Risk Factors Associated with 30-Day Hospital Readmission:
A Data Analytics Perspective
Introduction
Hospital readmissions within 30 days of discharge remain a significant challenge for healthcare systems. Frequent readmissions can indicate gaps in patient care, increase healthcare costs, and negatively impact outcomes. Understanding the factors associated with readmission can help hospitals identify high-risk patients and implement targeted interventions.
In this project, I analyzed hospital readmission data using Tableau to explore patterns, identify risk factors and generate actionable insights that support better patient care decisions.
Project Objective
The goal of this analysis was to identify key characteristics associated with patients who were readmitted within 30 days after discharge.
The project aimed to answer the following questions:
- Which patient groups experience the highest readmission rates?
- Are certain age groups more likely to be readmitted?
- What demographic and clinical factors contribute most to readmissions?
Tools Used
- Tableau Public
- Data Visualization
- Healthcare Analytics
Dashboard Overview
The dashboard was designed to provide an interactive view of patient readmission patterns. Key visualizations included:
Readmission Overview
A summary of total patients, total readmitted patients, high-risk patients, readmission rate and average initial stay cost.
Demographic Analysis
Visualizations showing readmission trends across:
- Age groups
- Gender
- Marital Status
Other Factors
Analysis of:
- Clinical Risk Factors
- Insurance Provider
- Care Plan