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

PURPOSE: 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. METHODS: 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. RESULTS: 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). CONCLUSION: 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.

Imaging Informatics Fellowship Curriculum: Building Consensus on the Most Critical Topics and the Future of the Informatics Fellowship.

The existing fellowship imaging informatics curriculum, established in 2004, has not undergone formal revision since its inception and inaccurately reflects present-day radiology infrastructure. It insufficiently equips trainees for today's informatics challenges as current practices require an understanding of advanced informatics processes and more complex system integration. We sought to address this issue by surveying imaging informatics fellowship program directors across the country to determine the components and cutline for essential topics in a standardized imaging informatics curriculum, the consensus on essential versus supplementary knowledge, and the factors individual programs may use to determine if a newly developed topic is an essential topic. We further identified typical program structural elements and sought fellowship director consensus on offering official graduate trainee certification to imaging informatics fellows. Here, we aim to provide an imaging informatics fellowship director consensus on topics considered essential while still providing a framework for informatics fellowship programs to customize their individual curricula.

Balancing the Scales: An Analysis of Social Determinants of Health, Radiology Report Acuity, and Radiology Staffing Models in an Academic Health System.

PURPOSE: Social determinants of health, including race and insurance status, contribute to patient outcomes. In academic health systems, care is provided by a mix of trainees and faculty members. The optimal staffing ratio of trainees to faculty members (T/F) in radiology is unknown but may be related to the complexity of patients requiring care. Hospital characteristics, patient demographics, and radiology report findings may serve as markers of risk for poor outcomes because of patient complexity. METHODS: Descriptive characteristics of each hospital in an urban five-hospital academic health system, including payer distribution and race, were collected. Radiology department T/F ratios were calculated. A natural language processing model was used to classify multimodal report findings into nonacute, acute, and critical, with report acuity calculated as the fraction of acute and critical findings. Patient race, payer type, T/F ratio, and report acuity score for hospital 1, a safety net hospital, were compared with these factors for hospitals 2 to 5. RESULTS: The fraction of patients at hospital 1 who are Black (79%) and have Medicaid insurance (28%) is significantly higher than at hospitals 2 to 5 (P < .0001), with the exception of hospital 3 (80.1% black). The T/F ratio of 1.37 at hospital 1 as well as its report acuity (28.9%) were significantly higher (P < .0001 for both). CONCLUSIONS: T/F ratio and report acuity are highest at hospital 1, which serves the most at-risk patient population. This suggests a potential overreliance on trainees at a site whose patients may require the greatest expertise to optimize care.