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:
- Identify both clinical and non-clinical risk indicators that increase probability of a readmission.
- Articulate how big data and technology contribute to identifying patients at risk for readmission.
- 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:
Non-Members: $40 (for Live Event only)