Hospital Emergency Room Dashboard

Overview

An interactive Power BI dashboard analyzing 19 months of emergency room activity (April 2023 – October 2024) across 9,216 patient visits — built to surface wait time issues, satisfaction trends, referral patterns, and staffing/peak-time insights for ER operations teams.

Tools used: Power BI Desktop · Power Query

The dashboard has four pages:

• Monthly View — Single-month snapshot with a date filter: patient count, wait time, satisfaction score, referrals, admission status, demographics, and hourly/daily traffic
• Consolidated View — Same KPIs aggregated across the full date range, with a date slicer for custom ranges
• Patient Details — Row-level table of every patient visit (ID, name, gender, age, race, wait time, referral, admission status)
• Key Takeaways — Written descriptive analysis summarizing the headline findings

Key Metrics (Full Period)

• 9,216 unique patient visits
• 35.3 min average wait time
• 4.99 / 10 average patient satisfaction score
• 3,816 patients referred out to another department
• 50.04% of patients admitted vs. 49.96% treated and released
• 59.32% of patients waited over 30 minutes
• Busiest day: Saturday (1,377 patients); busiest hours: 00:00–02:00
• Largest age group: 30–39 years (1,200 patients)
• Largest patient race group: White (2,571 patients)

Dataset

The source dataset contains one row per patient visit, with fields covering patient ID, admission date/time, demographics (gender, age, race), department referral, admission status, satisfaction score, and wait time.

Insights & Recommendations

• Wait times exceed 30 minutes for the majority of patients — staffing during peak hours (00:00–02:00, especially Fri–Sun) could reduce this
• Satisfaction score (4.99/10) points to room for improving patient experience, likely tied to wait times
• General Practice and Orthopedics are the top referral destinations — worth reviewing capacity/turnaround in those departments
• Nearly half of all patients require admission, reinforcing the need for adequate bed/staff availability around peak days