Joining my session at conference JavaScript over. Modernized large scale healthcare data engineering platforms from CMS 2000 claims submissions to air driven violation pipelines that process millions of transactions data. My goal today is to share how modern JavaScript frameworks traditionally used in web apps can be repurposed. AI Ready Modular E violation systems. So these talk sites, the interaction of healthcare data compliance, EL engineering and software architecture. So finally, you'll see how we lightweight JavaScript core can scale to enterprise with the performance, agility and transparency our industry demands. So let's go and deep dive into our project. What is the healthcare claims and what is the current state and what is the impact? So let go to the current state. So healthcare organization handles millions of claims each month. So each one must satisfy hundreds of business and compliance rules under the ANSI X 2 8 37 standard. So the ANSI X two standard is a separate format, which we develop the files to CMS. It has a different structure and large elements and that need to be satisfied, then only the file is going be processed by the cms. So the challenge is not only the. Claim formats differ between institutional, professional, and dental types, and every payer add its own edits. So in many environments, validation is still manual or semi-manual relying on rules, spreadsheets, or legacy engines written years ago. So when new regulations that are updating the systems can. Take weeks, sometimes months. So the results, high rejection rates, delayed payments and mounting costs. So staff spend hours correcting data instead of focus on patient care or analytics. So this stage, the stage for a new approach on that agile rule drive and technology. And now what will be the impact? So a reactor claim does not just impact accounting, it disrupts the entire revenue cycle. For example if you're sending a file to CMS with 5,000 claims and one fail, one claim just file because of the structure or some missing elements. File will be failed. So what happens is when we get the we get a nine, nine written file in which we need to check and see what is the failed component, and then we need to figure out what, whether the strict structure issue or it's a data issue. Or, and then we need to send a clip. So sometimes this may take one, one day, two day, or weeks or months to figure out, because if it's a data issue, we need to figure out from where the data is coming, need to update the data from the source, and then it should flow through the system. So there's a reason what happens. The study says studies shows that. 15 to 20% of the claims are denied of first submission, and roughly one third of those are never recovered. That's billions of dollars lost annually across the US healthcare system, so each denial. When claim is sent, which a denial triggers a chain of manual tasks we need to review, correct them and resubmit, and often take 30 to 45 days to resolve. It may take more also, so not only certain time. So meanwhile, provider cashflow servers and client suffers while the amount is not going from one end, one floor to the other floor. So by building smarter valuation layers that detect errors before claims reach the payer organizations. Can reduce Nels by up to 70%. So violation is not about compliance, it's about financial stability and operational efficiency. So why is the JavaScript a centric approach for an approach for violation so modern than JavaScript? Frameworks offer unprecedented flexibility for building healthcare ation system. Unlike monolithic solutions, a modular just architecture enables. Rapid operation, easy maintenance, and seamless integration with existing infrastructure. This approach leverages declarative business rule engines that separate violation logic from implementation details. Developers can define rules in human readable formats while a framework handles execution, error handling, and performance optimization. So the metadata driven designs means violations. Rules live as configuration rather than hard code logic, enabling business to participate in rule management without requiring deep technical knowledge. So what are the core violation capabilities that we are gonna use in this AI driven 8 37 transmissions? So the four things we are going to do is eligibility verification, provider credentialing. State logic coding accuracy. So let's deep dive. What is eligibility verification? What is provider running? And the other two things. So let's explore the core validation capitals and make this frame for powerful practical for healthcare users. So we begin with eligibility verification in many claim system error start because patient coverage is not verified in real time. Here a JavaScript player connects directly to payer APIs. Confirming active coverage, benefit limits and service eligibility before a claim ever lose the provided system. Think of it's an instant checkpoint that reduces preventable den denial right at the source. So if anything is missing or anything is wrong in the claim, so in the initial ation itself, we don't even send the claim to the CMS. So that way we initially check from our system and then we rectify that, and then we deliver to the. CMS. That's how we save the time and money. And next is provider credentialing. So this step automatically validates NPA numbers, licenses, tax, money codes, and vector status using data from NPS or CAQH directories. So when a provider's license expires or the out of network, the framework flags it immediately. So manual lookup, no surprise later. So that's fair. If any NP is missing or NP is wrong, or NP addresses missing or NP is unknown, issues may be present in our system or, but when you're directly calling the APIs from NPS or cqs, so that way they'll be always active and there'll be always a primary address and physician address and