Hi there. Welcome to my talk show at con 42 Site Label Engineering. So today's topic is data engineering in healthcare, how it is transforming personalized medicine and diagnosis. So data engineering is transforming the healthcare in big way fundamentally changing the personalized medicine and diagnostic approaches, and also implementing a clinical settings by creating robust infrastructure. Basically the data engineering enables comprehensive patient profiles that supports truly personalized treatment. So this also taking care of the foundation, addressing complex challenges, which are including the data integration across all systems, and also the scalability of exponentially growing information volumes. Data governance, quality governance, and advanced analytics requirements. As healthcare continues, its digital transformation, data engineering, evolving challenges related to privacy production, and this also taking care of edge computing for point of care diagnostics. Includes the design for diverse populations and ethical implementations of artificial inte. In clinical workflow, the found the foundation of personalized healthcare. So there are certain f the foundations which are which plays the important role, which are genomic information, that clinical history integration, and the lifecycle and environmental data. And the data engineering infrastructure. So for the genomic information the cost of this is going to be like, dropped dramatically from a hundred million 2021, 2001 to just thousand in 2023. Enabling widespread critical approach and coming to the clinic. History integration this is a confidential patient profiles incorporate medical records. Treatment is three and the diagnostic result across previously sliced systems and the lifetime and environmental data. So these are the certain variable devices and environmental sensor providers, continuous monitoring, contributing to the 48% of annual growth in healthcare data volume and coming to the final data engineering infrastructure. This is where the data engineering design complex. Pipelines to process the data which is 2, 3, 1 4 exabytes of healthcare data expected by 2030. Data collection and integration challenges. Healthcare data is in credibility, diverse from the EHRs and images to variable and genomic. Sequences. So each of these the genomic sequencer, wearable devices and patient reported outcomes, each with unique format and standards. And there is also the unstructured clinical information, which is up to 80% of the clinical relevant information, which is existing, the unstructured format, which has to be bring it to the structured format. Using some natural language processing to extract the data and the system fragmentation. A single hospital typically manages over 50 disposable clinical information systems, each with unique data models and exchange prototypes that must be reconciled to create unified patient representation, data quality and governance. So there is a certain process we follow for the data quality and the governance. Initially is to start with the lineage tracking and the master data management, standardization and validation. So these are the four steps which are involved in the data quality and the governance. And this will definitely improve efficiency of personalized medicine, reli fundamentally on pristine data quality. Advanced analytics infrastructure, so the high performance computing, stream processing, lops pipelines, interactive visualization. So these are the four standards that that improves the technology, which I have been used in the advanced analytics infrastructure. So supporting the analytical requirement of personalized medicine requires a sophisticated and computational resources. Tailored to the unique characteristics of the healthcare data. Genomic analytic workflow for clinical applications typically requires 122 240 CPU hours per patient for comprehensive, varied analysis and interruption. Data engineering technologies have demonstrated a significant quantitative impacts across various aspects of healthcare delivery. So the natural language processing system now achieve over 90% accuracy in extracting the clinical concepts for narrative text. While implementing data province frameworks reduces errors by 35%, so FHIR based data models, which improves the query performance an average of 50% compared by traditional relational models. So a well-designed clinical visualization that reduces decision time by 25%, and also that agnostic accuracy by 20%. How the genomic medicine is transforming. So first, the rapid sequencing and then the efficient storage, and then the automated interation and population integration. So these are the four topics how the genomic medicine is transforming which is available in the current situation. So the rapid sequencing, which which, the sequence now completed in halon instead of weeks. And this enables critical genetic diagnosis for critical ill newborn and immediate intervention and the efficient storage. So we have the efficient devices where we can store that data in the current systems and also the automated interpretation. There are sophisticated vari annotation pipelines, integration filters. Millions of genetic variations down to 20 to 25 clinical significant S for targeted treatment planning. So data engineering team at leading academic medicine center have a revolutionized genomic by developing efficient processing pipelines that analyze complete genomics, unpredictable diagnostic evaluation. So the early warning system predict clinical ation five to two hours before traditional sign, and then multidi multimodal integration. This process, 250 to 400 variable per patient, including continuous waveform data. Advanced analytics apply time series analytics to identify sub pattern predictor of disease progression. Continuous learning. This automated ation workflow incorporate new data to ma maintain model accuracy. So pioneering the healthcare system are harnessing sophisticated data engineering to deploy early warning systems that detect clinical hovers before every. Patient profile that captures psychological status according to multiple patterns which unprotected precision. So these are the four sub basis where we identify the disease and then we take certain actions to protect the patient. And this shows the clinical impact of our data engineer. What are the metrics and what is the traditional approach and also the data engineering approach. The diagnostic time has reduced. Of pretty much on a huge number. So before the diagnostic time used to take a lot of time, and now the time has come down by 43 and the treatment selection physician. Based on the data analytics, they can decide how this patient has to be treated. And this information will help the doctor to take 60% more precise. A faster way to treat the selection precision and the time taken to diagnostic for critical cases. So normally it takes around 16 days to take the necessary steps for diagnostic of the critical cases, but now it takes only two to six hours to take a decision. And the whole genomic analysis. This used to take around two to three days. Right now it's taking around eight hours and the variant review efficiency, so there are millions of variants before and now as of now in the market, it's 30 to 50 s. The treatment outcome prediction before that additional push was to see it as a baseline, but right now that is 30% more accurate. And in-hospital mortality is a baseline. And that has reduced by 23%. So right now what are the challenges in healthcare? Data engineering. So there are certain ethical implementations and which which also includes to designs, the edge computing, the privacy protection, and exponential data growth. So these are the five steps which we might cross across in dealing with the future challenges in the healthcare data engineering. And that will definitely help healthcare data to, to grow at approximately 36% annually, significantly outpatient storage capacity, expenses. And also the future of healthcare delivery risks firmly on the shoulders of robust data engineering by building the technical infrastructure that enables personalized medicine and advanced diagnosis. Data engineering are not merely supporting healthcare, but fundamentally transforming it. So the solutions for data integration, quality management, analytics, and ethical implementation creates cycles. Where increased information, accessibility drives improved clinical outcomes, so generating more data to further refine predictive models. As personalized medicine initiated expands beyond academic centers to community settings, the role of data engineering will become increasingly central to healthcare evaluations. Thank you very much for taking time to attend this session. Again, this session is again about the data engineering and transforming the healthcare, so you have a wonderful day. Thank you.