Hello everyone. Welcome to this session on Engineering Intelligence Transforming Healthcare with BI and AI from Data to Outcomes. I'm Ali Krishna. I go by Krishna, and I'm from the institution of Electronics and telecommunication engineers. In today's talk, I'll share how platform engineering is reshaping healthcare. Helping us turn raw, fragmented data into intelligent insights that drive measurable outcomes. We'll explore how clinical, operational and financial domains are converging under a single vision, intelligent healthcare systems that don't just process data, but actively improve care, delivery and patient experience. Now healthcare is in the midst of a fundamental transformation driven by platform engineering. For decades, hospitals struggled with the siloed systems. Data fragmentation and inefficient workflows. But now AI and BI form the backbone of intelligent healthcare systems. We are witnessing standardized integration replacing patchwork legacy systems. Platform engineers are the architects of this revolution, designing systems that unify diverse data and enable innovation. Most importantly, we are moving from technology pilots and aspirations to measurable outcomes, reducing costs, improving clinical accuracy, and enhancing patient satisfaction. Throughout this session, I'll show you how these capabilities are engineered at scale and how they are already changing the healthcare ecosystem. Here is the agenda, and this is our roadmap for today. We'll start by discussing the current healthcare engineering challenges, issues like a data fragmentation, lack of interoperability and integration complexity. Then I'll walk you through the BI and AI platform architecture. What makes it scalable, secure, and compliant? Next I'll share real world case studies where hospitals and health systems achieved a measurable impact both clinically and financially. And finally, we'll wrap up with adoption strategies and explore future paradigms what healthcare intelligence might look like in the next coming decade. So let's begin. The challenges. The average health system today operates more than 16 different platforms, and most of them using incompatible data models. This is a challenge. Over 80% of healthcare data is unstructured, locked away in physician notes imaging or scan PDFs, and it's growing nearly 50% every year. On top of this, compliance frameworks hipaa, GDPR, and new AA regulations add multiple layers of complexity. And when it comes to interoperability, only 30% of hospitals have achieved meaningful integration despite regulatory mandates. These engineering barriers slow down innovation, and unless we address them. Potential of healthcare data will remain untapped. Modern healthcare intelligence platform is not a single tool. It's a layered ecosystem. Firstly, the data integration layer built with the FHIR based APIs, realtime streaming, and ETL or E three pipelines that unify. Structured and unstructured data. Secondly the analytics processing layer where NLP extracts meaning from text dashboards provide actionable visibility and machine learning models, predict patient risk or resource needs, and finally, the delivery infrastructure. Ensuring secure role-based access, seamless workflow integration and explainable AI so that clinicians trust and adopt these tools. When all these layers come together, the platform becomes more than a data system. It becomes a realtime intelligence engine for the healthcare. At the heart of modern healthcare platforms is FHIR, which is stamped as a fast healthcare interoperability. Resources. FHIR has become an essential standard for healthcare integration because. Speaks the language of today's developers using restful API framework instead of outdated rigid data exchange format. And this restful API framework is very popular and known by most of today's developers. And since the year 2021, adoption has skyrocketed by nearly 87%. Hospitals that use FHIR see integration projects completed 63% faster than by using HL seven. And critically organizations realize over three times the written on investment because data flows more freely across systems. FHAR is not just a technical standard, it's the bridge that enables. Secure, granular and scalable access to both structured and document-based data. It lays the foundation for everything from clinical analytics to AI deployment. Now, clinical intelligence covered by AI is no longer an experimental field. It's real, and it's delivering results really. In dermatology, AI achieves over 95% diagnostic accuracy compared to 86% for human dermatologists. In radiology, AI reduces false negatives for lung module detection by 26% directly impacting cancer diagnosis and survival rates, and in pathology. AI speeds diagnosis by 60%, maintaining near perfect accuracy. But these breakthroughs don't happen in isolation. No. Platform engineers make them practical by embedding AI models into clinical workflows so that physicians see decision support to clean their systems just at their fingertips without disrupting care delivery. This is how we scale intelligence rather than leaving it in just research labs. Let's see guest study here. Here's a real world example, a medical center. The emergency department faced severe overcrowding and longboarding times, so they built an integrated BI and AI platform tailored for ED operations. So the results were transformative. Boarding time for admitted patients dropped by 32%, and trash was accelerated by 41%, powered by AI driven production. And the system generated over $4.2 million in annual savings through smarter resource utilization. The key was not just analytics, but