Rosenkrantz Andrew B, Wang Wenyi, Bodapati Sudheshna, Hughes Danny R, Duszak Richard
Department of Radiology, NYU Langone Medical Center, New York, New York.
Harvey L. Neiman Health Policy Institute, Reston, Virginia.
J Am Coll Radiol. 2017 Nov;14(11):1419-1425. doi: 10.1016/j.jacr.2017.04.025. Epub 2017 Jun 30.
The aim of this study was to assess both existing Medicare provider code assignments and a new claims-based system for subspecialty classification of private practice radiologists.
Websites of the 100 largest US radiology private practices were used to identify 1,476 radiologists self-identified with a single subspecialty ([1] abdominal, [2] breast, [3] cardiothoracic, or [4] musculoskeletal imaging; [5] nuclear medicine; [6] interventional radiology; [7] neuroradiology). Concordance of existing Medicare radiology subspecialty provider codes (present only for nuclear medicine and interventional radiology) was first assessed. Next, using a classification approach based on Neiman Imaging Types of Service (NITOS) piloted among academic practices, the percentage of subspecialty work relative value units (wRVUs) from 2012 to 2014 Medicare claims were used to assign each radiologist a unique subspecialty.
Existing Medicare provider codes matched only 8.0% of nuclear medicine physicians and 10.7% of interventional radiologists to their self-reported subspecialties. The NITOS-based system mapped a median 51.9% of private practice radiologists' wRVUs to self-identified subspecialties (range, 23.3% [nuclear medicine] to 73.6% [neuroradiology]). The 50% NITOS-based wRVU threshold previously established for academic radiologists correctly assigned subspecialties to 48.8% of private practice radiologists but incorrectly categorized 2.9%. Practice patterns of the remaining 48.3% were sufficiently varied such that no single subspecialty assignment was possible.
Existing Medicare provider codes poorly mirror subspecialty radiologists' own practice website-designated subspecialties. Actual payer claims data permit far more granular and accurate subspecialty identification for many radiologists. As new payment models increasingly focus on subspecialty-specific performance measures, claims-based identification methodologies show promise for reproducibly and transparently matching radiologists to practice-relevant metrics.
本研究旨在评估医疗保险现有提供者代码分配情况以及一种基于索赔的新系统，用于对私人执业放射科医生进行亚专业分类。
利用美国最大的100家放射科私人执业机构的网站，确定1476名自我认定为单一亚专业的放射科医生（[1]腹部，[2]乳腺，[3]心胸，或[4]肌肉骨骼成像；[5]核医学；[6]介入放射学；[7]神经放射学）。首先评估医疗保险现有放射亚专业提供者代码（仅适用于核医学和介入放射学）的一致性。接下来，采用基于学术机构试行的内曼影像服务类型（NITOS）的分类方法，使用2012年至2014年医疗保险索赔中亚专业工作相对价值单位（wRVU）的百分比为每位放射科医生分配一个独特的亚专业。
医疗保险现有提供者代码仅将8.0%的核医学医生和10.7%的介入放射科医生与其自我报告的亚专业相匹配。基于NITOS的系统将私人执业放射科医生wRVU的中位数51.9%映射到自我认定的亚专业（范围为23.3%[核医学]至73.6%[神经放射学]）。先前为学术放射科医生设定的基于NITOS的50%wRVU阈值正确地将亚专业分配给了48.8%的私人执业放射科医生，但错误分类的占2.9%。其余48.3%的执业模式差异很大，无法进行单一亚专业分配。
医疗保险现有提供者代码很难反映亚专业放射科医生自身执业网站指定的亚专业。实际付款人索赔数据可为许多放射科医生提供更细致、准确的亚专业识别。随着新的支付模式越来越关注亚专业特定的绩效指标，基于索赔的识别方法有望将放射科医生与实践相关指标进行可重复且透明的匹配。
