News | Radiology Business | July 06, 2017

Claims System Could Lead to Private Practice Radiology Subspecialty Quality Metrics

New study using Medicare claims data identifies radiologist subspecialties more accurately than current system

Claims System Could Lead to Private Practice Radiology Subspecialty Quality Metrics

July 6, 2017 — A new Harvey L. Neiman Health Policy Institute study shows that a claims-based system used to subspecialty classify academic radiologists accurately identifies self-designated subspecialties for the approximately half of included private practice radiologists who have a subspecialized practice. The study is published online in the Journal of the American College of Radiology (JACR).

“Medicare is increasingly looking to develop specialty- and subspecialty-specific quality metrics that are as relevant and meaningful as possible to individual physicians, reflecting the diversity of physician practice,” noted lead author Andrew Rosenkrantz, M.D., MPA, an associate professor of radiology at NYU Langone Medical Center and a Neiman Institute affiliate research fellow. “There is currently no objective and reproducible method for determining radiologists’ subspecialties.”

Rosenkrantz and his colleagues looked at the websites of the 100 largest U.S. radiology private practices to identify 1,271 radiologists self-identified with a single subspecialty. Concordance of existing Medicare radiology subspecialty provider codes was first assessed. Next, using an academic practice piloted classification approach based on the Neiman Imaging Types of Service (NITOS) coding platform, the percentage of subspecialty work relative value units (wRVUs) from Medicare claims data were used to assign each radiologist a unique subspecialty.

The researchers found that the NITOS-based system mapped a median 51.9 percent of private practice radiologists’ wRVUs to self-identified subspecialties. The 50 percent NITOS-based wRVU threshold previously established for academic radiologists correctly assigned subspecialties to 48.8 percent of private practice radiologists, but incorrectly categorized 2.9 percent. Practice patterns of the remaining 48.3 percent were sufficiently varied such that no single subspecialty assignment was possible.

“Unambiguously classifying the subspecialties of private practice radiologists has presented a historic challenge given varied practice patterns and the fact that many subspecialists spend a considerable fraction of their time outside of their primary practice areas,” said Richard Duszak, M.D., FACR, professor and vice chair for health policy and practice in the department of radiology and imaging sciences at Emory University and senior affiliate research fellow at the Neiman Institute. “Existing CMS [Centers for Medicare and Medicaid Services] provider subspecialty codes match poorly with private practice radiologists’ self-identified subspecialties and perform notably worse in such identification than in the academic practice setting. As new payment models increasingly focus on specialty- and subspecialty-specific performance measures, claims-based identification methodologies should be further explored to ensure that radiologists are scored and paid using the most appropriate practice-relevant metrics.”

“Our work validates the ability of a claims-based system for determining radiologists’ subspecialty in the private practice setting, with an error rate of less than 5 percent. This classification system could be used for such quality-based payment systems, as well as to enable future research studies characterizing the national radiologist workforce,” added Rosenkrantz.

For more information: www.neimanhpi.org

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