Hi everyone. Good morning. This is Achi ma. I'm here to talk about AI and bi, the Future of Intelligent Healthcare Analytics. Today we are discussing about how in healthcare organizations are evolving their data analytics by blending conventional business intelligence with sophisticated AI capabilities. We are going from the typical structured reports and dashboards to platform. That uses a machine learning, natural language processing and computer vision. These conversions in facilitating tremendous achievements in fraud detection, predictive modeling for chronic diseases management, and automated KPI monitoring. Let's see how these technologies combine to build an intelligent healthcare analytics system. Laying the ground for more efficient and responsive healthcare environment. The shift from. Conventional BI to analytics with ai. Traditional BI has been our go-to for building structured reports for years, but it relies on preexisting templates and queries that limits its ability to detect hidden patterns. With healthcare information now doubting every 73 days and 80% of it is an unstructured data, traditional methods can no longer handle this AI driven analytics. Turns this on its head by leveraging machine learning to detect patterns, natural language processing, to extract meaning from vast quantities of clinical documents and computer visions, vision to read images. This is clear. Move away from static descriptive reporting to a dynamic system that provides predictive insights, enabling us to react quickly to the constantly evolving data landscape. Healthcare data growth versus analytics capabilities. Look at the rate of change in our data environment. Healthcare data is doubling every 73 days, and a staggering 80% of it is an unstructured data with 1.2 billion clinical documents generated each year. The traditional BI systems were built on a yearly doubling rate of 365 days. And that aren't completely built to handle this kind of growth. Furthermore, AI enhanced analytics have been shown to have improved performance. For example, recording over 90% of sensitivity and specificity in diabetic retinopathy detection compared to 70% with traditional methods, and by 2025, healthcare data will reach 25,000 petabytes. A far cry for the 5,000 petabytes that these legacy systems were built for. The reality says everything about why a new AI driven approach isn't just preferable, but is required fraud detection or claims processing. in healthcare, fraud is significant problem costing the industry billions of dollars every year. Three to 10% of total expenditures. Rule-based fraud detection systems are frequently outsmarted by complex fraud schemes, reducing false positive rates as high as 60% system augmented with artificial intelligence and business intelligence features, examining billing patterns and apply emission learning techniques to identify statical anomalies. Such system can achieve 70% to 90% accuracy rates for fraud detection while simultaneously lowering false positives by 30 to 50%. This flexible approach enables new fraud patterns to be discounted without requiring continued continual reprogramming, thereby allowing more efficient allocation of investigate resources and protection of financial systems. Predictive modeling for managing chronic diseases in chronic disease management. Early identification and intervention are paramount. Business intelligence platforms augmented with AI utilized sophisticated algorithms such as deep learning and conventional neural networks to conduct risk satisfaction. With predict two sensitivities above 80%. Not only do these models forecast treatment outcomes for individual patients and potential side effects, but they also forecast hospital readmissions rates with accuracy ranging from 75 to 85% when such platform include, so social determinants of health data. These systems acquire a deeper understanding of. Patient risk and predictive accuracy is increased by 10 to 20%. This individualized approach facilitates earlier intervention and more personalized patient care. Automated KPI monitoring ability, manual tracking of KPIs on dozens of metrics can be overwhelming. A augmented business intelligence automates this task by detecting anomalies in real time and adjusting sensitivity thresholds based on historical context and contextual factors, rather than innu, editing teams with indistinguishable alerts the system intellect intelligently prioritize notifications, reducing low rate. Alerts by 40 to 60%, thus minimizing alert feting. In addition, natural language generation converts complex numerical information into brief contextual relevant narrative explanations, thus reducing interpretations time by 20 to 30%. This streamlined method allows. Teams to focus on most critical problems, AI impact on healthcare analytics performance. Here we contrast legacy systems with those infused with ai. The performance graph indicates definitely advances in a range of areas. AI not only enhances fraud detection and decrease false positives, but also enhance disease forecasting and readmission prediction. Minimizing unwanted alerts further enhances operational effectiveness. These performance gains illustrate that combining AI is greater than a tech upgrade. It's revolutionary solution that yields tangible benefits in patient care and re resource utilization, implementation challenges, data quality and integration. While AI powered analytics offer significant benefits, they deployment is not without their challenges. For instance, even established electronic health record system can have up to 65% of patient records with missing critical data. In addition, many healthcare organizations are use responsible for an average of 16 clinical systems and 18 administrative systems, which often do not communicate with each other, thus making data integration more and more challenging. This is also an urgent need for standardization of medical terminologies. Considering that coding variations for the same clinical concepts can be as high as 30 to 40%. Addressing these challenges is important to ensure the reliability of our data to allow AI systems to operate at their best level. Trust building in healthcare, AI trust is key to AI adoption in healthcare. Over 70% of healthcare professionals have expressed concern regarding AI algorithm transparency. To build conference explainable AI methods are used to show how conclusions are reached, rigorous validations frameworks. Testing, sensitivity, specificity, and calibration thus helps ensure consistent performance. Furthermore, thus, project incorporating continuous clinical input have adoption rates around three times greater than those without. More than 80% of respondents point to the need of understanding the limitations of these algorithms. Emphasizing the importance of transparency in AI implementation, healthcare, ai, emerging trends. Artificial intelligence advance in healthcare sectors today are facilitating additional advances. For instance, ambient intelligence delivers real-time analytic support during patient encounters, while autonomous analytic agents. Can proactively surface areas for improvement. Moreover, multimodal AI models which combine various types of data have shown a performance gains of eight to 15% or modules utilizing a single modality. Furthermore, federated learning facilities privacy, preserving analytics by enabling algorithms to train on decentralized data without requiring the. Centralization of sensitive data. These advances represent the next generation of innovation and establish a foundation for a more sophisticated healthcare analytics ecosystem. The future of intelligent healthcare analytics. Looking ahead, the convergence of artificial intelligence and classic business intelligence will transform healthcare analytics. In earnest, the multidisciplinary strategy will improve patient experience via personalized care and reduce wait times, advance population health through smarter predictive modeling, reduce operational expenses through heightened efficiencies and fraud detection, and improve clinical satisfaction by lessening documentation burdens. A doesn't supplement our systems, but arguments them delivering more insightful insights and perpetrating the ultimate objective of realization, the quadruple aim in healthcare. It is an existing data driven future with significant potential for all stakeholders. In summary, our presentation today has revealed. How the combination of AI and conventional business intelligence revolutionizes healthcare analytics, whether enhancing fraud detection or predictive modeling, or automated KPI ranking and undercover emerging trends. These innovations are ushering in an era of smarter and more efficient healthcare. This development underpins a data-driven echo system. We greater insights translate to enhanced patient care and operational excellence. Thank you for your time and attention. This is.