Predicting Readmission Rates

Identifying Factors That Influence Predicted 30-Day Readmission Rates for Heart Disease in States with High Excess-Readmission Ratio using Center for Medicare and Medicaid Services data

By: Umesh Singh, Nate Strong, & Adrian Hall

Datasets and Methods: CMS data

CMS has been adopting measures to reduce 30-day hospital readmission for specific diseases that high healthcare burden such as heart attack, pneumonia, COPD, hip-knee surgery, and heart failure.

Our goal was to visualize importance of heart attack/chest pain as a cause of death across states and then determine factors or healthcare measures that lower the predicted readmission rates (PRR). Improving these factors can help the states with high excess-readmission ratio (ERR), defined as predicted: expected readmission rate. States with ERR greater than 1 are of interest for implementing the timeliness and effective measures to reduced the PRR. Data sources from CMS:

• Timely and Effective Care data 
• HospitalReadmission Reduction Program data

Hypothesis and Specific Aims

• Improving the timeliness and effectiveness of care in patients with heart attack or chest pain will reduce the 30-day readmission rates and deaths.
• Our aim is to determine the factors that significantly predict readmission within 30 days of discharge (30-day Readmission).
• We also aimed to identify the states with highest death rates due to heart attack and focus on states where predicted readmission rates could be reduced.