Hospital-Emergency-Room-Data-Analysis

Database: Microsoft SQL Server (T-SQL, CTEs, Window Functions)
Analysis: Python (Jupyter Notebook, Pandas, NumPy)
Visualization: Power BI (Interactive Dashboards, DAX), Seaborn, Matplotlib
Documentation: Microsoft Word (Strategic Reporting)

🔍 Key Analytical Features

Patient Demographics: Segmented population into Children, Adults, and Seniors, revealing that 77% of patients are Adults or Seniors.
Operational Efficiency: Measured average wait times (consistent at ~35 mins) and flagged admission rates (50.04%).
Data Quality Audit: Identified a massive 58% "Unknown" referral rate and a 72% NULL rate in patient satisfaction scores.
Statistical Analysis: Conducted correlation matrices and outlier detection (Z-Score > 3) to validate data integrity.
Time-Series Trends: Analyzed Month-over-Month (MoM) admission fluctuations from April 2023 to October 2024.

💡 Strategic Recommendations

Standardize Data Entry: Implement mandatory EHR fields to eliminate the high "Unknown" referral and demographic categories.
Predictive Staffing: Align clinical shifts with peak admission months (August/May) identified in the trend analysis.
Enhance Patient Feedback: Deploy point-of-care SMS surveys to bridge the 72% satisfaction data gap.
Targeted Care Models: Tailor facility resources toward geriatric and adult chronic care to match the 77% primary demographic.

📂 Project Structure

/SQL_Queries/: T-SQL scripts for data extraction and business logic.
/Python_Analysis/: Jupyter Notebooks for statistical modeling and correlation.
/Dashboards/: Power BI .pbix files and screenshots of interactive reports.
/Reports/: Final executive summary and strategic recommendations.

Dataset Link: https://www.kaggle.com/datasets/xavierberge/hospital-emergency-dataset