Physician Specialty and Radiologist Characteristics Associated with Higher Medicare Patient Complexity

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Meaningfully measuring physician outcomes and resource utilization requires appropriate patient risk adjustment. We aimed to assess Medicare patient complexity by physician specialty and to further identify radiologist characteristics associated with higher patient complexity. The average beneficiary Hierarchical Condition Category (HCC) risk scores (Medicare's preferred measure of clinical complexity) were identified for all physicians using 2014 Medicare claims data. HCC scores were compared among physician specialties and further stratified for radiologists based on a range of characteristics. Univariable and multivariable analyses were performed. Of 549,194 physicians across 54 specialties, the mean HCC risk score was 1.62 ± 0.75. Of the 54 specialties, interventional radiology ranked 4th (2.60 ± 1.29), nuclear medicine ranked 16th (1.87 ± 0.45), and diagnostic radiology ranked 21st (1.75 ± 0.61). Among 31,175 radiologists, risk scores were higher (P < 0.001) for those with teaching (2.03 ± 0.74) vs nonteaching affiliations (1.72 ± 0.61), practice size ≥100 (1.94 ± 0.70) vs ≤9 (1.59 ± 0.79) members, urban (1.79 ± 0.69) vs rural (1.67 ± 0.59) practices, and subspecialized (1.85 ± 0.81) vs generalized (1.68 ± 0.42) practice patterns. Among noninterventional radiology subspecialties, patient complexity was highest for cardiothoracic (2.09 ± 0.57) and lowest for breast (1.08 ± 0.32) imagers. At multivariable analysis, a teaching affiliation was the strongest independent predictor of patient complexity for both interventional (β = +0.23, P = 0.005) and noninterventional radiologists (β = +0.21, P < 0.001). Radiologists on average serve more clinically complex Medicare patients than most physicians nationally. However, patient complexity varies considerably among radiologists and is particularly high for those with teaching affiliations and interventional radiologists. With patient complexity increasingly recognized as a central predictor of clinical outcomes and resource utilization, ongoing insights into complexity measures may assist radiologists navigating emerging risk-based payment models.