Improving Follow-Up Care in Populations at High Risk for Readmission

Attendees who participate in this session will learn:

-Methods to identify high-risk of readmission population

-Deployment strategies to further strengthen care coordination of high-risk of readmission population in an effort to reduce readmission

-Primary Care Provider workflows to ensure 7-day follow-up appointment

-How to build efficiencies related to follow-up appointment scheduling practices

-How to improve access to care for follow-up appointments

Tool: Binary logistic regression model internally developed to identify the population at the highest risk for readmission. Dashboard created to display and monitor key process and outcome performance indicators

Problem: There was no comprehensive process to identify and coordinate prioritized follow-up care for patients with complex care needs

Tool Selection: Binary Logistic Regression model allowed for the prediction of outcomes based on characteristics of diagnoses in targeted populations

Usage: Targeted patients were displayed in a worklist. Trending data on 7-day appointment scheduling, 7-day appointment attendance, 7-day readmission rate, and 30-day readmission rate was viewed in a Statistical Process Control chart format and accessed via a dashboard. The dashboard summarized the data to be displayed in a weekly compliance format

Results: Patients who did not attend a follow-up appointment within 7 days of hospital discharge are 8.2x more likely to readmit within 7 days of discharge. Realized a 57.5% increase in 7-day follow-up appointment scheduling and a 40% increase in attendance

Quality Improvement Engineer at Memorial Health
Quality Improvement Engineer – Memorial Health

Speaker Type: Poster Presentations On-Demand

System Administrator of Quality and Performance Improvement at Memorial Health
System Administrator of Quality and Performance Improvement – Memorial Health

Speaker Type: 60 Minute Session On-Demand, Poster Presentations On-Demand