Interactive Healthcare Waiting List Dashboard built using Power BI to analyze patient demand, waiting times, and specialty performance through data-driven insights.
I developed a Healthcare Waiting List Power BI dashboard to give healthcare stakeholders a clear, real-time view of patient waiting lists across multiple specialties and case types. The dashboard analyzes patient demand across age profiles, waiting time bands, and medical specialties to help decision makers understand service pressure and prioritize healthcare resources.
It highlights the latest waiting list size of 709K patients compared to 640K patients in the previous year, revealing a growing healthcare demand. The dashboard also identifies high-pressure specialties such as Paediatric Orthopaedic, Paediatric Urology, and ENT, helping healthcare administrators focus on reducing waiting times and improving service delivery.
Healthcare systems often struggle with long patient waiting lists due to limited visibility into demand across specialties and patient groups. Decision-makers need a single analytical report that answers:
- Which specialties have the longest waiting lists?
- How are waiting times distributed across different age groups?
- Which case types contribute most to the waiting list?
- How has patient demand changed over time?
Without a centralized analytical dashboard, it becomes difficult to identify service bottlenecks, allocate resources efficiently, and improve patient care outcomes.
- Latest Month Waiting List: 709K patients
- Previous Year Waiting List: 640K patients
- Case Types: Outpatient | Day Case | Inpatient
- Waiting Time Bands: 0-3, 3-6, 6-9, 9-12, 12-15, 15-18 Months
- Top Specialties: Paediatric Orthopaedic, Paediatric Urology, ENT, Orthopaedics
- Growing patient backlog: The waiting list has increased to 709K patients, showing rising healthcare demand.
- High demand specialties: Paediatric Orthopaedic and Paediatric Urology show the highest waiting times.
- Outpatient cases dominate: Most patients fall under the Outpatient category, contributing heavily to the waiting list.
- Waiting time distribution: The majority of patients are waiting within the 0-6 month time band.
- Age related patterns: Some specialties show longer waiting periods for older patient groups.
- Prioritize high demand specialties: Allocate additional healthcare resources to specialties with the highest waiting lists.
- Improve outpatient capacity: Expanding outpatient services could significantly reduce waiting list pressure.
- Use data-driven planning: Healthcare administrators should regularly monitor waiting list trends to proactively manage service demand.
- Optimize scheduling: Better scheduling and operational planning can reduce bottlenecks and improve patient service efficiency.
Power BI | Power Query | DAX | Data Modeling | Data Visualization
This dashboard demonstrates how data-driven healthcare analytics can:
- Reduce patient waiting times
- Improve healthcare resource allocation
- Identify specialty demand trends
- Support better healthcare decision making