Data-Driven Insights into Hospital Readmissions

Publication Date

2025

Presentation Length

Poster/Gallery presentation

College

College of Sciences & Mathematics

Department

Math and Computer Science, Department of

Student Level

Undergraduate

SPARK Category

Research

Faculty Advisor

Christina Davis

SPARK Session

Data Science

Presentation Type

Poster

Summary

Hospital readmissions are a growing concern for healthcare systems due to their financial cost and impact on patient outcomes. This research explores data-driven techniques for analyzing patient record trends and determining variables linked to an increased risk of readmission. This project uses machine learning models and analytical techniques and assesses their effectiveness in predicting readmission outcomes. In addition, statistical techniques were applied to compare patient subgroups and identify significant patterns in the data. The results provide insights on the variables that might be early predictors of readmission and the ways in which data analysis can help hospitals enhance care planning and lower preventable returns.

This research demonstrates the potential of predictive modeling in enhancing patient care strategies and shows how data science can be used in a healthcare setting to enable better informed decision-making.

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