Good evening everyone. I'm Bra Haredi Ham, a developer with the Department of Veterans Office. Today I'm excited to share how advanced Observability is transforming healthcare IT system. In the next 15 minutes, we'll explore why observability is the backbone of resilient distributed healthcare systems, diving into challenges, solutions, and real world impacts. Let's get started. Healthcare depends on complex, interconnected IT systems, but 73% of the organization lack comprehensive visibility into these distributed systems. These visibility gaps can lead to delays or disruptions in patient care. For example this chart shows how 73% of healthcare organizations struggle with the system. Blind sport Advanced Observative framework address this by reducing the meantime of the resolution by 84%. This means issues are identified and fixed faster, minimizing patient impact. Additionally. Integrated distributed tracing with the clinical workflow, cut troubleshooting time by 67%, allowing IT teams to focus on proactive care. This graph highlights the dramatic drop in resolution time. Let's compare traditional monitoring with the advanced observability and see why this shift is critical. Traditional monitoring in healthcare often relies on basic system level metrics like CP usage without context. This leads to fragmented visibility, reactive troubleshooting, and no correlation with the clinical workflows. For instance, a system crash might only be noticed after it delays patient care. In contrast, advanced Observability provides end-to-end visibility across care pathways, unified tracing of patient journeys and proactive analytics to prevent disruptions. It directly links system health to patient outcomes, ensuring it issues don't compromise care. This dashboard shows how observability integrate metrics for real-time insights. This is a shift in game changer for healthcare reliability. Healthcare observability is not just about tech, it's about patient safety and compliance. First, we need HIPAA and GDPR complaints, monitoring and encryption, audit trials, and role-based access to productions, to data. Second, analytics musing technical metrics to critical outcomes so we can prioritize issues that affect patient directly. For example, a CS slow system could delay critical care and observability. Help us catch that in real time. Here is an example of HIPA compliance monitoring interface. Finally, high availability is non-negotiable. We need fault tolerance system with 99.99% up, down, and automated failover to ensure critical visibility even during outages. This requirement to ensure observability support life critical systems. Let's look at the real world example of how this principle were applied in one critical care scenario. Unpredicted system latency across interconnected services was slowing responses times putting patients at risk. To address this, we implemented end to end. Distributed tracing with custom in infrastructure across all clinic service boundaries. As a result, a 67% reduction in troubleshooting time, allowing us to resolve issues before they escalated to clinical disruptions. This means faster, safer care delivery. This graph shows the drop in troubleshooting time post implementation. This case study shows how observability directly improves patient outcomes. Next, let's see how observability performs under pressure. Like during p peak load, during high traffic period peak time volumes systems are under immense strain. Our data shows system reliability dropping by 75% at peak load without proper observability. This chart shows the reliability dipping as patient volume spikes. By implementing advanced telemetry collections, we achieved a 58% improvement in system reliability. During this period, real-time adjustment based on observability data prevented service degradation, ensuring critical healthcare operations continue smoothly. This highlights how observability maintains stability when it matters most. Now let's explore how AI is revolutionizing observability in healthcare. AIOps is transforming healthcare observability first. Predictive analysis can anticipate system failures 24 to 48 hours in advance with the 93% accuracy, allowing proactive fixes before patient care is implemented. This model predict failure with high accuracy. Yes. Second intelligent filter reduces false post to alarms by 71%, focusing IT teams on clinically significant issues. Finally, automated remediation enable self-healing system resolving complex issues in minutes and cutting it interventions by 64%. This dashboard prioritize critical alerts AIOps, make observability smarter and more efficient. Let's see how HBase observability takes this further. HBase observability brings monitoring close to the point of care. It reduces latency by 83%, enable critical distance in milliseconds. It also ensure continuous monitoring during network disruptions vital for unrepped uninterrupted care. This shows age monitoring in action. Additionally, local data processing enhance privacy complaints by handling since two data at the source. Finally, real time anomaly detection at the point of care lets clinicians address issues before the affect patients. So how do we implement this? Let's look at the strategy. Implementing observability requires a structured approach. First assessment. For assessment, it requires audit existing systems, identify gaps in clinical workflows, and define healthcare specific requirements. Next, implementation. Deploying distributor pricing, integrate metric logs, and traces into unified platform and ensure HIP and GDR compliance. This workflow outlines our implementation steps. Finally, adoption train clinical and IT staff foster a data driven culture and build a cross functional responses. Teams this strategy ensure observability aligned with the patient care priorities. Let's see how these components fit into a broader framework. Our healthcare observability framework has three layers, the technical metrics layer, monitoring infrastructure applications, and the network with the healthcare specific threshold For critical care, the business impact layer correlate system performance with patient outcomes, optimizing treatment, timing. The compliance layer ensure continuous HIPAA and GDPR complaints with automated reporting. Let's wrap up with the key take ofs to summarize advance observability, reduce meantime to resolution by 84% and boost system reliability by 76% directly improving clinical outcomes. It must be a core architecture principle, not an afterthought. We need to link technical metrics to patient care and design solutions with the compliance first, by providing observability, we built resilient systems that save time. Thank you for your attention. Observability is the backbone of a reliable healthcare system, and I hope this presentation showed a transformative potential. Thank you.