https://github.com/anuj-kshatriya/sql_project_hospital_data

This project focuses on analyzing hospital data using SQL queries. It provides insights into patient care, medical expenses, hospital performance, and departmental efficiency. The dataset includes hospital names, locations, departments, doctor and patient counts, admission/discharge dates, and medical expenses
https://github.com/anuj-kshatriya/sql_project_hospital_data

dataanalysis dataanalysis-projects dataanalytics projects sql sqlproject

Last synced: 4 months ago
JSON representation

This project focuses on analyzing hospital data using SQL queries. It provides insights into patient care, medical expenses, hospital performance, and departmental efficiency. The dataset includes hospital names, locations, departments, doctor and patient counts, admission/discharge dates, and medical expenses

• Host: GitHub
• URL: https://github.com/anuj-kshatriya/sql_project_hospital_data
• Owner: anuj-kshatriya
• Created: 2025-04-03T15:35:24.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2025-04-03T15:50:52.000Z (10 months ago)
• Last Synced: 2025-06-07T15:47:09.990Z (8 months ago)
• Topics: dataanalysis, dataanalysis-projects, dataanalytics, projects, sql, sqlproject
• Homepage:
• Size: 5.86 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

          
# SQL_project_HOSPITAL_DATA

# 🏥 Hospital Data Analysis using SQL  

## 📂 Database & Tables  

The project is structured around a **Hospital** database with a key table:  

- **Hospital_data** 🏨: Stores details of hospitals, departments, doctors, patients, medical expenses, and admissions/discharges.  

## 🔍 Key Analyses & Insights  

✅ **Total Patient Count** – Finding the total number of patients treated.  

✅ **Average Number of Doctors per Hospital** – Understanding staffing levels.  

✅ **Top 3 Departments with the Most Patients** – Identifying high-demand medical specialties.  

✅ **Hospital with the Maximum Medical Expenses** – Finding cost-intensive hospitals.  

✅ **Daily Average Medical Expenses** – Analyzing hospital spending efficiency.  

✅ **Longest Hospital Stay** – Identifying cases with extended patient stays.  

✅ **Total Patients Treated Per City** – Mapping healthcare demand by location.  

✅ **Average Length of Stay Per Department** – Understanding hospital resource utilization.  

✅ **Department with the Lowest Patient Count** – Finding underutilized specialties.  

✅ **Monthly Medical Expenses Report** – Analyzing hospital costs on a monthly basis.  

## 🛠️ Technologies Used  

- **SQL (MySQL/PostgreSQL)** for data querying and analysis.  

- **Aggregate functions, GROUP BY, ORDER BY, and JOINS** for extracting insights.  

- **Date functions** for calculating stay durations and monthly expenses.  

## 📊 Future Enhancements  

- Introduce **predictive analytics** for hospital admissions.  

- Implement **performance metrics** for hospital efficiency evaluation.  

- Create **interactive dashboards** for visualizing key insights.  

This project is ideal for learning **SQL-based data analysis in the healthcare domain**, providing valuable business and operational insights for hospitals! 🚀