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Analytics: Case 2

At Admin EM, we believe that data analytics provide a critical tool in understanding the impediments to patient flow. This case is one example of how understanding your data can lead to better decision making.
This emergency department is seeking to expand. In deciding how to best utilize the limited resources of the hospital, members from several clinical teams were gathered to determine what the “critical needs” of the department included, and how to prioritize them. The team came up with the following list: 

  • Install a second CT scanner: CT utilization is increasing. Average patient age and acuity has increased, resulting in a greater demand for CT imaging. In addition, the hospital has obtained trauma and stroke certification, further incorporating CT imaging into standard protocols. 
  • Purchase a second portable Xray machine: The department currently owns one portable X-ray machine and a built in X-ray machine inside a radiology room. 
  • Migrate to point-of-care lab analysis devices to run chemistry, hemogram, cardiac enzymes, and urinalysis and pregnancy testing.
  • Add ED phlebotomists to the staff. With the increased complexity of patients, there has been an increased in IV catheter placement delays. Dedicated phlebotomists could provide support to nursing. 
  • Hire a nighttime ultrasound technician to allow 24 hour ultrasound access. Current staffing includes 8a-5p staffing 7 days a week. 

Costs for all suggestions are high and the emergency department leadership needed a method to assess the impact of each modality in order to determine which option held the greatest return on investment. A few visualizations were provided by Admin EM to help clarify the impact of available options: 

  • Turn Around Time: Collecting correct data on the time it takes to process each modality (US, Xray, CT) and lab was helpful. The graph below demonstrates the average time for each modality by day of week. There is some variation expected based on daily volume. This is most notable in the CT turn around time.
  • Tests per 100: Obtaining the real number of tests ordered for each 100 patients allowed for a true comparison across modalities. Labs exceed 100, which means that multiple labs are ordered per patient making this category the most utilized. 
  • Impact factor: This percentage is generated from the number of studies and expected turn around time. It reflects the percentage of available daily process time that is consumed by each modality.  Given the frequency of lab testing and the amount of time each lab requires, lab processing consumed the highest portion. 


After careful consideration of the data and visualizations presented, the area of greatest impact was determined to be the lab. More patients receive lab testing than any other test. In addition, given the amount of time it takes to process lab specimens, more time was spent waiting for lab results than any other modality. In order to improve patient flow, this category was the most likely to give the largest return on investment. From the assembled suggestions, only one impacted lab processing, the suggestion to migrate to point of care testing. The answer became clear. 

Could your facility benefit from better analytics ? Contact us to learn more about a free analysis and visualization.

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