1. Group 7 Hospital Readmission Predictive Analytics
Janet Scott
Jeffrey Kaden

2. Executive Summary Goal Key findings Recommendations
To understand the factors that predict whether or not a hyperglycemic patient is likely to be readmitted to the hospital within 30 days of an “encounter”.
Key findings
“number_inpatient” is a significant predictor
Other significant variables are “metformin[Down]”, “number_outpatient”, “glipizide[No]”, and “A1Cresult[None]”.
Males that are 50 years of age or older have a higher rate of readmission than other segments of the population, with the highest percentage of readmissions being Caucasian males between the ages of 70 and 80.
Recommendations
Create strategies and treatment plans to address this disease before it progresses, thereby improving the system.

3. Business Problem Main Objective Regulatory Requirements
Identify which diabetic patients will be readmitted within 30 days of discharge.
Regulatory Requirements
The Affordable Care Act established the Hospital Readmissions Reduction Program (HRRP)
Requires the Centers for Medicare & Medicaid Services (CMS) to reduce payments to hospitals with excess readmission rates.
Hospital Benefit
Enhanced patient outcomes
Improve treatment
Reduce 30 day readmissions
Ensure payment from CMS

4. Basic Questions Being Investigated
What the common characteristics are that predict which patients are likely to be readmitted to the hospital within 30 days?
Age
Sex
Weight
Race
If the length of a patient’s stay in the hospital is a predictor of a readmission?
If the number of diagnoses for a patient entered into the Electronic Medical Record (EMR) is an indicator for readmission?
What are the top 5 factors that predict a diabetic readmission within 30 days?

5. Existing & Traditional Ways of Addressing the Issue
Currently, very little is being done in real time
Hospitals are mainly using
Patient education
Prescription drugs
Diet plan
Exercise plan
Monitor blood sugar
Historical data
Sending patients to hospice care before going home
Sending patients home with a support line in the event of an issue
*Problem*
Patients don’t always follow doctors advice

6. Data Sources
Obtained by the Center for Clinical and Translational Research, Virginia Commonwealth University.
Health Facts database
De-identified the data as required by HIPPA
Ranges from the year 1999 to 2008
130 hospitals
55 attributes

7. Data Preparation Selected specific data variables Cleansed Data
From 55 variables down to 23 variable
Cleansed Data
Ensured data classifications were the same
Divided data into testing and validation
Testing 2/3
Validation 1/3

8. Findings (Logistic Regression)
Scatterplot Matrix
Bivariate Analysis

9. Findings (Logistic Regression) continued
Nominal Logistic Fit
Confusion Matrix

10. Findings (Clustering)
Clustering Analysis
Dengrogram
Scatterplot Matrix
K-means
Highest Risk Patients
Males, 50 years and older with an extended hospital stay
Highest at risk group
Caucasian Males between the ages of with an extended hospital stay.

11. Conclusion Answered all of the basic questions being investigated
Who will likely be readmitted within 30 days?
Male, Caucasian, within the age range of 70 to 80
What are the key factors that increase readmission rates?
Number of inpatient visits
Patients Metformin level being down
Number of outpatient visits
If medication “Glipizide” was administered
If patients A1C test was not performed.

12. Conclusion Cont. How hospitals would use data findings
Use key factors or expand to additional factors to broaden scope of at risk patients
Flags in the Electronic Medical Record (EMR)
New evaluation of at risk patients
Better discharge education on at risk patients
Update existing & traditional ways of addressing the issue
Share information and model with other hospitals