Understanding how a provider’s utilization rates compare to their peers is crucial for identifying areas of strength and potential improvement. This analysis often involves comparing two sets of numbers: the provider’s actual utilization rate and the peer group’s average utilization rate. A key challenge lies in effectively visualizing this data for clear insights. This article explores a solution using Power BI to compare these two numbers visually for each billing code.
Deconstructing the Data Challenge
The core issue stems from the structure of the data. Each provider has individual records with their total visits and visits broken down by specific billing codes (e.g., 99212, 99213, 99214, 99215). Additionally, the dataset includes pre-calculated peer utilization rates for each code. Standard line charts in Power BI default to plotting values on the y-axis and categories on the x-axis, which doesn’t facilitate a direct comparison between a provider’s and their peer’s utilization rate for each individual code.
The desired visualization requires a different approach: plotting the billing codes on the x-axis and the utilization rates (both provider and peer) on the y-axis, enabling a side-by-side comparison for each code.
A Solution for Comparing Two Numbers: Visualizing with Power BI
The solution hinges on restructuring the data for Power BI to interpret it correctly. Instead of separate columns for provider and peer utilization rates for each code, the data needs to be transformed. A more effective approach involves creating a new table with the following structure:
| Provider NPI | Code | Utilization Type | Rate |
|---|---|---|---|
| 1234567890 | 99212 | Provider | 0.25 |
| 1234567890 | 99212 | Peer | 0.30 |
| 1234567890 | 99213 | Provider | 0.40 |
| 1234567890 | 99213 | Peer | 0.35 |
| … | … | … | … |
This table unpivots the original data, creating separate rows for provider and peer utilization rates for each code. This “Utilization Type” field now allows Power BI to group and display the data as desired. With this restructured data, a clustered column chart in Power BI can effectively visualize the comparison:
Conclusion: Transforming Data for Effective Comparison
Visualizing the comparison between two sets of numbers, such as provider and peer utilization rates, requires careful consideration of data structure. By transforming the data to a format that clearly delineates the categories and values to be compared, Power BI can generate insightful visualizations. This method provides a clear, concise way to analyze performance and identify areas for improvement. A well-structured data model is paramount for enabling effective visual comparisons in business intelligence tools. Using the right chart and the correct data formatting provides a robust visual answer, offering an immediate understanding of the relationship between a provider’s performance and their peer group benchmark.
