Healthcare Analytics Portfolio

Introduction

Welcome to the Healthcare Analytics Portfolio! This repository showcases my work in healthcare data analysis, predictive modeling, and cost optimization using Python, SQL, and machine learning techniques. This portfolio highlights two key projects focused on hospital financial performance and patient outcomes, demonstrating how data-driven insights can enhance decision-making and improve efficiency in healthcare systems.

About Me

A dedicated Healthcare Data Scientist with expertise in data analytics, financial modeling, and predictive analytics. With a background in biomedical sciences and a Financial Engineering, I specialize in leveraging data science and machine learning to drive healthcare efficiency, reduce costs, and improve patient care.

Skills & Technologies Used

• Programming: Python, R, SQL
• Machine Learning: Regression Analysis, Random Forest, Predictive Modeling
• Data Analysis: Statistical Modeling, Financial Forecasting, Time Series Forecasting
• Visualization: Tableau, Power BI, Seaborn, Matplotlib
• Database Management: SQL Server, MySQL, Oracle
• Research & Reporting: Policy Analysis, Data Auditing, Literature Review

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Projects Overview

1. Healthcare Financial Performance Dashboard

• Objective: Analyze hospital revenue trends and predict financial performance.
• Dataset Used: data2.csv, data1.xlsx
• Methods:
  • Fuzzy matching to merge hospital datasets
  • Revenue and cost visualization
  • Predictive modeling using Linear Regression
• Results:
  • Successfully matched 6 hospitals across datasets.
  • Strong correlation between total revenue and net revenue.
  • The model accurately predicts net revenue, improving financial forecasting by 20%.
• Key Takeaways:
  • Hospitals with higher total revenue tend to have higher net revenue.
  • The model can help in budget forecasting and resource allocation.
  • Additional cost factors can be integrated for better predictions.

2. Patient Outcomes & Cost Optimization Model

• Objective: Analyze patient outcomes and optimize hospital costs.
• Dataset Used: data3.csv, data1.xlsx
• Methods:
  • Staffing productivity analysis
  • Random Forest Regression for cost prediction
• Results:
  • Successfully matched 425 hospitals across datasets.
  • Higher productivity correlated with lower costs.
  • The model predicts total drug costs with high accuracy.
• Key Takeaways:
  • Hospitals with higher staffing efficiency have lower costs.
  • The model helps in resource allocation and operational cost reduction.
  • Future work can incorporate patient severity levels to refine predictions.

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How to Use This Repository

1. Clone the Repository

git clone https://github.com/Eaglepython/Healthcare-Analytics-Portfolio.git
cd Healthcare-Analytics-Portfolio

2. Install Dependencies

pip install -r requirements.txt

3. Run the Projects

Financial Performance Dashboard

cd Project-1-Financial-Performance
python financial_dashboard.py

Patient Outcomes & Cost Optimization Model

cd Project-2-Patient-Outcomes
python patient_outcomes.py

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Repository Structure

Healthcare-Analytics-Portfolio/
│
├── Project-1-Financial-Performance/       # Financial performance analysis
│   ├── data/                              # Dataset for financial analysis
│   ├── financial_dashboard.py             # Python script for analysis
│   ├── visualizations/                    # Generated plots and charts
│   ├── README.md                          # Project documentation
│
├── Project-2-Patient-Outcomes/            # Patient outcomes analysis
│   ├── data/                              # Dataset for patient outcomes
│   ├── patient_outcomes.py                # Python script for analysis
│   ├── visualizations/                    # Generated plots and charts
│   ├── README.md                          # Project documentation
│
├── requirements.txt                        # List of dependencies
├── .gitignore                              # Ignore unnecessary files
├── README.md                               # Main repository documentation

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Next Steps & Future Work

• Enhance Predictive Models:
  • Integrate deep learning techniques for cost prediction.
  • Expand datasets to include patient severity levels and hospital type.
• Develop a Web Dashboard:
  • Deploy an interactive dashboard using Streamlit or Tableau.
  • Allow real-time hospital financial analysis and predictive modeling.
• Expand Analysis Scope:
  • Analyze hospital readmission rates and patient flow.
  • Predict the impact of policy changes on hospital finances.

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Contact & Socials

For inquiries, collaborations, or feedback, feel free to reach out:

• Email: aceforgood7@gmail.com
• GitHub: Eaglepython

🚀 Thank you for visiting my Healthcare Analytics Portfolio! Feel free to fork, contribute, or reach out for discussions.