natashazengeni/health-access-service-pressure

Power BI analysis of healthcare access, service pressure and patient experience across Zimbabwe, January - June 2026. Zimbabwe Health Access & Service Pressure Dashboard Power BI analysis of healthcare access, service pressure and patient experience across Zimbabwe, January–June 2026. 📊 Project Overview This project analyses healthcare service activity and performance across Zimbabwe using monthly facility-level data from January to June 2026. The dashboard was developed to explore patterns in healthcare utilisation, service pressure, medicine availability, patient satisfaction, referral completion and digital health infrastructure. The analysis focuses on identifying geographic differences in service performance and highlighting areas that may require further investigation or targeted intervention. 🎯 Objectives - Assess healthcare service utilisation across provinces. - Identify geographic differences in patient waiting times. - Examine medicine stockout patterns across districts. - Compare patient satisfaction across provinces. - Assess referral completion and telehealth activity. - Examine network uptime as an indicator of digital health infrastructure. - Identify areas that may benefit from targeted operational attention. 🛠️ Tools & Technologies - Microsoft Power BI - DAX - Data cleaning and transformation - Data visualization - Excel 📈 Key Performance Indicators Indicator| Result Total outpatient visits| 414K Total telehealth consultations| 68K Average waiting time| 82.6 minutes Average patient satisfaction| 3.6 / 5 Average network uptime| 83% Average referral completion| 74% 🔍 Key Insights 1. Service pressure varies across provinces The overall average waiting time was 82.6 minutes, with Mashonaland West recording the highest average waiting time at 87.2 minutes, compared with 79.0 minutes in Manicaland. This variation suggests differences in service pressure or operational conditions across provinces and highlights areas that may require further investigation. 2. Medicine availability is uneven across districts Medicine stockout periods varied considerably b …