Recent radiologist compensation and clinical productivity trends have not been well characterized, especially across academic vs. non-academic practice settings. To assess recent trends in in financial compensation and clinical productivity between academic and non-academic settings in diagnostic (DR) and interventional radiology (IR). We studied deidentified data from the Medical Group Management Association (MGMA) for both DR and IR radiologists in academic and non-academic practices from 2014 to 2023. Median, 25th and 75th percentile, and mean values were analyzed for compensation, collections and work relative-value-units (wRVUs). Compensation and productivity data were compared by radiology subspecialty (DR vs IR), practice type (academic vs non-academic provider), geographical region of the US, and practice size. Trends in absolute changes were analyzed with linear regression. The MGMA Survey data for 2023 included responses for 3769 radiologists (2883 in DR and 886 in IR). In 2023, non-academic radiologists had greater total median compensation than academic faculty in both DR (by 27%) and IR (by 32%). From 2014 to 2023, median compensation increased faster for academic DRs (3.2% annually) than for non-academic DRs (1.9% annually). In 2023, DRs produced greater median wRVUs than IRs (by 53 % for non-academic and 46 % for academic radiologists) with higher collections, but IRs had higher compensation (by 16% in non-academic and 10% in academic settings). Over the last decade, IR physician compensation increased by 3.9% and 3.4% annually for non-academic and academic IR physicians respectively while median work RVUs trended downward (by -1.5 % for non-academic and -2.4 % for academic physicians) with declining collections (by -4.4 % annually for non-academic and -2.1 % for academic physicians). Over the last decade, the salary gap between academic and non-academic radiologists has narrowed. Physician compensation has increased at a faster pace in IR, despite relatively lower clinical productivity and declining collections.
Read full abstract