also the secondary address for those NPIs. There be, there will be a little, very little chance of failing the claim with a missing NP or missing address or missing tax money codes. That way we can figure out the issues before sending it to the CMS itself. So then we have data logic violation. This is where hundreds of common timeline errors are caught. It checks the dimension, dates come before discharges, that the dates of birth is realistic. And that services fall within timely filing limits. Suppose if an encounter happens in something of 2020 December, but he's not he's enrolled somewhere in 2025 due to the system entry or something. So basically the claim will not be accepted because of some data entry or somewhere. Some mismatch has been happening, so we need to fix the date logics also. So these small details often make or break claim acceptance. So the last comes the coding accuracy, the engine coex, isolated 10, diagnosis, C, pt, S, and HCPCS codes against official code sets and modifier rules. It even supports. For example, making sure a C PT code that requires A always includes and every claim, whatever we are submitting, CMS should have a C PT code, which is valid. So these CCPT codes or ICT codes every year, CMS changes. A few reschedule your non-schedule. Sometimes every year. CMS also saves you. I CD codes are retired, so we need to make our system to be always updated. So that's the reason we bring it to the configurations and we order, we add as in a violation check so that we don't submit the invalid i CD codes here. CP should be a valid C PT code. Collectively, these modules form a 360 sheet. They don't detect the problems. Explain so teams. So now we'll go to the architectural design. So now let's step back and look how all these capabilities fit together technically. So at the bottom sits the data ingestion layer, the component read raw, a DA thousand files, those long test streams, full loss segments and loops, and converts them into structured Jason objects. Once that happens, every downstream service can read the data like standard key value. Frazzling with Iio syntax. So as we told in the beginning, the ANSI 8 37 structure, so that structure is very tough to read unless who has a lot of experience in the healthcare system or the claims level. The system has lot of loops and structures that no one could understand easily. So converting into the J object. That makes the user to read easily and they can find out what data elements are missing or some segments are missing. So now next, go to the rule engine code. This is a brain of the system. It pulls the right set of the rules based on the claim type, payer and line of business, because the rules are metadata driven. You can add, remove, or prioritize them without touching the code base. That flexibility is crucial when regulations change. So what happens is every year, as we told. The iicd code will be changing some np iicd code changing sometimes some code from risk adjustment to non-risk adjustment code. Some iicd code will be retiring. So all these made as a configuration that would be easy for the, for our system to maintain. So then we have an integration service. So these are external connectors. Reach out to third party APIs or databases, for example, to verify eligibility or NP details in real time. All of them run synchronously, so even under heavy loads. And finally comes the response handler. Once violation is complete this module, format the results to j XML or flat file and route them back to clearinghouse data warehouse or B dashboard. So what's this? So once everything is done, all the violations, vault IC code verification is done, integration service. Then what this is again, the j changes the it's file to the A 37 standard file format. And then send the file to the clearing author, and the clearing author will send the file to CMS. And same data we can use in your dashboards and the warehouses. So the anti designs follows a microservice philosophy, so each component scale fail or evolve independently. That makes it future proof for cloud deployments on AWS Lambda Azure functions all Kubernetes. So now let's go to. How as we discussed before, there are like three multiple claim types. So out of them they're institutional, professional, and dental. So when you go to institutional hospital and facility claims with complex billing hierarchies, revenue codes, and service line details requiring sophisticated violation rules. So what are the rules? They're having? Revenue code violation. Board and discharge statistics. So same way when you go to, when you're coming to eight 30, professional claim generation. So physician outpatient services claims with procedure codes is a mandated thing. Procedure mod should be M and place of service requirements. Leading size violations shows what requirements for institution, what requirements for professional and what requirements for dental. So in the professional also, place of services is mandatory and modify combinations. So because as our, as in the COVID, we got telehealth. So we have a, for a telehealth, we need to specify, we have, we are having spec specific modify. Then only the CMS knows this is a telehealth claim. If it's not, if it's something like blank or, no, the CMS knows, okay, it's not a telehealth claim, it's an, it's an electronic or assessment. That's how they, we can figure out how it's, and the rendering provider checks. So whether the NPI is there or not, and coming to Dental 8 30 70 dental procedure claims with tooth numbering surfaces and specialized code requirements unique to dental practice management. So it's tooth surface violation, CT code accuracy, treatment plan logic. So each claim type requires specialized violation rule while sharing common infrastructure components, making modular essential for. So now here comes the real time and batch processing. So once architecture