Data Analytics for Identifying and Reducing Hospital Readmissions

Data analytics examines healthcare data to identify patterns and personalize care plans ultimately by reducing hospital readmissions. Readmissions after discharge increase healthcare costs, lower quality, and dissatisfied patients. To overcome these issues, a novel Data analytics approach has been proposed to reduce hospital readmissions. This inquiry begins with a complete dataset of clinical data, patient demographics, and Electronic Health Records (EHRs). Initially, the data gathered from the clinical data, patient demographics, and electronic health records are fed to the preprocessing module for transforming raw healthcare data into a structured form for analysis. After preprocessing, an Exploratory Data Analysis (EDA) is performed which simplifies the initial insights and hospital readmission risk factor identification. The most essential features are selected from the analyzed outcomes of EDA for readmission prediction. After feature selection, the development of hospital readmission models uses three data analytics methods. At first, the Machine Learning (ML) based methods predict the readmission status and the sequential relationships and anticipate readmission risk over time are captured using a neural network. Finally, the patient groups with comparable readmission trends are identified through hierarchical clustering. After data analytics, those outcomes are fed to the real-time patient monitoring system to reveal readmission factors such as discharge planning and timely drug management. The proposed method is validated by using healthcare datasets. In comparison, the proposed method achieves an accuracy of 97% whereas, the predictive modeling, risk factor, care process, and alert method achieves 85%, 78%, 90%, and 83% respectively.