Session ID: 
317

AI-Powered Early Prediction of Post-Acute Care Need

10:45am - 11:45am Friday, March 13
Orlando - Orange County Convention Center
W204A

Description

Early identification of patients who require post-acute care upon discharge was achieved through the creation of a predictive model using intelligent analytics. Through the early identification, transition care team productivity is increased, medically unnecessary length of stay and likelihood of readmission is reduced. Through the delivery of real-time information with clinical insights brought to the attention of the case manager, the care team is empowered with the ability to prioritize their work flow and allocate their time and resources most appropriately to provide the highest standards of care to patient transitioning to post acute care.

Learning Objectives

  • Identify and evaluate key components necessary for successful integration of automated predictive models into care transition teams workflows to improve post acute care planning
  • Describe and analyze the requirements, limitations and process of designing and developing an automated predictive system using electronic health records
  • Discuss the challenges faced when developing a model for a low prevalence event

Speaker(s)

Medical Director,
Parkland Center For Clinical Innovation (PCCI)

Continuing Education Credits

CAHIMS
1.00
CME
1.00
CNE
1.00
CPHIMS
1.00

Audience

Allied Health Professional
CEO, COO
CNIO/CNO

Level

Intermediate