My name is K, and I will be presenting on healthcare. Im probability. I would like to start with a simple question. What happens when patient arrives in an emergency room and their medical history isn't accessible? Too often doctors have to make decisions without knowing about prior allergies. As results or treatments. This isn't an IT challenge, it's a human challenge, and this is exactly what healthcare interoperability is about, ensuring the right data reaches the right hands at the right time. So today I'll walk you through on how platform engineering principles can help us solve these interoperability challenges. We'll cover the complexity of healthcare data exchange, the architecture we use to address the exchange. How can the new cloud native and AI powered solution help some of the real world case studies? And by the end, I hope to show you how engineering choices directly translates into improved patient outcomes. The healthcare interoperability challenge, healthcare data is fragmented, spread across legacy EHRs, lab, imaging systems, even personal applications. Traditional point to point can't keep up the scale. Data formats vary. Now, there are certain HL seven fire custom schemas. There is a high security and compliance demand and solution needs to scale from small clinics to massive regional networks. And when systems don't talk to each other, patients pay the price. We have. A lot of challenges with respect to having an emergency treatment without any prior history. There is a fragmented care in care coordinations that there are duplicate test because the test and the priorit test information are not available for the doctors to provide the right care at the right. So in order to address this, we have a platform architecture for healthcare data exchange, which is a multi-layered architecture that separates the concerns while providing flexible integration capabilities. Data ingestion layer handles connections to diverse healthcare systems. Through standardized connectors. Data transformation helps to normalize formats and also with the increasing AI capability, this can, the mappings can be achieved in a short period of time. Orchestration layer basically will help with validation of the business rules. No consent if there are any, and any conflict resolution. The last but not least, the application interface layer is primarily providing access to this information using FHIR compliant APIs that applications can rely on. This isn't just a technology stack. It's about making sure that when a specialist, a family doctor, and an ER physician, all treat the same patient. They are not working in silos. They are looking at the same information about the patient and providing the right care that is required. There are also cloud native strategies that we can apply, and this gives us agility in the healthcare that is desperately needed. Microservices and Kubernetes basically can help from a scalability aspect. Serverless computing for variable workloads, container ization, and also infrastructure as a code for consistency and compliance. And obviously as we move to cloud, there are also an AI enhanced data mapping and transformations, so ai. In itself can significantly reduce the complexity and cost of implementing interoperability solution. Traditionally, mapping requires a lot of manual effort to identify the relationship between different data schemas and develop rules. There are a lot of medicine learning algorithms and NLP that can help with. The matching semantic matching for the mapping purposes, and also the NLPs are widely available to translate the free text clinical documentation and providing the ability to search and provide analytics on the documentation. Under the notes in this section I cover. Briefly about security and compliance architecture, of course, in healthcare security isn't optional. It has to be integrated part of the solution. So we implement federal identity and single sign on there is always fine grain authorization, which is provided by role because different types of healthcare providers require access to different categories of patient information based on their role in patient care. Emergency physicians may need broader access or specialties, require only access to the relevant information. The data has to be encrypted, end to end in the solution from transmission and storage. Even if intermediate systems are compromised, the data provides automated rotation and secure distribution of encryption keys. That is a comprehensive audit. Logging capture all data access and modification activities to support compliance, reporting, and secure monitoring Generates detailed records without manual intervention, while providing real-time alerts from suspicious action pattern. This isn't about compliance, it's about trust. Trust between providers, and most importantly, trust. With patients. These are some of the real world implementation case studies that I would like to walk through a regional health information exchange. Basically, HIEs are the organization which connect hundreds of providers now gives ER doctors access to critical patient histories. In minutes and seconds, there are multiple hospital consolidation effort. Used this approach to migrate system gradually without disrupting care and obviously patient facing apps built on smart using fire APIs and giving individuals control over their complete health record. Obviously while we have all of this that has to be monitoring and observability strategies that has to be aligned with all of the implementation. So talking through this while we have the fire APIs, the application performance monitoring tracks API response time. Data transformation throughput and system resource utilization. Automated alerting ensures immediate notification when metrics exceeds threshold. Data flow monitoring ensures information moves through information pipelines as expected. It tracks messages, processing rates, success rates, and end 20 de latency. There's also an error in tracking analysis which has, which is done comprehensively. To ensure if there is an anomaly detected within the data that has been tracked and resolved in it while we build the solution. We also need to ensure that the deployments are automated and there are best DevOps practices that are adapt. So healthcare and interoperable platform benefits significantly from this automated deployment practices that reduce human error and enabling consistent repeatable deployments across different. The complexity of healthcare integration solution makes manual deployment processes error prone and difficult to maintain as systems evolve. Automated deployment pipelines ensures that changes are deployed consistently by also providing rollback capabilities if there are any issues discovered. So while we talk through. The deployment, obviously from an operationalization, what comes as the performance optimization and scalability, so as healthcare interpreter platform handle significant data volumes, transaction rates, while meeting response times suitable for clinical workflows. So we want to make sure the data processing is optimized. Efficient. Transformational algorithms minimize computational resources while maintaining accuracy. There are also able to handle parallel processing approaches for the increasing data volume need. There are also intelligent caching strategies to ensure that the same or the similar set of data is cached within the system and is not processed repetitively. Database partitioning, indexing strategies and query optimization enables platforms to maintain, perform while accommodating growing data requirement and historical data that must remain accessible. Load balancing and auto-scaling ensures that perform platforms can handle variable demand pattern while maintaining cost efficiency, auto-scaling algorithms, and just computing resources. Based on demand while ensuring capacity for traffic spike. So where are we headed from a future perspective? Obviously there are blockchain concepts that can be used in healthcare to ensure there's a tamper proof audit and patient control data sharing. Through challenges around performance, scalability, and compliance remain. There are advanced AI capabilities using LLMs that show promise of improving clinical documentation processing, and I'm enabling more sophisticated and natural language interfaces. So while closing, let's remember, interoperability isn't just about system talking to each other. It's about ensuring people, patient, doctor, nurses, have what they need to make the best possible decision. If we as engineers, architects, and innovators can design platforms that connect these dots, then we are not just solving a technical challenge, we are improving care, reducing cost, and in some cases even saving lives. Thank you. A good.