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Editor's Notebook: January 2025

Rosenkrantz, Andrew B
PMID: 39878912
ISSN: 1546-3141
CID: 5780942

Evolving Trainee Participation in Radiologists' Workload: A National Medicare-Focused Analysis From 2008 to 2020

Burns, Judah; Chung, YoonKyung; Rula, Elizabeth Y; Duszak, Richard; Rosenkrantz, Andrew B
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.
PMID: 39453332
ISSN: 1558-349x
CID: 5740312

Editor's Notebook: December 2024 [Editorial]

Rosenkrantz, Andrew B
PMID: 39723950
ISSN: 1546-3141
CID: 5767662

External Validation of the Neiman Imaging Comorbidity Index in Medicare, Medicaid, and Private Payer Claims Data

Pelzl, Casey E; Drake, Alexandra; Rosenkrantz, Andrew B; Rula, Elizabeth Y; Christensen, Eric W
OBJECTIVE:The Neiman Imaging Comorbidity Index (NICI) was developed and validated in a claims dataset encompassing >10 million privately insured beneficiaries, in which it outperformed the commonly used Charlson Comorbidity Index (CCI) in predicting advanced imaging use. This external validation assessed the broader generalizability of NICI for predicting receipt of advanced imaging in nationally representative populations, including patients insured by Medicare, Medicaid, and private payers. METHODS:All 2018 to 2019 patient-level claims from the CMS Medicare 5% Research Identifiable File, CMS Medicaid 100% Research Identifiable File, and private insurance (commercial and Medicare Advantage) claims from Inovalon Insights, LLC, were included. Using 2018 comorbidity data, beneficiaries were assigned CCI and NICI. Area under the receiver operator characteristic curves (AUCs) measured index performance predicting advanced imaging in 2019. AUCs for NICI and CCI were compared overall, across age groups, and after adjusting for age and sex. RESULTS:A total of 108,846,549 beneficiaries were included across Medicare (n = 2,536,403), Medicaid (n = 49,685,052), and private insurance (n = 56,625,094) datasets. NICI outperformed CCI in Medicare (AUC: 0.7709, 95 confidence interval [CI]: 0.7702-0.7716 versus AUC: 0.7503, 95% CI: 0.7496-0.7510; P < .001), Medicaid (AUC: 0.6876, 95% CI: 0.6874-0.6878 versus AUC: 0.6798 95% CI: 0.6796-0.6800]; P < .001), and private insurance data (AUC: 0.6658, 95% CI: 0.6656-0.6660 versus AUC: 0.6479, 95% CI: 0.6477-0.6481; P < .001). NICI outperformed CCI in adjusted models and in nearly all age strata across the three cohorts. DISCUSSION/CONCLUSIONS:The NICI outperformed CCI in predicting advanced imaging in populations insured by numerous different payers. Validation data support NICI as the preferred index to adjust for patient comorbidities when studying advanced imaging as an outcome, but further investigations are warranted.
PMID: 39708026
ISSN: 1558-349x
CID: 5765062

Editor's Notebook: October 2024

Rosenkrantz, Andrew B
PMID: 39475705
ISSN: 1546-3141
CID: 5747052

Editor's Notebook: August 2024 [Editorial]

Rosenkrantz, Andrew B
PMID: 39197108
ISSN: 1546-3141
CID: 5687432

Radiologist Workforce Attrition from 2019 to 2024: A National Medicare Analysis

Rosenkrantz, Andrew B; Cummings, Ryan W
PMID: 39041939
ISSN: 1527-1315
CID: 5701842

The Neiman Imaging Comorbidity Index: Development and Validation in a National Commercial Claims Database

Pelzl, Casey E; Rosenkrantz, Andrew B; Rula, Elizabeth Y; Christensen, Eric W
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.
PMID: 38276924
ISSN: 1558-349x
CID: 5625402

Editor's Notebook: May 2024

Rosenkrantz, Andrew B
PMID: 38810113
ISSN: 1546-3141
CID: 5663612

The Yellow Journal Goes Multimedia [Editorial]

Rosenkrantz, Andrew B
PMID: 38598355
ISSN: 1546-3141
CID: 5664712