Using Unified Analytics and Machine Learning to Solve Complex Clinical Challenges
Healthcare organizations have more data at their fingertips than ever before, but struggle to effectively leverage that information to prioritize, predict, and prevent costly clinical challenges. A specific example can be found with Sepsis, where rates and mortality have not decreased in the past decade, and Sepsis-related hospitalizations cost 70% more than the average stay.
There is, however, good news - with the right data, predictive models, and prioritized lists of patients, interventions can be delivered at the right time to maximize successful outcomes and reduce costs of these complex clinical challenges. However, many providers struggle with siloed EHR data, infrastructure that doesn’t scale and static analytics tools that limit their ability to accurately predict and prevent these illnesses. Join this webinar to learn how to easily overcome these challenges and build scalable predictive models to identify high-risk patients.
During this webinar we’ll cover:
- The value of a unified analytics platform for ETL and data science at scale
- How to improve large-scale data processing by 10-15x with infrastructure on-demand
- Machine learning models for infection prediction and prevention
- Live demo of how a Sepsis prediction engine built on Databricks can help prioritize efforts