sruthi-kasturi / SQL_Synthea_Analytics

A project that uses SQL to analyze health data from the Synthea dataset. It looks at common health conditions, resource use, and provides insights for better health management.

🏥 Synthea_SQL_Analytics: In-Depth Analysis of the Synthea Dataset

Table of Contents

1. Introduction
2. Project Structure
3. Setup Instructions
   • Requirements
   • Installation
4. Usage
5. Key Insights
6. Conclusion
7. Future Work
8. About Me
9. Contact Information
10. Acknowledgements

Introduction

Welcome to my first SQL project! This repository contains a comprehensive analysis of the Synthea dataset, aimed at uncovering meaningful insights into healthcare patterns. The main focus areas include health conditions, vaccination trends, and hospital encounter durations. Link to the dataset

Project Structure

• Synthea Report.pdf: Dive into the detailed report that covers my analysis, findings, and conclusions.
• SQL_Project_script.sql: Explore the complete SQL script that powers this analysis.

Setup Instructions

Requirements

• PostgreSQL

Installation

1. Download PostgreSQL: Get it from the official PostgreSQL website.
2. Clone the Repository: Use the command git clone https://github.com/yourusername/SQL-Project.git to clone this repo.

Usage

Running the SQL Script

1. Connect to PostgreSQL: Open your PostgreSQL client and connect to your database.
2. Execute the Script: Run the following command to execute the SQL script:

   \i path/to/SQL_Project_script.sql
   

Key Insights

Here are some of the fascinating insights uncovered during this project:

• Health Conditions Across Ages: Identified the most prevalent health conditions within various age groups.
• Vaccination Patterns: Analyzed the most frequently administered vaccines.
• Hospital Encounter Durations: Calculated the average length of hospital stays across different age demographics.

Conclusion

This project highlights my ability to analyze complex healthcare data using SQL. The insights derived offer valuable perspectives on health conditions, vaccination trends, and hospital encounters.

Future Work

While this project lays a solid foundation, there are numerous avenues for further exploration:

• Demographic Analysis: Deeper analysis of additional demographic factors.
• Expanded Dataset Integration: Incorporating other datasets for a more holistic view.
• Advanced Analytics: Applying machine learning techniques for predictive insights.

About Me

I'm Sruthi Kasturi, currently pursuing my master's in Biomedical Informatics and Data Science at Arizona State University. This project marks my foray into the world of SQL and data analysis, combining my passion for healthcare and data science. Feel free to connect with me for any questions or collaborations!

Contact Information

• Email: [e-mail]
• LinkedIn: www.linkedin.com/in/sruthi-kasturi

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Thank you for exploring my project! Your feedback and suggestions are always welcome. Let's make healthcare data work for a better tomorrow!