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Hospital Analytics Dashboard
Patient Flow Analysis

Patient Wait List Analytics – Dashboard for Healthcare Insights

Long patient wait times are a critical challenge for hospitals, often leading to delays in treatment and reduced satisfaction. This project addressed that challenge by developing a Power BI dashboard powered by hospital SQL Server data. After cleaning and modeling the data in Power Query, the solution transformed raw records into clear insights on wait time trends, specialty delays, and patient demographics. With interactive slicers, monthly trend lines, and distribution visuals, the dashboard equipped healthcare teams with the tools to identify bottlenecks and streamline patient flow.

Project Highlights

  • Data Ingestion and Handling: Extracted patient wait list data from SQL Server managed by the hospital, cleaned and validated it in Power Query for accurate analysis.
  • Interactive Dashboard Design: Built a Power BI dashboard with dynamic slicers, monthly trend lines, and wait time distribution charts segmented by age groups and duration.
  • Specialty Performance Tracking: Created visuals highlighting top delayed specialties and case type splits, enabling targeted service improvements.
  • User-Centric Reporting: Delivered an intuitive interface that allowed healthcare managers to explore trends and drill down into critical wait time metrics.

Business Impact

  • Improved Patient Flow: Highlighted bottlenecks delaying appointments, helping hospital administrators take corrective actions.
  • Data-Driven Decisions: Equipped healthcare teams with actionable insights to prioritize resources where most needed.
  • Enhanced Patient Experience: Reduced average wait times by addressing high-delay areas, improving service delivery and satisfaction.

Tools & Technologies

  • SQL Server: For data ingestion
  • Power Query: For data cleaning and validation
  • Power BI: For interactive visualization and reporting

Project Information

  • Client: CareFirst Hospital
  • Portfolio: Healthcare Analytics
  • Service: Data Engineering
  • Category: Healthcare
  • Date: 19 Apr 2023