Faculty Bibliography

The current fee-for-service system for health care reimbursement in the United Stated is argued to encourage fragmented care delivery and a lack of accountability that predisposes to insufficient focus on quality as well as unnecessary or duplicative resource utilization. Episode payment models (EPMs) seek to improve coordination by linking payments for all services related to a patient's condition or procedure, thereby improving quality and efficiency of care. The CMS Innovation Center has implemented a broadening array of EPMs. Early models with relevance to radiologists include Bundled Payment for Care Improvement (involving 48 possible clinical conditions), Comprehensive Care for Joint Replacement (involving knee and hip replacement), and the Oncology Care Model (involving chemotherapy). In July 2016, CMS expanded the range of EPMs through three new models with mandatory hospital participation addressing inpatient and 90-day postdischarge care for acute myocardial infarction, coronary artery bypass graft, and surgical hip and femur fracture treatment. Moreover, some of the EPMs include tracks that allow participating entities to qualify as an Advanced Alternative Payment Model under the Medicare Access and CHIP Reauthorization Act (MACRA), reaping the associated reporting and payment benefits. Even though none of the available EPMs are radiology specific, the models will nevertheless likely influence reimbursements for some radiologists. Thus, radiologists should partner with hospitals and other specialties in care coordination through these episode-based initiatives, thereby having opportunities to apply their imaging expertise to help lower spending while improving quality and overall levels of health.

PURPOSE: The aim of this study was to assess relationships between county-level variation in Medicare beneficiary imaging resource consumption and measures of population economic status. METHODS: The 2013 CMS Geographic Variation Public Use File was used to identify county-level per capita Medicare fee-for-service imaging utilization and nationally standardized costs to the Medicare program. The County Health Rankings public data set was used to identify county-level measures of population economic status. Regional variation was assessed, and multivariate regressions were performed. RESULTS: Imaging events per 1,000 Medicare beneficiaries varied 1.8-fold (range, 2,723-4,843) at the state level and 5.3-fold (range, 1,228-6,455) at the county level. Per capita nationally standardized imaging costs to Medicare varied 4.2-fold (range, $84-$353) at the state level and 14.1-fold (range, $33-$471) at the county level. Within individual states, county-level utilization varied on average 2.0-fold (range, 1.1- to 3.1-fold), and costs varied 2.8-fold (range, 1.1- to 6.4-fold). For both large urban populations and small rural states, Medicare imaging resource consumption was heterogeneously variable at the county level. Adjusting for county-level gender, ethnicity, rural status, and population density, countywide unemployment rates showed strong independent positive associations with Medicare imaging events (beta = 26.96) and costs (beta = 4.37), whereas uninsured rates showed strong independent positive associations with Medicare imaging costs (beta = 2.68). CONCLUSIONS: Medicare imaging utilization and costs both vary far more at the county than at the state level. Unfavorable measures of county-level population economic status in the non-Medicare population are independently associated with greater Medicare imaging resource consumption. Future efforts to optimize Medicare imaging use should consider the influence of local indigenous socioeconomic factors outside the scope of traditional beneficiary-focused policy initiatives.

OBJECTIVE: The objective of the present study is to assess the feasibility of a novel claims-based classification system for payer identification of academic radiologist subspecialties. MATERIALS AND METHODS: Using a categorization scheme based on the Neiman Imaging Types of Service (NITOS) system, we mapped the Medicare Part B services billed by all radiologists from 2012 to 2014, assigning them to the following subspecialty categories: abdominal imaging, breast imaging, cardiothoracic imaging, musculoskeletal imaging, nuclear medicine, interventional radiology, and neuroradiology. The percentage of subspecialty work relative value units (RVUs) to total billed work RVUs was calculated for each radiologist nationwide. For radiologists at the top 20 academic departments funded by the National Institutes of Health, those percentages were compared with subspecialties designated on faculty websites. NITOS-based subspecialty assignments were also compared with the only radiologist subspecialty classifications currently recognized by Medicare (i.e., nuclear medicine and interventional radiology). RESULTS: Of 1012 academic radiologists studied, the median percentage of Medicare-billed NITOS-based subspecialty work RVUs matching the subspecialty designated on radiologists' own websites ranged from 71.3% (for nuclear medicine) to 98.9% (for neuroradiology). A NITOS-based work RVU threshold of 50% correctly classified 89.8% of radiologists (5.9% were not mapped to any subspecialty; subspecialty error rate, 4.2%). In contrast, existing Medicare provider codes identified only 46.7% of nuclear medicine physicians and 39.4% of interventional radiologists. CONCLUSION: Using a framework based on a recently established imaging health services research tool that maps service codes based on imaging modality and body region, Medicare claims data can be used to consistently identify academic radiologists by subspecialty in a manner not possible with the use of existing Medicare physician specialty identifiers. This method may facilitate more appropriate performance metrics for subspecialty academic physicians under emerging value-based payment models.

OBJECTIVE: To perform a survey-based assessment of current contrast reaction training in US diagnostic radiology residency programs. METHODS: An electronic survey was distributed to radiology residency program directors from 9/2015-11/2015. RESULTS: 25.7% of programs responded. 95.7% of those who responded provide contrast reaction management training. 89.4% provide didactic lectures (occurring yearly in 71.4%). 37.8% provide hands-on simulation training (occurring yearly in 82.3%; attended by both faculty and trainees in 52.9%). CONCLUSION: Wide variability in contrast reaction education in US diagnostic radiology residency programs reveals an opportunity to develop and implement a national curriculum.

RATIONALE AND OBJECTIVES: Emerging alternative metrics leverage social media and other online platforms to provide immediate measures of biomedical articles' reach among diverse public audiences. We aimed to compare traditional citation and alternative impact metrics for articles in popular general radiology journals. MATERIALS AND METHODS: All 892 original investigations published in 2013 issues of Academic Radiology, American Journal of Roentgenology, Journal of the American College of Radiology, and Radiology were included. Each article's content was classified as imaging vs nonimaging. Traditional journal citations to articles were obtained from Web of Science. Each article's Altmetric Attention Score (Altmetric), representing weighted mentions across a variety of online platforms, was obtained from Altmetric.com. Statistical assessment included the McNemar test, the Mann-Whitney test, and the Pearson correlation. RESULTS: Mean and median traditional citation counts were 10.7 +/- 15.4 and 5 vs 3.3 +/- 13.3 and 0 for Altmetric. Among all articles, 96.4% had >/=1 traditional citation vs 41.8% for Altmetric (P < 0.001). Online platforms for which at least 5% of the articles were represented included Mendeley (42.8%), Twitter (34.2%), Facebook (10.7%), and news outlets (8.4%). Citations and Altmetric were weakly correlated (r = 0.20), with only a 25.0% overlap in terms of articles within their top 10th percentiles. Traditional citations were higher for articles with imaging vs nonimaging content (11.5 +/- 16.2 vs 6.9 +/- 9.8, P < 0.001), but Altmetric scores were higher in articles with nonimaging content (5.1 +/- 11.1 vs 2.8 +/- 13.7, P = 0.006). CONCLUSIONS: Although overall online attention to radiology journal content was low, alternative metrics exhibited unique trends, particularly for nonclinical articles, and may provide a complementary measure of radiology research impact compared to traditional citation counts.