BIG DATA & HEALTHCARE ANALYTICS: A HIMSS EVENT
SAN FRANCISCO, CA - MAY 15-16, 2017
Modern big data resources detect variability in provider-level performance in near real time. Quantifying, visualizing, and reporting this variability can improve outcomes by identifying the best and worst performers and creating peer-based incentives for improvement. But the impact of simply describing existing variability is inherently limited without a deeper dive to understand its causes.
That’s where, as Stefano Bertozzi, dean of the UC Berkeley School of Public health, and his colleague Maya Patersen will explain, the emerging field of targeted learning analytics comes in.
In a hospital or other healthcare provider organization, targeted learning analytics can identify modifiable sources contributing to variability of performance and efficiency as well as suggest optimal courses of action that change over time as performance and other factors change. This enables a smarter, more data-driven healthcare system that learns, implements, and evaluates in rapid cycles, to continuously improve performance.