Predictive Analytics for Healthcare: Real Examples of Predictive Insights for Healthcare Applications
Chief Product Owner
Sriram Parthasarathy is Chief Product Owner of the Predictive Analytics platform at Logi Analytics. He works with customers to embed Predictive Insights directly in to the applications business users use on a daily basis. Sriram has over 20 years of experience in designing enterprise and OEM Analytical products. Prior to Logi Analytics, Sriram was with MicroStrategy for 15 years, where, as an early employee, he was integral to building & launching several product / modules. As a practicing Data Scientist, Sriram is passionate about making it easy for business users to predict what is going to happen and take preventive actions. In his free time, Sriram coaches kids for competitive Math and Science competitions.
Sponsored by: Logi Analytics
Delivering compelling applications with analytics at their core has never been more crucial—or more complex. For over 17 years, Logi has helped companies embed sophisticated dashboards and reports in their applications. Logi is the only developer grade analytics platform on the market, and is rated the #1 embedded analytics platform by Dresner Advisory Services.
November 13th, 2018 1:00PM ET
Healthcare applications are using predictive analytics and machine learning to deliver more value to their end users. With predictive analytics, applications can address hospital readmission rates, prioritize high-risk patients for screening, predict health outcomes for patients, foresee billing issues, and detect fraudulent claims.
Join the webinar to see real-world examples of predictive analytics in healthcare applications. We’ll also give advice on overcoming the top challenges application teams face when they’re embedding predictive analytics.
Common uses of predictive analytics for healthcare applications—including hospital readmissions, patient screenings, and billing issues
How to handle the most common healthcare data sources
How to overcome the top 4 challenges of embedding predictive analytics in applications