Dasha69-ops/mediorb-healthcare-dashboard

Market validation & Power BI dashboard for a healthcare appointment-booking platform in Nigeria demand research, competitor failure analysis, and supply-side facility data translated into a decision-ready go-to-market strategy. # MediOrb — Healthcare Appointment Booking: Market Validation & Dashboard Strategy _Data analysis for a healthcare appointment-booking platform, built as part of Team Nova at the TechCrush Alumni Buildathon._ ## Business Problem Nigeria's healthcare system serves an estimated 242.4M people (UN Population Division, 2026) through 42,063 registered facilities, but only 8.5% of those facilities have any form of digital booking system. MediOrb set out to validate whether there was real demand for a digital appointment-booking platform, and whether the supply side (the facilities themselves) could realistically support it. Before writing a line of product code, the team needed to answer: **is this a real market, or a solution looking for a problem?** ## My Role As data analyst on Team Nova, I analyzed the demand-side survey data (collected by the design team), led the supply-side facility research and competitor failure analysis, and drove the Power BI dashboard strategy behind this validation report, translating raw survey results, Play Store review data, and facility-level statistics into a decision-ready case for the product and engineering team. ## Approach - **Demand-side:** Analyzed an anonymous survey (n=82, collected by the design team) covering demographics, digital readiness, healthcare access barriers, and adoption intent - **Competitive analysis:** Sourced and reviewed 44 documented user complaints from Google Play Store reviews of existing healthcare apps, to identify _why_ competitors were failing, not just that they were - **Supply-side:** Analyzed facility-level data across 42,063 registered healthcare facilities density, ownership (public/private), digital booking penetration, and geographic distribution - **Synthesis:** Combined both sides into a phased go-to-market strategy (pilot → public network expansion → rural USSD strategy), with the rollout sequencing informed in part by AI-generated projections ## Key Findings **Demand is real and quant …