Nayya and Workday announce strategic partnership
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Navigating supplemental health insurance claims shouldn’t be a headache—but for most people, it is. From understanding what’s covered to actually filing and securing reimbursements, the process can feel confusing, time-consuming, and, frankly, not worth the hassle.
That’s why we built Nayya Claims–to take the headache out of supplemental health insurance reimbursements, ensuring employees get the benefits they are entitled to with minimal effort. But creating an automated, scalable, and intelligent system that works across different insurance plans and employer offerings wasn’t easy. We’re excited to share a behind-the-scenes look at how we built it.
Every time you visit the doctor, pick up a prescription, or receive medical care, it generates a lot of data, such as claims, insurance details, plan rules, and more. The problem? All this information lives in different systems, and none speak the same language.
Operationalizing Nayya Claims requires standardizing and processing various data sources with no common primary keys. Dealing with dissimilarities and caveats at the tail end of our pipelines ensures that we can validate data before feeding our rules engine. These cleaned and standardized inputs work together as the “raw material” to identify opportunities to file claims against supplemental insurance plans.
At the core of Nayya Claims is our rules engine. The Nayya Claims rules engine evaluates these data sources against each other to identify hidden reimbursement opportunities. The rules engine ingests medical and RX claims data using a structured system of labels, rules templates, and comparison values to establish facts and execute complex logic. This includes identifying pre-existing conditions, elimination periods, and medical history to ensure that only eligible individuals receive relevant recommendations.
By evaluating the member’s full encounter across individual claims and their prior medical history, we significantly improve our ability to spot opportunities that incomplete or missing source data might have otherwise missed. Additionally, the rules engine supports plan configurability based on employer-specific offerings, ensuring that eligibility determinations align with the nuances of different plan structures.
From a technical perspective, the primary challenge was creating a system that could handle large amounts of data from disparate sources securely and accurately. We focused on building a robust and flexible architecture that can adapt to different partner and employer needs and that could be grown and scaled seamlessly over time as we look to integrate with additional existing systems.
The original requirements for the rules engine stemmed from plan certificates from our flagship partners. To create a scalable and flexible solution, the team worked collaboratively across multiple disciplines, including data engineers, data analysts, software engineers, and products, to abstract these requirements and design a robust methodology. The process involved defining a systematic approach for building a data pipeline and a dynamic rules engine from an initially ambiguous set of requirements. We established a structured framework through iterative collaboration to interpret diverse plan structures, integrate seamlessly with external systems, and enable efficient decision-making. This approach ensured the rules engine's adaptability and long-term viability as a core component of Nayya Claims.
We're excited to see the positive impact that Nayya Claims will have on our beloved users as they get the absolute maximum out of their supplemental health plans.
