Part 7: Building a Healthcare Readmission Dashboard with Power BI

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Introduction

In this final part of the series, I built an interactive Power BI dashboard using the cleaned CMS Hospital Compare dataset prepared in the previous tutorials. By combining SQL Server and Power BI, I transformed publicly available healthcare data into visual reports that summarize hospital readmission performance across the United States.

Step 1. Connect Power BI to SQL Server

After cleaning and validating the dataset in SQL Server, I connected Power BI directly to my local SQL Server instance using Windows Authentication. Connecting Power BI directly to SQL Server allowed me to use the cleaned dataset without exporting it to a separate CSV or Excel file.

Load the Cleaned Dataset

After connecting successfully, I selected the Hospital_Readmission_Cleaned table from SQL Server. This table contains the cleaned data prepared in the previous tutorials and serves as the data source for the dashboard.

Verify the Imported Data

Before creating visualizations, I reviewed the imported fields in Power BI to confirm that the dataset loaded correctly. I also checked for blank values and ensured the columns were ready for reporting.

Step 2. Create KPI Cards

To provide a quick summary of the dataset, I created four KPI cards displaying the total number of hospitals, total discharges, total readmissions, and the average predicted readmission rate. These summary metrics provide an overview before exploring the detailed visualizations.

The dashboard summarizes:

  • Total Hospitals
  • Total Discharges
  • Total Readmissions
  • Average Readmission Rate

These summary metrics allow users to understand the overall dataset before exploring more detailed visualizations.

Step 3. Compare Readmission Rates by State

The first visualization compares the average readmission rate across different states.

This chart makes it easier to identify regional variation within the CMS Hospital Compare dataset and provides a high-level view of hospital readmission performance.

Step 4. Compare Clinical Measures

The second visualization compares average readmission rates across different clinical measures.

This comparison highlights how readmission performance varies among the reported healthcare measures and provides another perspective for exploring the dataset.

Different clinical measures show different average readmission rates. Visualizing these differences helps identify conditions that may benefit from additional quality improvement efforts.

Step 5. Build an Executive Dashboard

Completed Healthcare Dashboard

The completed dashboard combines summary metrics and interactive visualizations into a single report.

The dashboard includes:

  • KPI summary cards
  • State-level readmission comparison
  • Clinical measure comparison
  • Key insights

Together, these visualizations present hospital readmission information in a clear and organized format that supports exploration of the CMS dataset. Well-designed healthcare dashboards use concise KPIs and charts to help users understand patterns in complex datasets.

Key Insights

  • The dataset includes hospital records from multiple U.S. states.
  • The average predicted readmission rate in this dataset is approximately 15.8%.
  • Readmission rates vary across states.
  • Different clinical measures show different average readmission rates.
  • The dashboard was developed using publicly available CMS Hospital Compare data.
  • I structured this analysis to isolate the ‘Predicted Readmission Rate’ metric. This ensures the data reflects CMS risk-standardized logic, accounting for patient comorbidity severity rather than unfairly penalizing high-acuity teaching facilities.

Conclusion

This seven-part project demonstrated an end-to-end healthcare analytics workflow, from downloading CMS Medicare Hospital Compare data to cleaning it in SQL Server and building an interactive Power BI dashboard. Through this project, I strengthened my SQL, Power BI, and healthcare analytics skills while completing a portfolio project based on publicly available healthcare data.

📂 SQL Server Series

✅ Part 1 – How to Download Medicare Hospital Readmission Data
✅ Part 2 – Import Data into SQL Server
✅ Part 3 – Clean Medicare Data with SQL
✅ Part 4 – Exploring Medicare Hospital Readmission Rates with SQL
✅ Part 5 – Filtering Healthcare Data with WHERE and HAVING
✅ Part 6 – Visualizing Healthcare Data with Power BI
✅ Part 7 – Building a Healthcare Readmission Dashboard with Power BI

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