The Patient Census Dashboard is one of the most popular BI solutions for performance improvement at Allina Health. This associative in-memory data discovery BI application enables Care Managers across 11 hospitals at Allina Health to quickly identify those patients who have the highest predicted likelihood of readmitting, while they are still in the hospital.
This presentation will address rollout, acceptance and some early indications of impact. Included in the presentation will be a description of the general approach to BI at Allina Health and a walkthrough of each layer of the technical architecture of the Patient Census Dashboard specifically, including a high-level discussion of the predictive readmission risk model that Allina developed. As the first larger-scale foray into "near real time" data warehousing at Allina Health, you will hear the impact and understand EDW operational considerations. Generous time will be given to Q and A.
Following this presentation, attendees will be able to:
- Understand a general approach to enterprise Business Intelligence (BI) at Allina Health
- Conceptualize the technical architecture of the Patient Census Dashboard
- Be able to discuss the predictive readmission risk model that Allina developed
- Understand some of the operational considerations of "near real time" data warehousing
Michael Doyle, Director of Healthcare Intelligence, Allina Health
Jason Haupt, Ph.D., Senior Statistician, Allina Health
Mark W. Nelson, MHA, Director of Clinical Analysis and Care Loyalty