Are disparities in emergency department imaging exacerbated during high-volume periods?


      • Gender, age, race, and insurance type are associated with disparities in imaging in the emergency department.
      • Disparities are seen in different radiology-related time segments as well as length of stay in the emergency department.
      • Disparities are more pronounced in peak hours (compared to non-peak hours) particularly in image acquisition times.



      Evaluate if disparities in the emergency department (ED) imaging timeline exist, and if disparities are altered during high volume periods which may stress resource availability.


      This retrospective study was conducted at a four-hospital healthcare system. All patients with at least one ED visit containing imaging from 1/1/2016 to 9/30/2020 were included. Peak hours were defined as ED encounters occurring between 5 pm and midnight, while all other ED encounters were non-peak hours. Patient-flow data points included ED length of stay (LOS), image acquisition time, and diagnostic image assessment time.


      321,786 total ED visits consisted of 102,560 during peak hours and 219,226 during non-peak hours. Black patients experienced longer image acquisition and image assessment times across both time periods (TR = 1.030; p < 0.001 and TR = 1.112; p < 0.001, respectively); Black patients also had increased length of stay compared to White patients, which was amplified during peak hours. Likewise, patients with primary payer insurance experienced significantly longer image acquisition and image assessment times in both periods (TR > 1.00; p < 0.05 for all). Females had longer image acquisition and image assessment time and the difference was more pronounced in image acquisition time during both peak and non-peak hours (TR = 1.146 and TR = 1.139 respectively with p < 0.001 for both).


      When measuring radiology time periods, patient flow throughout the ED was not uniform. There was unequal acceleration and deceleration of patient flow based on racial, gender, age, and insurance status. Segmentation of patient flow time periods may allow identification of causes of inequity such that disparities can be addressed with targeted actions.


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