Faculty Bibliography

PURPOSE/OBJECTIVE:The aim of this study was to assess radiologists' performance on Medicare quality measures and identify physician characteristics potentially influencing such scores. METHODS:Medicare quality scores reported by US radiologists in 2015 were obtained from CMS. Associations were explored with publicly available physician characteristic data. RESULTS:Overall, 15,045 radiologists reported 40,427 Medicare quality scores encompassing 25 claims measures, 18 registry measures, and 2 qualified clinical data registry (QCDR) measures. Claims measures included reporting fluoroscopic times (n = 10,152; mean score, 80.3 ± 27.6), carotid ultrasound stenosis (n = 8,940; mean score, 86.8 ± 20.6), inappropriate mammography use of "probably benign" (n = 8,083; mean score, 0.4 ± 3.3), mammography reminders (n = 7,229; mean score, 86.6 ± 29.0), bone scintigraphy correlation (n = 2,712; mean score, 76.0 ± 27.0), and line-related infection prevention (n = 2,226; mean score, 83.3 ± 27.4). Registry measures were reported by ≤17 radiologists. The two QCDR measures were dose index registry participation (n = 246; mean score, 99.5 ± 1.4) and mammography recall rate (n = 77; mean score, 9.0 ± 5.6). Higher scores were observed for radiologists in larger practices (strongest independent predictor), in subspecialized practices, in academic practices, in the South and West, and with fewer years in practice. The fluoroscopic exposure times measure had the best performance scores by musculoskeletal and interventional radiologists, carotid Doppler measure by abdominal radiologists, mammography measures by breast radiologists, bone scintigraphy measure by musculoskeletal and nuclear medicine radiologists, and line infection measure by interventionalists. The dose registry participation QCDR measure had near perfect performance across generalists and subspecialists. CONCLUSIONS:Current Medicare performance metrics favor radiologists in larger practices and subspecialized radiologists, possibly reflecting support infrastructures and the narrow focus of most metrics, respectively. These findings may assist targeted data-driven reporting by radiologists and guide efforts to refine existing and develop new metrics.

PURPOSE/OBJECTIVE:The aims of this study were to compare the number of unique Medicare fee-for-service beneficiaries served by radiologists and other physicians and to identify characteristics of radiologists serving the most number of unique patients. METHODS:Medicare Physician and Other Supplier Public Use Files were used to identify all physicians who provided services to Medicare fee-for-service beneficiaries for the entirety of 2013. The average number of unique beneficiaries served was computed per specialty. The number of unique beneficiaries served was further stratified among radiologists in terms of physician and practice characteristics. RESULTS:Among 56 unique physician specialties, diagnostic radiologists on average served the most unique beneficiaries (3,150 ± 2,344). Among radiologists, the number of unique beneficiaries varied in association with numerous characteristics and was larger for male (3,214) versus female (2,521) radiologists, rural (3,551) versus urban (3,092) radiologists, nonacademic (3,427) versus academic (1,932) radiologists, generalist (3,866) versus subspecialist (1,981) radiologists, and radiologists in the South (3,716) versus other geographic regions (range, 2,432-3,217). The number of unique beneficiaries served increased significantly with smaller group practice size (2,218 for ≥100 group members versus 3,669 for ≤9 members). Among subspecialists, the number of unique beneficiaries was largest for breast imagers (2,594). CONCLUSIONS:The large number of unique beneficiaries served by radiologists highlights their important role in orchestrating patient care and their immense opportunities to expand the face of the specialty. An understanding of which radiologists serve the largest numbers of unique patients may help radiology practices target patient engagement and other Imaging 3.0™ efforts.

PURPOSE/OBJECTIVE:To study opioid prescribing behavior of US interventional radiologists (IRs). METHODS:Using Medicare Physician and Other Supplier Public Use File claims, we identified 2,133 radiologists whose practice in 2015 comprised predominantly interventional radiology. Cross-linking the Medicare Part D Prescriber File, their opioid prescription writing behavior was characterized. RESULTS:Most (52.2%) IRs wrote 10 or fewer prescriptions total for Medicare beneficiaries. Of the 47.8% who wrote >10 prescriptions, 87.4% prescribed an opioid, most commonly hydrocodone with acetaminophen, at least once (71.3%, 1-10 opioid prescriptions; 27.4%, 11-100; 1.3%, ≥101). Overall, 23.0% of all prescriptions by those IRs writing >10 were for opioids, with an average 8.0-day prescription. Average opioid prescriptions per IR were significantly (P ≤ .015) independently associated with their providing clinical evaluation and management (E&M) services (9.7 opioid prescriptions per IR with demonstrable E&M encounters versus 2.2 if not), practice size (12.6 for practices with ≤ 10 members versus 3.7-4.8 for larger groups), and geography (8.3 in the South versus 3.6-4.0 elsewhere). Rates were highest in Georgia (39.5) and lowest in Delaware (2.0). Higher opioid prescribing rates showed additional univariable associations with more years in practice and nonacademic practices. CONCLUSION/CONCLUSIONS:Most IRs write few, if any, prescriptions for Medicare beneficiaries. Of those who do, the large majority writes for opioids, at rates higher than national physician benchmarks. IRs' opioid prescribing varies significantly based on physician and practice characteristics and particularly whether the IR provides clinical E&M services. In light of the nation's opioid epidemic, these observations may guide education, practice improvement, and policy efforts to optimize opioid prescribing.

