Group 7 Hospital Readmission Predictive Analytics Janet Scott Jeffrey Kaden

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.

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

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?

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

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

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

Findings (Logistic Regression) Scatterplot Matrix Bivariate Analysis

Findings (Logistic Regression) continued Nominal Logistic Fit Confusion Matrix

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 70-80 with an extended hospital stay.

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.

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