Original Investigation| Volume 25, ISSUE 2, P219-225, February 2018

Physician Specialty and Radiologist Characteristics Associated with Higher Medicare Patient Complexity

Published:November 03, 2017DOI:

      Rationale and Objectives

      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.

      Materials and Methods

      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.

      Key Words

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        • 114th U.S. Congress
        H.R.2—Medicare access and CHIP Reauthorization Act of 2015.
        (Available at:)
        Date: 2015
        Date accessed: March 5, 2017
        • Centers for Medicare & Medicaid Services
        Medicare program; Merit-Based Incentive Payment System (MIPS) and Alternative Payment Model (APM) Incentive Under the Physician Fee Schedule, and Criteria for Physician-Focused Payment Models.
        (Available at:)
        • Federal Register
        Medicare Program; Revisions to Payment Policies Under the Physician Fee Schedule, DME Face-to-Face Encounters, Elimination of the Requirement for Termination of Non-Random Prepayment Complex Medical Review and Other Revisions to Part B for CY 2013.
        (No. 222. Vol. 77; 69317–69318; Available at:)
        • Rosenkrantz A.B.
        • Schoppe K.A.
        • Duszak Jr, R.
        Temporal and patient variations potentially impacting new payment models.
        J Am Coll Radiol. 2017; 14: 452-458
        • Lin P.J.
        • Maciejewski M.L.
        • Paul J.E.
        • et al.
        Risk adjustment for Medicare beneficiaries with Alzheimer's disease and related dementias.
        Am J Manag Care. 2010; 16: 191-198
        • Centers for Medicare & Medicaid Services
        Evaluation of the CMS-HCC risk adjustment model.
        (Available at:)
        • Centers for Medicare & Medicaid Services
        Payment standardization and risk adjustment for the Medicare physician feedback and value modifier programs.
        (Available at:)
        • Centers for Medicare & Medicaid Services
        Medicare fee-for-service provider utilization & payment data physician and other supplier public use file: a methodological overview.
        (Available at:)
        • Li P.
        • Kim M.M.
        • Doshi J.A.
        Comparison of the performance of the CMS Hierarchical Condition Category (CMS-HCC) risk adjuster with the Charlson and Elixhauser comorbidity measures in predicting mortality.
        BMC Health Serv Res. 2010; 10: 245
        • Missouri Census Data Center
        Metadata for dataset /pub/data/georef/zcta_master.
        (Available at:)
        • Centers for Medicare & Medicaid Services
        Phsyician compare.
        (Available at:)
        Date accessed: March 6, 2017
        • Accreditation Council for Graduate Medical Education (ACGME)
        Accredited programs and sponsoring institutions.
        (ACGME website; Available at:)
        • Vijayasarathi A.
        • Loehfelm T.
        • Duszak Jr, R.
        • et al.
        Journal club: radiologists' online identities: what patients find when they search radiologists by name.
        AJR Am J Roentgenol. 2016; 207: 952-958
        • Rosenkrantz A.B.
        • Wang W.
        • Hughes D.R.
        • et al.
        Academic radiologist subspecialty identification using a novel claims-based classification system.
        AJR Am J Roentgenol. 2017; 208: 1249-1255
        • Rosenkrantz A.B.
        • Wang W.
        • Bodapati S.
        • et al.
        Private practice radiologist subspecialty classification using Medicare claims.
        J Am Coll Radiol. 2017; (Jun 30; Epub ahead of print)
        • Neiman H.L.
        • Health Policy Institute
        Neiman imaging types of service.
        (Available at:)
        • OpenHeatMap
        (Available at:)
        Date accessed: March 6, 2017
        • Rosenkrantz A.B.
        • Nicola G.N.
        • Allen Jr, B.
        • et al.
        MACRA, alternative payment models, and the physician-focused payment model: implications for radiology.
        J Am Coll Radiol. 2017; 14: 744-751
        • Rosenkrantz A.B.
        • Hirsch J.A.
        • Allen Jr, B.
        • et al.
        Identifying radiology's place in the expanding landscape of episode payment models.
        J Am Coll Radiol. 2017; 14: 882-888
        • Newhouse J.P.
        • Wilensknagtay G.R.
        Paying for graduate medical education: the debate goes on.
        Health Aff (Millwood). 2001; 20: 136-147
        • Rich E.C.
        • Liebow M.
        • Srinivasan M.
        • et al.
        Medicare financing of graduate medical education.
        J Gen Intern Med. 2002; 17: 283-292
        • Rosenkrantz A.B.
        • Wang W.
        • Duszak Jr, R.
        The ongoing gap in availability of imaging services at teaching versus nonteaching hospitals.
        Acad Radiol. 2016; 23: 1057-1063
        • Fleishon H.B.
        • Itri J.N.
        • Boland G.W.
        • et al.
        Academic medical centers and community hospitals integration: trends and strategies.
        J Am Coll Radiol. 2017; 14: 45-51
        • Bai G.
        • Anderson G.F.
        Variation in the ratio of physician charges to Medicare payments by specialty and region.
        JAMA. 2017; 317: 315-318
        • Centers for Medicare & Medicaid Services
        Proposed changes to the CMS-HCC risk adjustment model for payment year 2017.
        (Available at:)
        • Sorace J.
        • Wong H.H.
        • Worrall C.
        • et al.
        The complexity of disease combinations in the Medicare population.
        Popul Health Manag. 2011; 14: 161-166
        • Chang E.
        • Ruder T.
        • Setodji C.
        • et al.
        Differences in nursing home quality between Medicare advantage and traditional Medicare patients.
        J Am Med Dir Assoc. 2016; 17 (960.e969–960.e914)
        • Kronick R.
        • Welch W.P.
        Measuring coding intensity in the Medicare advantage program.
        Medicare Medicaid Res Rev. 2014; 4