OBJECTIVE:The purpose of this study is to assess the utility of texture analysis of multiple MRI sequences for the differentiation of uterine leiomyomas and leiomyosarcomas. MATERIALS AND METHODS/METHODS:Seventeen leiomyosarcomas and 51 leiomyomas undergoing MRI before resection were included. Whole-lesion volumes of interest were placed on T2-weighted images, contrast-enhanced T1-weighted images, and apparent diffusion coefficient (ADC) maps. The diagnostic performance of histogram metrics was assessed. RESULTS:achieved sensitivity of 82.4% and specificity of 74.5%. CONCLUSION/CONCLUSIONS:For whole-lesion histogram metrics obtained on various MRI sequences, T2-weighted images provided the highest, and ADC maps the lowest, performance for differentiating uterine leiomyomas and leiomyosarcomas. Metrics reflecting percentiles from the bottom half of the histogram distribution outperformed the standard mean. Models combining the T2-weighted imaging whole-lesion metrics and patient age achieved particularly high diagnostic performance. Although these findings require validation in larger studies, they have implications for facilitating improved treatment selection for these two entities.

RATIONALE AND OBJECTIVES/OBJECTIVE:To assess associations between screening mammography utilization and Medicare beneficiaries' relationships with, and impressions of, their primary care physicians. MATERIALS AND METHODS/METHODS:Using the Medicare Current Beneficiary Survey Access to Care Public Use File, we retrospectively studied responses from a national random cross section of Medicare beneficiaries surveyed in 2013 regarding perceptions of their primary care physicians and their screening mammography utilization. Statistical analysis accounted for subject weighting factors to estimate national screening utilization. RESULTS:Among 7492 female Medicare beneficiaries, 62.0% (95% confidence interval 59.8%-64.2%) underwent screening mammography. Utilization was higher for beneficiaries having (vs. not) a regular medical practice or clinic (63.2% vs. 34.6%) and a usual physician (63.8% vs. 50.3%). Utilization was higher for beneficiaries very satisfied (vs. very dissatisfied) with the overall quality of care they received (66.0% vs. 35.8%), their ease of getting to a doctor (67.7% vs. 43.2%), and their physician's concerns for their health (65.7% vs. 53.4%), as well as for beneficiaries strongly agreeing (vs. strongly disagreeing) that their physician is competent (66.0% vs. 54.1%), understands what is wrong (66.3% vs. 47.1%), answers all questions (67.0% vs. 46.7%), and fosters confidence (66.0% vs. 50.6%). Independent predictors of screening mammography utilization (P < .05) were satisfaction with quality of care, having a regular practice or clinic, and satisfaction with ease of getting to their physician. CONCLUSIONS:Screening mammography utilization is higher among Medicare beneficiaries with established primary physician relationships, particularly when those relationships are favorable. To optimize screening mammography utilization, breast imagers are encouraged to support initiatives to enhance high-quality primary care relationships.

PURPOSE/OBJECTIVE:To explore associations between county-level measures of radiologist supply and subspecialization and county structural and health-related characteristics. METHODS:Medicare Physician and Other Supplier Public Use Files were used to subspecialty characterize 32,844 radiologists participating in Medicare between 2012 and 2014. Measures of radiologist supply and subspecialization were computed for 3,143 US counties. Additional county characteristics were identified using the 2014 County Health Rankings database. Mann-Whitney tests and Spearman correlations were performed. RESULTS:Counties with at least one (versus no) Medicare-participating radiologist had significantly (P < .001) larger populations (197,050 ± 457,056 versus 20,253 ± 23,689), lower rural percentages (39.5% ± 26.5% versus 74.6% ± 25.6%), higher household incomes ($47,608 ± $12,493 versus $42,510 ± $9,893), higher mammography screening rates (62.4% ± 7.0% versus 56.6% ± 15.3%), and lower premature deaths (7,581 ± 2,085 versus 7,784 ± 3,409 years of life lost). Counties' radiologists per 100,000 population and percent of subspecialized radiologists showed moderate positive correlations with counties' population (r = +0.505-+0.599) and moderate negative correlations with counties' rural percentage (r = -0.434 to -0.523). Radiologist supply and degree of subspecialization both showed concurrent positive or negative weak associations with counties' percent age 65+ (r = -0.256 to -0.271), percent Hispanic (r = +0.209-+0.234), and income (r = +0.230-+0.316). Radiologists per 100,000 population showed weak positive correlation with mammography screening (r = +0.214); percent of radiologists subspecialized showed weak negative correlation with premature death (r = -0.226). CONCLUSION/CONCLUSIONS:Geographic disparities in radiologist supply at the community level are compounded by superimposed variation in the degree of subspecialization of those radiologists. The potential impact of such access disparities on county-level health warrants further investigation.

