Hello everyone. This is Vishal. So I'm here to discuss about the predictive data analysis solutions. I have around two decade of experience in it. I'm working with Optum and basically in healthcare domain. So in my career mainly I focused on leveraging technology to improve operational efficiency and data driven decision making In the healthcare organization, I am exposed in designing and implementing, then optimizing data warehouse, tailored for healthcare, and have extensive experience with ET process. Data modeling and regularity co. That enabling healthcare provider to unlock actionable insight from complex dataset. My deep domain knowledge combined with the strong technical expertise in data architecture, ETL and reporting enable me to bridge the gap between the business needs and technology solution effectively. Where it is enhancing solutions, enabling predictive analysis, or supporting population health management. I'm passionate about transforming raw data into strategic asset that drive better outcomes. So today we are going to discuss about the building scalable t predictive analytics, data warehouse and healthcare. So this is how basically to improve the care quality while optimizing resource utilization. So when we talk about this topic, let's first discuss that what exactly it is important and how this is going to work in this scenario when we talk about data analysis. This article is basically explored the design and implementation of predictive analysis in data warehouse and modern hospital management system. This also alsos the transformation of healthcare operations. Through sophisticated data management architecture, focusing on the integration of real time analytics, machine learning, and advanced storage solutions. It also address the critical challenges in managing the structured and unstructured medical data, implementing security framework, and ensuring scalability while maintaining performance standard for the critical healthcare operations in today's world. When we are rapidly evolving healthcare landscape, hospitals are experiencing an digital transformation driven by the imperative to optimize operations. Enhanced percent care, quality and control is collecting cost. If you talk about recent studies, as you saw that here we are showing that 30% of global data, the whole healthcare will represent 30% of all global data by 2025. That efficiency gain 37.4% and the care quality is 42.8% enhancement in patient outcomes using analyticals platform. So the recent studies indicate the healthcare organization implementing data-driven decision making framework have witnessed a significant 37% improvement in ultrasound efficiency. The integration of big data analytics and artificial intelligence in this healthcare delivery model, with organizations reporting an average reduction of around administration overhead and a 28.6% increase in resource utilization efficiency when implementing a comprehensive data warehouse solutions, this whole healthcare sector, the digital transformation has categorized. A fundamental shift in how medical institution approach data management and analysis. The research indicate that our healthcare organization leveraging advanced analytics, have achieved a remarkable around 45.3% improvement in predictive accuracy for percent admission pattern, and around 33% of enhancement in basically resource allocation efficiency, the implementation operative analysis in healthcare. It basically demonstrate substantial impact on percent care outcomes and operational efficiency. According to the recent clinical studies, hospitals are utilizing advanced analytical platform and they have reported a 41.6% reduction in preventable readmissions, and around 38% improvement in early intervention success rate. The evaluation of healthcare data warehousing has been marked by significant technological advancement and methodological innovations. So when we talk about that, when the healthcare institution implementing modern data warehouse system that experience around 47% improvement data processing, and 44% enhancement in real time analytics, the business impact, we can see the cost reduction resource utilization. Manufacturer saving and decision making reduction they all are effectively impact the whole system of the healthcare industry. If we just implement a better and modern way of data warehousing system. The evolution of the healthcare data warehousing thing is basically marked. When we release that in the healthcare institution implementing the modern warehouse architecture, they can basically experience around 47% improvement in data processing efficiency, and 44% enhancement in real time analytics capability. As I told you, by 2025, we have around 30% of the global data is all about healthcare. The adoption of cloud-based solutions has further accelerated this transformation, and that enable around 35% reduction in infrastructure cost and 40% improvement in system scalability. These advancement have been particularly impactful in large hospital network, where integrated data platform have facilitated around 40% improvement in cross facility coordination, and around 38% enhancement in resource sharing efficiency. So implementing a predictive analysis in healthcare will demonstrate an impact on present care outcomes and also the operational efficiency. This is also shown by all the other studies when we talk about the architecture analysis. There are multi-layer architecture visualization here, analytics, air processing layer, and data storage layer. So when we talk about this analysis, the foundation of effective healthcare D House architecture is evolved dramatically within the exponential growth in healthcare data complexity and volume. In the recent studies indicate that healthcare Orions are experiencing an unprecedented search in data generation with estimate suggesting that the healthcare generates approximate 30% of the world data volume, as I discussed, projected to read. Around 2000 or more than that, exabytes by 2025. The emergence of multi-layer architectural approach has ized healthcare data management capability. The research demonstrate that healthcare institutions implementing modern data warehouse architecture have achieved significant improvement in data