Healthcare Data Analysis and Insights

Objective

The aim of this project is to analyze healthcare data to extract actionable insights that could help improve patient care and healthcare resource management. By working with multiple datasets, I utilized various Excel techniques to clean, transform, and analyze the data to uncover meaningful patterns.

Project Overview

This project focuses on analyzing healthcare data, such as patient health profiles, medical histories, and healthcare costs. The insights gained from this analysis are intended to assist healthcare stakeholders in making informed decisions regarding patient care and resource allocation.

Key Features:

• Data Sources: 3 healthcare datasets
• Tools Used: Microsoft Excel
• Focus Areas: Data cleaning, transformation, and visual analysis

Data Cleaning and Transformation

• Basic Data Cleaning: Learned and applied techniques such as using mean, median, and mode to clean the data.
• Patient Segmentation:
  • BMI (Body Mass Index): Segregated patients as Underweight, Healthy, Overweight, and Obese.
  • Blood Sugar Levels: Classified patients as Normal, Pre-Diabetic, and Diabetic.

Data Analysis

1. Smoking and Cancer Risk:

   • Insight: Using a Donut Chart, I found that non-smokers have a higher cancer history compared to smokers, indicating that smoking might not be the only factor contributing to cancer risk.
   • Obesity & Cancer Correlation: Through further analysis, I observed that obese and overweight patients were more prone to cancer than those with a normal weight.
   • Research Findings: Web research showed that being overweight can lead to insulin resistance, increasing the risk of colon cancer.

2. Blood Sugar and Surgeries:

   • Diabetes Analysis: Investigated the blood sugar levels of obese and overweight patients and found that most were either diabetic or pre-diabetic.
   • Surgeries & Blood Sugar: Using another Donut Chart, I found that patients with uncontrolled blood sugar levels underwent more than two surgeries.

3. Hospital Charges:

   • Obesity & Costs: Obese patients were found to incur higher hospital charges compared to others, even if their blood sugar levels were normal.
   • Valuable Insight: Maintaining a healthy weight through exercise and diet is critical to preventing diseases such as cancer and reducing healthcare costs.

4. Hospital Charge Trends:

   • Tier 2 Hospitals: A Column Chart revealed that Tier 2 hospitals had the highest hospital charges. This may be due to the developing nature of these cities, which may lack proper sanitation and awareness about physical health.
   • Charges by Age: With the help of a Line Chart, I observed that hospital charges increase with age, but after 60, these charges rise dramatically due to age-related factors.

5. Age, BMI, and Blood Sugar Trends:

   • As age increases, both blood sugar and BMI levels rise.
   • Beyond 60 years old, blood sugar levels increase sharply, while BMI levels decrease.
   • Young Patients (25-35): A surprising observation was the presence of obesity among younger patients, as indicated by peaks in the BMI trend.

Conclusion

This analysis highlights the importance of maintaining a healthy weight and blood sugar levels to prevent cancer and reduce healthcare costs. Additionally, Tier 2 cities should focus on improving healthcare infrastructure and awareness to minimize hospital charges.

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Key Excel Functions & Techniques Used:

• Data Cleaning: Mean, Median, Mode functions.
• Data Segmentation: Conditional formatting and formulas to categorize BMI and blood sugar levels.
• Charts:
  • Donut Chart: Used to compare smokers vs. non-smokers and their health histories.
  • Column Chart: Displayed hospital charges in different regions.
  • Line Chart: Showed trends in hospital charges by age.

Visualizations

• Donut Chart: Smoking and Cancer History Comparison
• Column Chart: Hospital Charges by Tier
• Line Chart: Hospital Charges and BMI Trends by Age

Project Resources

• Excel Sheet for Healthcare Data Analysis
• Project Guide Sheet