This project focuses on analyzing real-world Emergency Room (ER) operational data to uncover critical insights regarding patient demographics, hospital operations, and efficiency constraints. By executing a series of 30 structured SQL queries (ranging from basic data exploration to advanced multi-level analytical CTEs and Window Functions), this repository provides actionable recommendations to optimize ER resources, reduce patient wait times, and enhance overall operational throughput.
Based on the advanced data analysis of 9,216 patient records, here are the major operational findings:
- Peak Demand Hours (Resource Allocation): The absolute peak hour for ER admissions is 11:00 PM (recording 436 visits). This points toward a critical need for strengthening overnight medical staffing.
- High-Bottleneck Departments (Operational Delays): Patients referred to Neurology and Physiotherapy experience the highest average wait times (~36.8 and ~36.5 minutes respectively). These departments require process tuning or added diagnostic support.
- High Conversion & Admission Rates: Out of all ER triages, 50.04% result in successful hospital admissions, indicating that half of the incoming ER pipeline comprises high-acuity cases requiring inpatient care.
- Patient Flow Profiling: The ER treats a perfectly diverse group across all ages (Average age: ~40 years, ranging from infants to seniors up to 79 years old).
├── Hospital_ER_Data.csv # Raw ER Operational Dataset (9,216 rows)
├── Hospital_ER_Analytics.sql # Full SQL Script containing all 30 Queries
└── README.md # Project Documentation & Insights