processing efficiency with organizations reporting around 67% reduction in data retrieval latency, and around 80% improvement in data integration accuracy. The data injection layer represents a critical component in modern healthcare data architecture. Processing and increasing complex area of data types and sources. When we talk about unstructured data volume are expanding is even more rapid race. You can see the data is coming in different formats. Unstructured data more than rapid race. You can see 60 to 65% per year. This layer must accommodate both real-time streaming data. And percent monitoring system and batch processing of administration record with leading health constitution reportedly daily injection volumes exceeding 10 terabytes hospitals information system. You can see HIS have evolved to become increasingly sophisticated with modern platform managing an average of 8,000 distinct data points per percent encounter. The research source that integrated HIS platform in healthcare setting process across, you can say 1.2 million, operation operational connection daily basis with peak load during admission and discharge period, reaching out about 3000 ions per minute. If you talk about the more how the healthcare data growth and processing metrics, right? The annual metadata growth rate is around 54%. We have to talk about the real time processing because we are talking patient monitoring, resource allocations, emergency responses, and operational dashboard. As I said, there are 44.3% improvement in real time analytics capabilities by just implementing a proper and enhanced version, a proper and enhanced version of healthcare systems. So when we talk about the systems we can see our storage layer architecture. Modern healthcare data warehouse storage architecture have evolved significantly to address the exponential growth in medical data complexity. The structural data storage component, leverage modeling optimizes specifically for the healthcare workload. And you can see we are implementing star schema normally in healthcare facilities at implementing a structured data that more than 180,000 daily percent transaction while maintaining sub-second queries response. For 88% of anus queries performance analysis from leading healthcare institution source that properly implemented dimension models can maintain consistent query performance, even when processing a log queue data spanning three to five years. Organizations utilizing modern storage optimization techniques have reported compressions ratio address of 6.1 for clinical data resulting in substantial storage efficiency improvement while maintaining critical performance record. The unstructured data management capabilities have become increasingly sophisticated as healthcare organization deal with growing volumes of narrative. Clinical content. Current implementation shows that unstructured data account for approximate 67% of total healthcare data volume. With a typical hospitals, you can say modern document stores have around demonstrate significant improvement in test processing capabilities and achieve the success rate. Healthcare organizations utilizing flexible schema design using JSON format have reported a 39% improvement because data is coming unstructured format a lot, and it'll basically reduce the time of data processing and manage around 75 to a hundred million documents while daily injection rate of around 15,000. New document. In a medium, medium to large healthcare facility, you can see. The integration between the structured and unru data component through reference pointers has emerged as a crucial architecture feature in our analytics layer architecture. This analytics layer architecture, if you talk about our analytics layer, if you talk about this layer where the machine learning models and predictive algorithms come into the picture in this model. The modern healthcare warehouse has undergone significant evolution to meet the demanding requirements of healthcare operation. The studies demonstrate that healthcare, cognition, implementing and hybrid analytics architecture that achieves a use improvement in support efficiency and reduction in diagnostic latan. The stream processing capabilities have evolved to address the complexity of continuous percent monitoring system. With current implementation processing around, you can see 18,000 events per second from connected medical devices and clinical systems. Our study shows that healthcare organization utilizing advanced stream processing framework that can identify the critical clinical event around 90% of accuracy within two 50 milliseconds. This use the implementation of realtime operational dashboards has re. Has reorganized the healthcare decision making process. Healthcare facilities report a 40% improvement in resource allocation efficiently, and a 38% reduction in response time. The batch processing infrastructure that handle the complex analytics workload, that crucial for the healthcare quality, our the ETL workflow in the current healthcare system, manage an average of 65 million records daily. With peak processing volume, reaching around 120 million records during high demand periods. The organization we are implementing in optimized ETL pipelines that achieve transformation rates of approximate 1.8 million records per minute. Machine learning models that train the pipelines have demonstrated significant advancement in healthcare analytics, the machine learning pipeline process, and average of eight terabyte of historical percent data daily. The capabilities for historical trend analysis with modern system efficiently processing three to five years of historical data compromising approximately seven 50000000% encounters. When we talk about the performance optimization and bi, the strategy Healthcare data warehouse requires is. Sophisticated optimization strategy to maintain performance at a scale, the query performance improve and the cloud storage cost will definitely improve, but it helped to process the record faster. Modern healthcare facility typically manage between three to five, five petabytes of archive active data with daily query volume. Reaching around 350,000 analyticals requests distributed across various clinical and administrative department. We are