Hospital Patient Wait-Time Optimization is a healthcare analytics project focused on reducing patient delays and improving operational efficiency using SQL, Python, and Power BI.
The project analyzes 15,000+ patient records to identify bottlenecks in emergency care, OPD services, bed utilization, and staffing allocation.
- Analyze patient wait times across departments
- Identify peak-hour congestion patterns
- Perform root cause analysis (RCA) on service delays
- Monitor bed occupancy and doctor availability
- Build interactive dashboards for hospital operations
- Recommend staffing optimization strategies
- Peak-hour congestion increased wait times by 34%
- Staff shortages contributed to 22–28% of delays
- Emergency wait times averaged 61 minutes
- Average bed utilization reached 78%
- Proposed optimization reduced predicted wait times by 18%
- SQL
- Python
- Pandas
- NumPy
- Power BI
- HTML/CSS/JavaScript
- Chart.js
The dataset contains 15,000+ synthetic hospital patient records including:
- Patient demographics
- Admission details
- Wait times
- Bed occupancy
- Doctor availability
- Staffing metrics
- Delay reasons
- Real-time wait time monitoring
- Department-level performance analysis
- Bed occupancy tracking
- Root cause analysis visualization
- Staffing optimization recommendations
- Hourly congestion heatmaps
├── data/ │ └── hospital_patient_data.csv ├── dashboard/ │ └── hospital_waittime_dashboard.html ├── sql/ │ └── queries.sql ├── notebooks/ │ └── analysis.ipynb ├── README.md
This project demonstrates how healthcare analytics can improve operational efficiency and reduce patient waiting time through data-driven decision making.
Balashanmugam R