Data-Driven Insights into Diabetes-Related Hospital Readmissions in the United States: Trends and Predictors

Abstract

Hospital readmissions is a key metric of evaluating healthcare quality, efficiency of care coordination, discharge planning, and follow-up care. Readmissions, defined as a patient's re-hospitalization within a specified period, such as 30 days, are frequently associated with incomplete treatments, medication errors, or inadequate follow-up care. Diabetes-related hospitalizations which account for a significant percentage of these readmissions in the United States is a critical and rising concern to healthcare authorities and the number is increasing year by year. From 2016 to 2019, diabetes-related 30-day readmission rates consistently surpassed all-cause readmissions (readmissions due to any medical condition), averaging approximately 19.5% compared to 13.9%. Diabetes-related readmissions incurred substantial financial and emotional costs, with aggregate re-hospitalization costs rising from $11.23 billion in 2016 to $14.03 billion in 2019. These financial and emotional burdens on patients and healthcare systems highlight the importance of targeted interventions to mitigate risks associated with readmissions. With the growing availability of large-scale healthcare data repositories and computing resources, it is possible to address critical challenges involved in hospital readmissions using predictive analytics.This study utilizes the Healthcare Cost and Utilization Project (HCUP) Nationwide Readmissions Database (NRD) from 2016 to 2019 to develop machine learning (ML) models for predicting 30-day readmissions for diabetic patients. Using diverse attributes/factors such as patient demographics, hospital characteristics, payer type, and discharge disposition, this research explores how predictive modeling approach based on healthcare data repositories can generate actionable insights to improve diabetes-related patient outcomes. Independent predictors identified include payer type, disposition type, and median household income demonstrating significant predictive values across ML algorithms. Ensemble approaches such as Boosted Trees and Bootstrap Forest outperformed traditional methods, achieving Area Under Receiver Operating Characteristics (AUROC) scores of 0.7417 and 0.6978, respectively, while maintaining low misclassification rates (31.4% for Boosted Trees). These results highlight the potential of ML models trained on large-scale datasets to optimize care coordination. The findings of this study emphasize the importance of socioeconomic and institutional factors in predicting diabetes-related readmissions and the role of data-driven methodologies in advancing healthcare. This study contributes to the broader application of predictive analytics in healthcare offering scalable solutions to lower readmissions using healthcare data repositories. Future directions include the refinement of ML models and comparisons with existing studies to improve predictive accuracy and healthcare delivery for diabetic patients.

Keywords: Diabetes, Healthcare, Hospital Readmissions, Machine Learning, Predictive Analytics

DOI: 10.54941/ahfe1006030