Hello there. This is Santosh Kumar Rai. Welcome to Conf42 Cloud Native 2025. Today's topic is revolutionizing health care with data engineering. The integration of data engineering is transforming personalized medicine. Data engineers ensure the collection, integration, and analysis of vast data sets. Glencians can make real time, evidence based decisions using data driven approaches. So there are four main components of the data engineering roles, collection, integration, processing, and analysis. The collection enables the automated gathering of structured and unstructured health care data from EHRs. And the integration, seamless imaging of disparate healthcare system and data format while maintaining data integrity, quality and HIPAA compliance standards. Processing, real time data transformation and transcending using advanced ETL pipelines to prepare healthcare data for immediate clinical use and analysis. And the fourth one is analysis. This is the implementation of machine learning and statical models to extract actionable insights. enabling data driven clinical decisions and personalized treatment plans. And what are all the advanced data engineering techniques that have been used for the better performance? Number one is a real time ETL pipelines. Number two, scalable cloud architecture. Number three, AI driven analytics. The real time ETL pipeline. This enables continuous process of patient data streams for immediate clinical insights. This will facilitate rapid response in critical care scenarios. Scalable cloud architecture. This deploy flexible infrastructure that dynamically adopts to process massive healthcare data sets while maintaining HIPAA compliance high availability. AI driven analytics leverages Machine learning algorithms to analyze complex medical data, identify early disease indicators, and generate personalized treatment recommendations. Structuring and analyzing diverse data. Genomic sequencing, electronic health records, medical imaging, IoT based monitoring. So how genomic sequencing is. Analyzing complete DNA profiles, generating over 200 gigabytes of actionable genetics insights per patient. Electronic health records. These longitudinal patients history, including treatments, medication, and outcome for informed decision making. How medical imaging is helping in terms of using the new technology, which delivers high resolution diagnosis, visualization through x rays. MRIs and CT scans for precise analysis. IoT based monitoring. This streams continuous vital signs. and health metrics from wearable and medical devices for proactive care. What are all the impacts on the precision diagnosis? So AI diagnostic systems, this will reduce the medical errors by 30 percent and the machine learning models enhance patient's risk assessment by 50 percent enabling early intervention and preventive care strategies. Integration of AI driven diagnostics results in 40 percent faster treatment. Initiations and 25% better patient outcomes. We'll look at the case studies for early disease detection. So initially we collect the data in terms of integrating genomic data, clinical records under real time monitoring system to create a comprehensive patient profile across 50 plus health indicators. And we look in the pattern recognization, applying the advanced machine learning algorithms to analyze the potential data detecting. This is markers with a 94 percent accuracy through neural network analysis, predictive modeling. So leveraging AI driven predictive models to forecast disease. Progression six to 12 months in advance, enabling proactive treatment, planning and risk management, improved outcomes. We can achieve 40 percent faster diagnostics time and 35 percent reduction in late stage disease progression through early detection and intervention stages. Another case study is about AI assisting drug discovery. Here we will process over 100 million molecular compounds. And the chemical interactions daily reducing the candidate identification done by 60 percent through advanced AI algorithms. And the target identification is to utilize machine learning to analyze protein drug interaction across 100, 000 potential targets, achieving 85 percent accuracy in predicting therapeutic effectiveness. Clinical trials, reducing traditional drug development timeline from 10 years to 4 years through AI optimized trial design and patient matching result in 40 percent cost reduction. This is another case study with a real time patient monitoring. So we monitor the data streaming, processing via 1000 plus vital signs per second from wearable and bedside monitors for comprehensive health track. Anomaly Detection AI algorithms analyze patterns to identify critical health changes with 95 percent accuracy within seconds. Intervention This enables rapid response teams to reduce critical event response by 60%. Leading to better patient outcomes, alerting the smart notification systems, prioritize and deliveries, urgent alert to the right health care provider instantly. And what are the challenges in terms of data security and compliance? So the first one is the trust and then the compliance and the security. So these are the three main data security and compliances we have to consider. the healthcare data security demands robust production of sensitive patient information through state of the art encryption and access controls. Strict adhere to HIPAA and GDPR regulations requires comprehensive documentation, regular audit, and careful management data access protocol. Building and maintaining patient trust depends on transparent data handling practices. ethical AI implementation and clear communication about how their information is used and protected. So the healthcare industry faces increasingly cyber security threats with data breaching costing an average of 9. 2 million per incident. Organization must balance the need for data accessibility with engine security measurement measures. While ensuring compliance with evolving international regulations and what are the challenges while we harmonize or integrate or standardize the data. Healthcare system currently uses over 40 different data standards, making standardization critical for ensuring consistent data, quality, and reliable analysis. Integration across disparate systems like EHRs, lab systems, and imaging databases remain a significant challenge with healthcare organizations. spending an average of 500, 000 annually on integration effort. Harmonizing data format is essential for enabling seamless data exchange with studies showing the standard standardized data format can reduce analysis time by 65 percent and improve diagnostic accuracy. 40%. So here we see a data diagram of expected improvement with personalized medicine in terms of reduced adverse reactions and also improved treatment. And what are the actionable insights for precision healthcare? We talk about build robust infrastructure, leverage advanced techniques. address key challenges. These are the three actionable insights for the precision health care. So when we talk about the built robust infrastructure, deeper and scalable data architecture that ensure HIPAA compliance, data and storage system and automated backup protocols to handle increased volume of patient data, leverage advanced techniques. So implement the real time ETL pipelines and AI powered analytics. to process patient data streams, enabling rapid insight generation and predict modeling for improved clinical outcomes. Address key challenges. Proactively tackle security vulnerabilities through end to end encryption while establishing standardized data protocols to enhance system interoperability across healthcare networks. Thank you very much for your time.