REARRANGING A DATA INFRASTRUCTURE TO REDUCE READMISSIONS
Gabriela Ramirez, PhD, MPH, Director, Enterprise Analytics, Clinical Analysis and Outcomes, ORLANDO HEALTH
In many hospitals and healthcare systems, the data that forms the foundation of the scoring model to determine risk factors for readmission is kept in disparate source systems, requiring users to invest significant amounts of time in gathering and analyzing information.
This session will explain how Orlando Health, a private, not-for-profit healthcare network, uses the combination of the LACE predictive model and healthcare analytics via an Enterprise Data Warehouse (EDW) to predict the likelihood of patient readmissions.
Among the topics covered are why Orlando Health decided to use the LACE protocol, the challenges faced implementing it, the efficiency of using an automated calculation versus a paper one, and the ways the quality of the data affects the validity of the predictions.