Dhia0184/Tunisia-Healthcare-Flow-Analysis

Business Intelligence project analyzing Emergency Department patient flow and bottlenecks at Hôpital Charles Nicolle, Tunisia. # 🏥 Emergency Department BI Solution — Patient Flow & Revenue Optimization > A Business Intelligence project designed to reduce ED overcrowding, minimize revenue leakage, and improve patient outcomes at **Hôpital Charles Nicolle**. --- ## 📌 Project Overview The Emergency Department at Hôpital Charles Nicolle handles approximately **180,000 visits per year** but faces critical operational challenges: excessive patient wait times, high "Left Without Being Seen" (LWBS) rates, and misaligned staffing schedules. This BI solution visualizes the **end-to-end patient journey** to help administrators identify and relieve throughput bottlenecks — with a primary goal of reducing the LWBS rate by **40%** within the first year. --- ## 🎯 Business Objectives | Objective | Description | |---|---| | Bottleneck Detection | Identify the longest delays across the patient journey stages | | Peak Demand Analysis | Determine high-volume days and hours for better resource planning | | Staffing Alignment | Compare actual nurse-to-patient ratios against clinical standards | | LWBS Risk Profiling | Profile patients most likely to leave without being seen | | Triage Effectiveness | Detect potential triage misclassifications | | Re-admission Risk | Track 72-hour return rates by diagnosis | | Resource Saturation | Monitor bed occupancy trends throughout the day | --- ## 📊 Key Performance Indicators (KPIs) | # | KPI | Formula | Business Relevance | |---|---|---|---| | 1 | **Door-to-Doc Time** | Medical assessment time − Arrival time | Most critical metric for patient safety | | 2 | **LWBS Rate** | Patients left ÷ Total arrivals × 100 | Measures lost revenue & liability risk | | 3 | **Average Length of Stay (LOS)** | Discharge time − Arrival time | Measures total system efficiency | | 4 | **Boarding/Waiting Time** | Admission to ward − Decision to admit | Delay in moving patients out of ED | | 5 | **Bed Occupancy Rate** | Occupied beds ÷ Total beds × 100 | Predicts department saturation …