integration. We connected real time capacity management there. Predictive analytics and machine learning triage into one unified platform. So this is engineering intelligence in action, solving a real operational bottleneck with measurable outcomes. Financial intelligence is another area where AI is proving game changing. Revenue cycle operations traditionally suffer from inefficiencies, delays, and preventable denials. But with PI and ai, we are changing that NLP extracts Billing critical data, buried in unstructured clinical notes. NLPs popular for doing this kind of task. FLAG claims that are likely to be denied before they're even submitted. Automated coding ensures compliance and maximizes the reimbursement. And BA dashboards track revenue leakage in real time, something that used to take months to uncover. So the impact, a 67% drop in denied claims, a 23% boost in real claim claims. And a 12% increase in overall revenue. This is financial engineering applied to healthcare, turning hidden insufficiency into a tangible value. Now, one of the biggest engineering challenge is unstructured data in terms of clinical notes, discharge summaries, referral letters. Over 80% of valuable clinical insight is hidden in these documents. Natural language processing or NLP allows us to extract this information with over 92% accuracy techniques like named entity recognition, sentimental analysis, and medical calls of detection term unstructured text into structured FHIR compliant resources. These structured insights can then power population, health analytics, quality reporting, or even AI driven clinical decision supports. And today's NLP pipelines reach 94% occupant accuracy in medical terminology extraction. That means we are unlocking knowledge that was once inaccessible and making it available for real time. Intele now. Healthcare transformation is not just about operations, it's about the patient experience. BI and AI enable truly personalized engagement. And as I have previously mentioned, AI came into a great companion for bi now, resulting in great success, greater success. So predictive care pathways, identify when and how to intervene for each patient. Optimizing outcomes and avoiding unnecessary visits. And NLP powered outreach increases response rates by 78% compared to generic messages. Ensuring patients are more engaged and proactive in their care. And AI driven virtual health assistance provide 24 by seven triage and support. Reducing care while extending the reach of clinicians nowadays, everybody knows is virtual health assistance. So picking up and very widely popular. And together these tools enable personalized medicine at scale, giving patients the right care at the right time and in the right way. And building these platforms is not easy. Success depends on matching solutions to organizational readiness, and it is not just having a great or high-end technologies. Implementing these technologies together is a real challenge. And coming to the readiness of the organizations, a low readiness environment should start small, like with small pilot programs and simpler tools while focusing on building data quality and cultural readiness and high readiness organizations with infrastructure and expertise in place can go further deploying advanced analytics. Machine learning and enterprise wide intelligence platforms across all settings. The success factors are consistent, cross-functional collaboration between technical and clinical teams, incremental rollouts and clear ROI metrics defined upfront. So the pitfalls are also clear, focusing on technology instead of the problem. Underestimating the importance of data quality and failing to integrate tools into clinical workflows. One shouldn't ignore these things when building systems engineering successes, less about the tools themselves and more about how we align the them with the people, processes, and outcomes. Looking ahead several emerging paradigms will shape the next decade of healthcare intelligence, federated learning. Where AI models are trained across multiple institutions without sharing raw data, protecting privacy, while enabling collaborations. SOA models can be trained in a different institution. Different similar institution like yours, but just without without exposing the actual data, ambient intelligence, sensor rich environments, the document encounters automatically reducing the administrative burden on clinicians and multimodal AI combining imaging, text sensor data, and genomics into unified patient insight. Creating a 360 degree view of health. And finally, AI regulation compliance, ensuring that every system we build is transparent, explainable, and auditable. These paradigms are not futuristic. They're already taking shape and platform engineers will be the ones to make the operational at school here. We are at the end of our presentation in the last slide. Let's wrap up. Okay. To wrap up A-F-H-A-R based integration and standardized APIs are no longer optional. They are the foundation of modern healthcare platforms. The true measure of success is measurable outcomes, clinical improvements, operational efficiencies, and financial games that prove ROI. And most importantly, sustainable change comes from cross-functional collaboration, tech technical experts, working hand in hand clinicians to deliver practical, usable solutions. The future of healthcare engineering is not just about building more advanced technology, it's just about the building intelligent systems that amplify human capabilities and transform patient outcomes at scale. That's it. Thank you. Thank you all for my presentation. Thank you.