set, the next question is how do we run it effectively at scale? That's where our dual processing approach comes in. Let's go to the realtime API valuation. So process claims as they're submitted through restful APIs with sub-second response times ideal file clearing hoses, integration practice management systems, and immediate feedback scenarios where providers need instant violation results. So the API layer supports. Both synchronous and asynchronous patterns, allowing system to choose between immediate responses or queued processing based on complexity and volume requirements. Now get into the batch processing engine, how it works, so it handle engine volume, claim files during off pick covers with parallel processing. Perfect for end of day submissions, large provider groups and scenarios where throughput patterns more than immediate response. So the batch engine optimizes resource utilization, truly inline job shape during automatic logic and complement. So error reporting. That groups issues for efficient remediation. So another, what want to say is there's two more design properties, flexibility realtime, for instance, feedback and batch for large scale operation loads. So together they make the system both fast and reliable. So critical qualities in a 24 by seven healthcare environment where ways the data processing is going to be done. We can also do for the further preparation of AI integration. So once we have a metadata driven foundation, we can seamlessly add AI capabilities without rebuilding anything. So the first step is intelligent rule adaption. We can train machine learning models on historical claim outcomes to identify patterns if a certain rule s. Recommend turning it or merging it with contextual conditions. Over time, the frameworks learns from experience. So the second is directive analysis. A models evaluate incoming claims and estimate the likelihood of payer acceptance before submissions. So high risk claims can be flagged for manual review. Suppose if you're getting a high risk ED code or claim, or h ccc, we just make this flag. To double check, and then we send it to CMS. So while low risk ones flow strike through, so this reduces manual intervention and accelerates payments, supply cycles. The beauty is that the, a conce use the same metadata as the rule, so the results remain reasonable and auditable a must in regulator industries like healthcare. So in a healthcare. Any claim is submitted or any once after you submitting the claim. If return the ance and the ICN, everything needs to be auditable because it all depends on the federal and state government ities and everything. Each and everything need to be audited so we can also use our future ready extensions. The validation framework can integrate with blockchain networks to create immutable trails of claim violation decisions. This transparency builds trust between payers and providers while ensuring compliance with regulatory requirements. Smart contracts can automate certain violation steps and trigger payment workflows when claims meet predefined criteria, reducing processing time and administrative overhead. So decentralized violation rules can be shared across the healthcare ecosystem. Creating INDUSTRYWIDE standards while maintaining organizational autonomy. So let's go through, as we wrap up this session, let's pull everything together and reflect on the key ideas we have covered. First, JavaScript has evolved for beyond its front end rules. In this framework, it becomes a powerful engine for healthcare data, forced, modular and complexity, customizable. It's non-blocking, runtime, and rich ecosystem. Make it perfect for real time. And more than JS frameworks provide foundation for building a scalable 8 37 violation that adapts to changing business requirements without architectural rewrites. Just based on the confis, we can do this. And second is metadata driven design. Separate rules from the courts. Business users can, MAL, can validate law can manage validate logic through configuration, reducing it dependency and accelerating response to payer rule changes. Third, API first architecture is what ties it all together. It lets this validation capabilities. Plugs seamlessly into clearinghouse practice management systems and payer gateways. So whether data moves in real time or in it batches, the system build consistent and predictable. So fourth is the AI Ready Foundation unlocks strategic transformation. AI brings break to intelligence, learning from past outcomes to flag potential denies before they occur. So while blockchain introduces verifiable transparency, eliminating disputes, and building trust between payers and the providers, what it does is, so if anything feels, it's like any. It's a higher risk code or it seems like it's a highly billable code. So before sending it to CMS, we just take a stop flag it, send it to double review, and once it is reviews done, we send the CMS. That way, if it rejects, it takes a lot of time to again. Redo this so it's better before sending. We do as a mechanism, we build a mechanism where we can check before sending the claim to the CMS. So ulti ultimately this approach transform this approach transforms violations from a routine comprehensive check pointers strategic capability that drives financial performance, operational efficiency, and patient satisfaction. The bigger message here is that technology choices matter. When we design systems that are modular, explainable, and intelligent, we future proof not only our software, but also the organizations that depend on it. Thank you again so much for being part of this session. I encourage you to experiment to think differently about where JavaScripts fits you in a data engineering world and to connect with, and you can connect with me in LinkedIn for continue to further discussions on the healthcare. Thank you so much for giving this opportunity.