TinyanOsa1/Healthcare-Access-Project-

This Power BI project analyzes healthcare accessibility in Africa, with a focus on urban vs. rural disparities. The goal is to visualize data to identify challenges and provide actionable insights for equitable healthcare delivery. # Healthcare-Access-Project- This Power BI project analyzes healthcare accessibility in Africa, with a focus on urban vs. rural disparities. The goal is to visualize data to identify challenges and provide actionable insights for equitable healthcare delivery. # Objectives • Analyze facility distribution, funding, and workforce availability. • Identify key disparities in healthcare access. • Provide insights and recommendations for policy improvement. ___ # Process Overview ### 1. Data cleaning and preparation Dataset provided by **Axia Africa** for this project. Dataset consist of: • 2000 Rows • 13 Column • I Imported CSV file into Power BI. • Verified data types and cleaned missing values (none found). • Added a new column to classify regions into Urban or Rural based on population, electricity, internet availability, and distance to facility. ### 2. Dashboard Design • Color Theme: Red and White • Background: Off-white • Primary: Medical Red (#E53935), Dark Red (#D32F2F) • Secondary: Teal (#00838F) ### 2.1 Layout Highlights: • KPIs (top-right): Total funding, Facility count by type, Average emergency response time • Title and image (top-left) • Country slicer (left panel) ___ # Insights • Urban areas have nearly 3x more facilities (hospitals, clinics, health centers) than rural areas. • Urban facilities receive more funding than rural ones. • There is no correlation between funding and response time. High funding doesn’t guarantee faster emergency response times. • Hospitals are most efficient (lowest cost per patient visit), followed by clinics. Health centers are the least efficient. • Urban areas have significantly more workforce and accessories (doctors, nurses, and beds) than rural areas ___ # Challenges • Rural facilities are underfunded. • Long distances to facilities. • Poor infrastructure (electricity, internet, transport). • Limited workforce in rural areas. ___ # Recommendations • Increase targeted funding in underserved rur …