implementing the parting strategies that evolve significantly with the introduction of machine learning driven partition scheme selection. Partitioning framework can reduce query execution time by 60% for times risk analysis queries, which represent approximate 58% of analytical workload in healthcare. Healthcare organization implementing is smart. Partitioning typically between 12 to 50 active partition point table. But by using the machine learning integration, the diagnostic support, treatment planning and readmission prevention, we can see more than 40% improvement in diagnostic accuracy through the machine learning models. Treatment planning is also improved around 36.7% reduction in treatment planning time. Machine learning help us to improve the whole the whole time. How can we make our time and utilize the time more efficiently? The response time under 150 milliseconds for 88% of frequently executed queries when you're using column storage optimization. That benefited from Advanced com algorithm and integrated data layout strategy integration with BI tool has evolved through the adoption of, inte, inte intelligent query routing and casing mechanism. The data storage optimization, we go with three different format, hot data, warm data tier, and the core data here. Hot data means what? Basically the high performance in memory processing for missing critical present information requiring instant access. For example, real time monitoring system, active treatment protocol, or fast retrieval of emergency clinical decisions. Warm data. Basically the balance, performance cost. SSD stories, high speed. S-S-D-R-A comprehensive 30 to 90 days clinical histories. Upcoming appointments. See during and follow up. You can see as scholar a warm data, which frequently accessed cold data is basically cloud-based archival solution, secure cloud archiving. With compliance certified encryptions, we can regularly compliance records with tamper proof audit traces. Data will be saved successfully and it'll be archive. We are using ING approach, machine learning approach for that. Data access layer performance optimization has been re ized by predictive query optimization and intelligence casting strategy. Healthcare recognition Report that machine learning enhanced view ization strategy reduce an average report generation time by 58% while supporting an average of 2,800 concurrent query sessions continuously. Modern healthcare data warehouse must address complex scalability challenge while ensuring consistent performance and data accessibility. Our studies indicate that healthcare organization implementing cloud-based scalability and framework achieve around one 34% high data processing throughput, and 71% improved query performance during high demand period. In the healthcare domain where everything is data. We have to access the data. So query optimization must to be improved. We are implementing the query optimization, indexing strategies, data partitioning and compression techniques, indexing strategy that custom healthcare specific indexing patterns like which by which pattern? The data will be searched a lot. We can use that pattern to implement indexes to make query performance faster. Load balancing mechanism have evolved to become. To become increasing sophisticated in healthcare environment, the development methodology significantly influence implementation outcomes. In healthcare settings, organizations start adopting I implementation strategy and they can report around 65% of higher project success rate. By time to time, the implementation of cap capacity planning process have become increasingly critical in health environment. Because data will be stored and need to access from their ions. Performance optimization process is evolved a lot to incorporate healthcare specific requirements. Organization report that it'll reduce their average execution time and improve the resource utilization and improve the performance when you make it. There is many challenges. Because data is coming from the structured data, unstructured data use volume of the data, a lot of data, how to integrate with the EHR system, with a legacy system, with a security complex, comprehensive, unstructured data. This all will be taken care if you balance our system right, and create our system in such a way so that everything will be managed and enhanced. The query performance we have to implement our roadmap. Assessment and planning, architecture, design, pilot implementation, and we have to full development and training. We have to understand how exactly the architecture matters because a multi-layer design delivered around 48%. You can see improvement in processing efficiency. We have to focus on the primary goal. Performance will be faster. Present outcome as a primary goal. The stakeholder involvement is very necessary. Build comprehensive into a foundation, not as per afterthought. So just one thing here, I want to say in the last that the implementation of predictive analysis data warehouse has fundamentally transformed modern hospital management by enabling data driven decision making across all operational aspect healthcare organization. Adopting these sophisticated architecture frameworks have demonstrated significant improvement in patient care quality. Operational efficiency and resource utilization. The success of these implementation relies on carefully balanced architecture decisions that address the unique challenges of healthcare data management, including the integration of diverse data sources, realtime processing requirement and significant security standard, and the evolution of machine learning, enhanced optimization strategy. Coupled with advanced storage solution and analytics capabilities has created a robust platform capable of supporting both current operational needs and future scalable requirement. As healthcare continues to generate increasingly complex and a lot of volume data, the importance of well-designed predictive analysis data warehouse becomes even more critical for maintaining high performance healthcare delivery system while ensuring patient data security and mobility competencies. So it is very required how to make our system in a much better way. Thank you so much for joining this session. Thank you.