Faculty Bibliography

PURPOSE/OBJECTIVE:Increasing volumes and productivity expectations, along with practice type consolidation, may be impacting trainees' roles in the work effort of radiologists involved in education. We assessed temporal shifts in trainee participation in radiologists' workload nationally. METHODS:All US radiologists interpreting noninvasive diagnostic imaging for Medicare fee-for-service beneficiaries were identified from annual 5% Research Identifiable Files from 2008 to 2020 (n = 35,595). Teaching radiologists were defined as those billing services using Medicare's GC modifier, indicating trainee supervision. Billed work relative value units were used to determine the percentage of teaching radiologists' total workload with trainee participation. Mean trainee participation in workload was calculated for teaching radiologists overall and stratified by radiologist and practice characteristics determined using National Downloadable Files. RESULTS:The percentage of radiologists involved in teaching increased from 13.6% (2008) to 20.4% (2020). Among teaching radiologists, mean total workload increased 7% from 2008 to 2019 and decreased in 2020 to 2% below 2008's level; mean teaching workload decreased 19% from 2008 to 2019 and decreased in 2020 to 31% below 2008's level. Mean trainee participation in teaching radiologists' total workload decreased from 35.3% (2008) to 26.3% (2019) and 24.5% (2020). Teaching radiologists showed decreased mean trainee participation when stratified by gender, experience, subspecialty, geography, practice type, and practice size. CONCLUSIONS:The percentage of US radiologists involved in resident teaching has increased, likely reflecting academic practice expansion and academic-community practice consolidation. However, a declining percentage of teaching radiologists' total workload involves trainees; this dispersion effect could have implications for education quality.

OBJECTIVE:To build the Neiman Imaging Comorbidity Index (NICI), based on variables available in claims datasets, which provides good discrimination of an individual's chance of receiving advanced imaging (CT, MR, PET), and thus, utility as a control variable in research. METHODS:This retrospective study used national commercial claims data from Optum's deidentified Clinformatics Data Mart database from the period January 1, 2018 to December 31, 2019. Individuals with continuous enrollment during this 2-year study period were included. Lasso (least absolute shrinkage and selection operator) regression was used to predict the chance of receiving advanced imaging in 2019 based on the presence of comorbidities in 2018. A numerical index was created in a development cohort (70% of the total dataset) using weights assigned to each comorbidity, based on regression β coefficients. Internal validation of assigned scores was performed in the remaining 30% of claims, with comparison to the commonly used Charlson Comorbidity Index. RESULTS:The final sample (development and validation cohorts) included 10,532,734 beneficiaries, of whom 2,116,348 (20.1%) received advanced imaging. After model development, the NICI included nine comorbidities. In the internal validation set, the NICI achieved good discrimination of receipt of advanced imaging with a C statistic of 0.709 (95% confidence interval [CI] 0.708-0.709), which predicted advanced imaging better than the CCI (C 0.692, 95% CI 0.691-0.692). Controlling for age and sex yielded better discrimination (C 0.748, 95% CI 0.748-0.749). DISCUSSION/CONCLUSIONS:The NICI is an easily calculated measure of comorbidity burden that can be used to adjust for patients' chances of receiving advanced imaging. Future work should explore external validation of the NICI.