Abstract: 
Market share competition as well as increased transparency of healthcare quality information has led healthcare organizations to narrow their focus on improving outcomes related to specific quality indicators. The rate at which patients readmit to acute care hospitals as an inpatient is one of those metrics that warrants such focus. Case managers know that the days immediately following hospital discharge can be a very pivotal time and patients can be particularly vulnerable. Case managers do their best to coordinate appropriate post-discharge care, but how do they determine which patient may be at risk for a readmission? Preventing readmissions is an interdisciplinary process and an “all hands on deck” type of approach is necessary. There are many tools and scoring mechanisms available to help assess a patient’s readmit risk.

In this presentation, learn how one organization has taken it a step further and utilized “big data” from thousands of clinical records to develop an evidence-based algorithm to help identify patients at risk for readmission in the days following discharge. Learn about their “playbook” and what interventions the interdisciplinary team implements for the patients at risk for readmission and how they address both clinical and non-clinical causes of readmissions.

Objectives:

1. Identify both clinical and non-clinical risk indicators that increase probability of a readmission.
2. Articulate how big data and technology contribute to identifying patients at risk for readmission.
3. Discuss how avoiding readmissions is an interdisciplinary process.

Presented by:
Dina Walker, RN, MSN, ACM-RN, RN-BC
National Director of Case Management for Encompass Health

CE Credits:  1 Hr RN and CCM

COST: