Hello everyone and thank you for tuning into Con 42. I'm sat and I'm today excited to walk you through how predictive analytics and machine learning are not just enhancing but fundamentally reshaping the way we approach population health. In the next few minutes, we will look at how data, when used intelligently and ethically can drive not only better healthcare outcomes. But also more equitable and cost effective systems. We will cover the challenges we face, the methods we are using, and the impact we are already seeing in real world population. The population health challenge. Let's begin by addressing the core challenge. Across the healthcare ecosystem, around 60% of our organization struggle to identify and proactively manage high risk populations. This is not due to the lack of will. It's often due to the data silos, outdated infrastructure, and limited access to integrated tools. Product two Care enabled by the right analytics has been shown to reduce hospitalizations. By 25% and improve preventive care enhanced by 30%. We are talking about a real shift from episode care to continuous personalized support. And these insights come from diverse healthcare studies conduct across 35 states, leveraging data from the C-D-C-C-M-S and institution like Mayo Clinic and Kaiser Permanente. The 360 degree patient view. To make predictive care effective, we must start with a complete 360 degree view of each patient. We do this by integration, three key types of data, clinical records from EHRs, utilization and cost data from claims and social determinants of health, or SDOH. By combining these, we gain not just a medical snapshot, but an understanding of the context behind a patient's health, their environment, income, education, even housing stability. With advanced ETL pipelines, secure API Gateways and HL seven FHI compliance, we ensure this data is both producted and accurate. Validated over 99.7% of accuracy. The result, A foundation we can trust for making life impacting decisions, risk stratification models. Next, we apply risk stratification using supervised machine learning models. Train on over 8.5 million patient records. We evaluate over 3000 variables, including lab results, medication adherence, utilization patterns. And even socioeconomic inputs. We use algorithms like random forest and gradient boosting, which have outperformed traditional logical regressions, delivering 87 to 92% prediction accuracy in anticipating hospitalization within a six month window. This lets us bucket patients into four tires, high risk, rising risk, low risk. And healthy each time received a tailored intervention plan from intensive care management to wellness and education initiatives. This personalization at scale is what moves the needle in population health. Predict predictive analytics. The results speak volumes in studies Over 30,000 patients compare it to predictive models, to traditional core. Readmission dropped by 20%. Er, we see visits decreased by 30%. Overall health cost reduced by 20%. Perhaps more importantly, patient engagement increased by 40% with higher appointment attendance and better follow through of care plans. All of this was achieved using statistically significant method of EAB testing and. Prosperity score matching, ensuring the outcomes were in just al. Predictive analytics isn't just about risk scores, it's about outcome, impact, and equity. Geo special healthcare analytics, one of the most powerful and sometimes overlooked tools in our arsenal. Geo hospital analytics. Using GIS technology, we map disease prevalence, resource usage, and care access across gaps entire regions. With this insight, we have helped organizations identify healthcare deserts, designing mobile outreach programs, and even optimize where to place new clinics. For example, you by using catchment area modeling and ISOP prone maps. We improved facility placement efficiency by over 40%, reducing both patient travel time and operational strain. This brings precision to population health, right care, right place, right time, machine learning applications. Let's not drive deeper into how machine learning fits into this equation. We aggregate vast data sets from EHRs. Labs pharmacy records to socioeconomic indicators. Our models range from decision trace to deep neural networks, the undercover patterns that would take years to identify manually. For instance, one model flags patients at risk of readmission based on subtle combinations of medication refill patterns and prior claims data with What's key here is not just prediction, but. Inter interpretability, we ensure clinics can understand why a model made a recommendation. Fostering trust and adoption case study, chronic disease management. A example comes from a regional health system serving 150,000 people, mostly between the ages of 45 and 75. With the high diabetics burden, they implemented our predictive platform focused on hba one C trends, medication adherence, and social data. Over 18 months of diabetic admissions dropped by 42%, thus saved 3.2 million annually. Even more powerful was the feedback loop. Real-time patients outcome were fed back into the system. To continuously adjust care plans. It's not just a dashboard, it's living a learning system addressing healthcare disparities. A core value of this work is health equity using SDOH and claims data. We identify undeserved population based on a zip code, ethnicity, income, and access of access to care. We partner with LO local organizations to design culturally sensitive outreach programs like mobile screening vans and translated education materials. We also monitor equity KPIs to track the long term success of these interventions. Analytics isn't just for efficiency, it's for about fairness and inclusion implementation roadmap. Implementing predictive analytics isn't a plugin and plug and play task. It requires structured roadmap, data integration connection, EHR claims and SDOH sources, securely and consistency analytics, depart, develop, deployment, test and refine predictive models, train staff on BI dashboards, workflow integration. Embed insights into real world clinical processes without disruption, continuous improvement. Use a op plan, do study, act, look to recalibrate models and expand. Reach. Change management is key. We work closely with stakeholders to ensure clinicals are supported not over. Key takeaways. Let's recap the core insights from today. Integrated data creates a complete patient view. Predictive analytics shifts care from reactive to proactive hospital analytics, add geographic precision. Machine learning powers smarter, faster decision and equity must remain front and center. We are not just building models, we are building better healthcare future. Thank you for so much time on in joining me. It's an existing exciting time to be in healthcare technology. With the right tools and frameworks, we can transform not just systems, but lives. Let's continue building healthcare system. That are intelligent, inclusive, and truly impactful. Thank you very much. Thanks for joining me at Con 42. Have a good day.