Data analysis at the granular level can identify not only what is driving overutilization, but also what can be done to help reduce clinical variation.
In one case, a deep dive into CT utilization revealed that the easy answer wasn’t the correct one.
During our work with clients, there is no better feeling than providing them with an aha! moment. With our data analysis, broad, high-level information is only the beginning. By crunching data from a range of sources, we uncover underlying causes and meaningful findings that truly can make a difference.
For example, use of CT scans has been trending upward in the United States, especially in emergency departments. The volume of imaging in EDs is increasing among the Medicare fee-for-service population, and per ED visit overall. This trend suggests overutilization, which increases health care costs overall and risks patient exposure to excess radiation levels.
Our Emergency Medicine Performance Analytics application can identify not only what is driving overutilization, but also what can be done to help reduce clinical variation.
Real-World Application
To reduce unnecessary radiation exposure, a client of ours wanted to investigate their sites’ CT utilization patterns. The client has three ED locations, one of which had particularly high CT use.
To help solve this problem, d2i presented data for the three facilities in two file formats:
- The first file analyzed the total number of ED patients for whom a CT was ordered, with the percent of patients seen by each doctor.
- The second file was a bar graph depicting the number of CT scans ordered by each doctor per 100 patients.
We uncovered several conclusions that clarified what was actually happening: Although ED No. 3 had the highest utilization rate, it wasn’t just that facility’s doctors who were ordering more CTs. Even doctors who usually worked at another site ordered more CTs when working at ED No. 3. The overutilization was not provider-dependent, but instead was location-dependent. A closer look revealed that ED No. 3’s patient population was typically older, and may have had a higher incidence of chronic disease.
Without a deeper examination of data, the client may have assumed overutilization was related to a group of providers and stopped there. This could have led to a blame game and no real improvement. Instead, clearly determining the source of the problem provided insight on how to correct it. This is the type of data-driven storytelling that makes analytics a worthwhile (and smart!) quality improvement investment.
Let us show you how having contextually relevant, actionable data can help you improve your operations. Contact our business intelligence experts to learn more about analyzing data on a granular level, providing your organization with meaningful insights using a storyboard approach.