transmitted in the healthcare landscape. The technology extracts, analyzes, and presents actionable coverage information and cost estimates that are accurate and up-to-date. By utilizing AI and ML, we can extract meaningful insights from every corner of over 180,000 unique health insurance plans, including the overburdened comments section. 

For healthcare providers and administrative staff, this means:

• Billers spend less time combining through the comments section to find patient information.
• Improved cost-share estimate updates reducing claim denials and revenue lost.
• An improved patient experience that’s faster and provides more accurate cost estimates.

Conclusion

‍As the healthcare industry continues to innovate with new benefit designs and coverage options, our technology is built to adapt to each new option. Our model learns from each transaction, refining its algorithms to keep up with the sector's pace. Through a combination of technology and expert human input, Nirvana ensures that healthcare providers can rely on accurate and comprehensive coverage checks. 

‍