The Untold Story of Predicting Readmissions for Heart Failure Patients

Abstract

The availability and accessibility of Electronic Health Record (EHR) data create an opportunity for researchers to revolutionize healthcare. The recognition of the importance of secondary use of EHR data has led to the development of research-ready integrated data repositories (IDRs) from EHR data. Analyzing this data can help researchers connect the dots and can lead to critical clinical findings through predictive analytics methods. Unfortunately, poor data quality is a problem that affects the accuracy of such findings. An example of a data quality problem is poor information about the specifics of admission, discharge, and readmission. Heart Failure (HF) is one of the most common cardiovascular diseases. 5.7 million people in the United States have heart failure with 870,000 new cases annually, and this disease is the leading cause of hospital readmission. Predicting readmission for heart failure patients has been well-studied. The readmission periods that researchers have studied range between 30 days to one year. However, shorter than 30 days readmission have received less research attention. In my research, I shed light on an overlooked yet important group of readmissions: very early readmissions. Currently, little is known about what causes heart failure patients to come back so quickly. In the long term, my career goal is to predict very early readmission patients before discharge and improve on the discharge decision making. It is a step toward personalized healthcare to improve patient care eventually. The broad goal of my dissertation is to leverage the availability and accessibility of electronic health data and characterize day 1-30 readmission, more specifically characterizing very early readmissions. My approach to reach my goal went through four major steps: 1) Reviewing the literature to understand the field and how early readmission have been defined, 2) Using retrospective EHR data from UW Medicine to build an accurate visit table for heart failure patients, 3) Using the visit table to build a prediction model to characterize day 1-30 readmissions, 4) Improving on the model by applying different machine learning algorithms and imputation techniques for missing data.