PURPOSE/OBJECTIVE:The aim of this study was to characterize out-of-pocket patient costs for advanced imaging across the US private insurance marketplace. METHODS:Using the 2017 CMS Health Insurance Marketplace Benefits and Cost Sharing Public Use File, which details coverage policies for qualified health plans on federally facilitated marketplaces, measures of out-of-pocket costs for advanced imaging and other essential health benefits were analyzed for all 18,429 plans. RESULTS:Independent of deductibles, 48.0% of plans required coinsurance (percentage fees) for advanced imaging, 9.7% required copayments (flat fees), and 8.0% required both; 34.3% required neither. For out-of-network services, 91.5% required coinsurance, 0.1% copayments, and 1.0% both; only 7.4% required neither. In the presence of deductibles, patient coinsurance burdens for advanced imaging in and out of network were 27.7% and 47.7%, respectively, and average in- and out-of-network copayments were $319 and $630, respectively. In the presence of deductibles, patients' average coinsurance ranged from 10.0% to 40.9% in network and from 29.1% to 75.0% out of network by state; these tended to be higher in lower income states (r = -0.332). For no-deductible policies, patients' average out-of-network coinsurance burden for advanced imaging was 99.9%. Among assessed benefits, advanced imaging had the highest in-network and second highest out-of-network copayments. CONCLUSIONS:In the US private insurance marketplace, patients very commonly pay coinsurance when undergoing advanced imaging, both in and out of network. But out-of-network services usually involve drastically higher patient financial responsibilities (potentially 100% of examination cost). To more effectively engage patients in shared decision making and mitigate the hardships of surprise balance billing, radiologists should facilitate transparent communication of advanced imaging costs with patients.

OBJECTIVE:The purpose of this study is to identify the specialty characteristics of providers referring musculoskeletal (MSK) extremity imaging examinations to radiologists, so as to better understand the drivers of MSK imaging utilization and potentially improve the appropriateness of such imaging examinations. MATERIALS AND METHODS/METHODS:Data on provider referral for MSK extremity imaging services were extracted from the 2014 Medicare Referring Provider Utilization for Procedures public use file, which aggregates data on diagnostic procedures according to referring provider identities and service codes. MSK extremity imaging services were identified using Neiman Institute Types of Service codes. The referring provider specialty was identified from cross-linked Medicare provider characteristics files. RESULTS:For 4,275,647 MSK extremity imaging examinations ordered, the most common specialties of the referring providers were orthopedic surgery (37.6% of ordered examinations), internal medicine (20.2%), family practice (14.8%), emergency medicine (7.9%), and rheumatology (5.7%). Orthopedic surgery was the referring specialty that most commonly ordered MSK extremity CT (33,465 ordered examinations; for all other specialties, < 2000 examinations), MRI (325,485 examinations; for all other specialities, < 20,000 examinations), and radiography (1,249,748 examinations; for all other specialities, < 850,000 examinations), whereas internal medicine was the referring specialty that most commonly ordered MSK extremity ultrasound examinations (8052 ordered examinations; for all other specialties, < 6000 examinations). Among the select specialties most relevant to MSK imaging, the most frequent referrers after orthopedic surgeons were rheumatologists, for radiography (236,057 ordered examinations) and ultrasound (2034 examinations), and podiatrists, for CT (1201 examinations) and MRI (19,159 examinations). The most commonly ordered individual MSK extremity imaging services were knee radiography, with 190,354 examinations ordered by orthopedic surgeons; hand radiography, with 66,167 examinations ordered by rheumatologists; foot radiography, with 137,042 examinations ordered by podiatrists; shoulder radiography, with 11,299 examinations ordered by sports medicine specialists; and hip radiography, with 9838 examinations ordered by physiatrists. CONCLUSION/CONCLUSIONS:Referral patterns for MSK imaging vary considerably by provider specialty. Referral pattern insights may guide targeted efforts by radiologists to ensure the appropriateness of such examinations.