An interactive analytics dashboard for exploring hospital emergency room (ER) operations and patient flow. This dashboard provides detailed visualizations of ER data, helping administrators and staff identify patterns, monitor performance, and optimize resource allocation.
The Hospital Emergency Room Dashboard is designed to visualize hospital ER data to help understand patient behavior, operational efficiency, and performance metrics. Using structured ER data, this dashboard highlights trends in:
- Patient arrivals and visit volumes
- Wait times and patient throughput
- Admission vs. discharge ratios
- Demographics and patient satisfaction
- Departmental referrals
It is intended for hospital administrators, healthcare analysts, and operational managers who need actionable insights from historical ER data.
The dashboard uses a structured ER dataset containing the following types of information:
| Data Category | Fields | Description |
|---|---|---|
| Patient Information | Patient ID, Age, Gender, Race | Unique identifiers and demographics for ER patients |
| Visit Details | Visit Date, Arrival Time, Discharge Time, Wait Time, Admission Status | Captures patient flow, duration of stay, and outcomes |
| Medical & Departmental Data | Department Referred To, Reason for Visit, Treatment Provided | Helps analyze department workload and referral patterns |
| Operational Metrics | ER Capacity, Staff on Duty, Hourly Patient Count | Supports resource planning and identifying bottlenecks |
| Patient Feedback | Satisfaction Score, Feedback Comments | Measures patient satisfaction by demographic or visit type |
The data can come from hospital ER logs exported as CSV or structured database extracts.
- Total ER Visits: Track daily, weekly, monthly, and yearly patient volumes.
- Patient Demographics Analysis: Breakdown by age, gender, and race to identify which groups use ER services the most.
- Wait Time Analysis: Average wait times by hour, day, and department to detect bottlenecks.
- Admissions vs. Discharges: Compare numbers to understand ER load and hospitalization rates.
- Referral Analysis: Examine which departments receive the most referrals and from which patient groups.
- Satisfaction Analysis: Compare patient satisfaction across demographics, visit types, and departments.
- Peak Hour Heatmaps: Identify busiest times of day to improve staffing efficiency.
- Trends & Forecasting: Observe trends in patient volume and wait times over months or years.
- Patient Volume Patterns: Identify high-volume days and months to optimize staffing schedules.
- Wait Time Bottlenecks: Detect times when wait times spike and allocate resources efficiently.
- Demographic Trends: Determine which age groups or genders are most frequent ER users.
- Admissions Analysis: See the ratio of patients admitted vs discharged to anticipate bed occupancy.
- Departmental Workload: Understand which departments handle the most ER referrals.
- Satisfaction Trends: Track which groups report lower satisfaction to improve patient experience.
- Hourly Analysis: Pinpoint the busiest hours for targeted resource deployment.
| Category | Tool |
|---|---|
| Data Visualization | Power BI Desktop |
| Data Processing | Power Query, DAX (Data Analysis Expressions) |
| Data Source | CSV/Database of ER visits |
| File Format | .pbix for the Power BI project |
Below are the key dashboard views included in this project.
A high‑level view of the emergency room performance showing core metrics like:
- Total number of patients
- Average wait times
- Satisfaction scores
- Admissions vs non‑admissions
Visualizes trends in patient visits over time, including breakdowns by gender, age group, and visit frequency.
Shows wait time distribution by department, time of day, and day of week — helping identify bottlenecks.
Displays key insights from all dashboards.
(January 2023 - December 2024)
The emergency room dataset, spanning 24 months, provides valuable insights into the patterns and trends of 3,613 unique patients.
- Average wait time: ≈35.3 minutes, highlighting the need to improve operational efficiency.
- Overall satisfaction score: 4.86 / 10, indicating moderate satisfaction levels.
- Patients aged 50-59 years gave the highest satisfaction (5.29), while those aged 70-79 years rated the lowest.
-
2,096 patients did not require any referrals.
-
Among referred patients, the most common departments were:
- General Practice: 722 cases
- Orthopedics: 392 cases
- Physiotherapy: 104 cases
- Cardiology: 99 cases
Busiest Days:
- Wednesday: 549 patients
- Monday: 543 patients
- Sunday: 528 patients
Busiest Hours:
- 11 PM, 12 AM, 6 PM, 7 PM, indicating the need for sufficient staffing during these times.
Age Groups:
- 20-29 years: 501 patients (largest group)
- 30-39 years: 472 patients
- 60-69 years: 473 patients
- 50-59 years: significant number of patients
Race Distribution:
- White: 1,008
- African American: 780
- Multiracial: 606
- Asian: 402
- Declined to identify: 399
- Admitted: 1,792 patients
- Treated & Released: 1,821 patients
This analysis highlights:
- High ER patient volume
- Moderate satisfaction levels
- Significant demand for General Practice and Orthopedics referrals
- Busiest periods: Wednesdays and late-night / early-morning hours
- Diverse patient demographics
- Highest satisfaction among 50-59 year-olds (5.29), lowest among 70-79 year-olds
These insights provide actionable data to enhance resource allocation, streamline operations, and improve patient care quality.
Hospital-Emergency-Room-Dashboard/
├── Hospital ER_Data.csv # Cleaned dataset with patient, visit, and operational details
├── Hospital Emergency Room Dashboard.pbix # Power BI dashboard file
├── Hospital Emergency Room Dashboard.pdf # PDF of Power BI dashboard file
└── README.md # Detailed project documentation
- Download the dataset: Ensure
Hospital ER_Data.csvis available in the same directory. - Open Power BI Desktop: Load
Hospital Emergency Room Dashboard.pbix. - Explore Visualizations: Use filters and slicers to examine trends by date, department, patient demographics, and other key metrics.
Requires Power BI Desktop (free version available).
- Predictive Modeling: Forecast peak hours and patient volume using historical data.
- Real-Time Integration: Connect to live hospital data feeds for up-to-date dashboards.
- Expanded Metrics: Include treatment success rates, ER cost analysis, or length of stay statistics.
- Interactive Reports: Publish dashboards to Power BI Service for web access and sharing.