The Experts below are selected from a list of 219228 Experts worldwide ranked by ideXlab platform
Jay R Parikh - One of the best experts on this subject based on the ideXlab platform.
-
integration of the community based academic Radiologist with the academic radiology department a strategic imperative
Journal of The American College of Radiology, 2020Co-Authors: Jay R Parikh, Megan KalamboAbstract:Abstract Academic radiology departments are expanding into the community with deployment of community-based academic Radiologists (CBARs). The remote practice locations, unique workplace challenges, and limited opportunities for meaningful collegial interactions can become drivers for Radiologist isolation, dissatisfaction, and burnout. Integration of CBARs with the academic radiology department with which they are affiliated is a strategic imperative to mitigate Radiologist isolation and potential burnout. Committed physician leadership by the academic radiology department can support integration. Strategies to strengthen integration include bidirectional clinical coverage systems, pairing new CBARs with established academic Radiologist mentors at the academic center, encouraging CBARs to serve on academic committees and collaborate on research projects with Radiologists at the academic center, and recognizing CBARs for their achievements in the areas of clinical productivity, practice development, community outreach, collegiality, and innovation.
-
The Community-Based Academic Radiologist.
Journal of the American College of Radiology : JACR, 2018Co-Authors: Megan Kalambo, Jay R ParikhAbstract:As academic radiology practices expand into the community, the lines that have historically distinguished the academic from private practice Radiologist are becoming increasingly blurred. In this article, we introduce the new concept of the community-based academic Radiologist and address some of the unique challenges and opportunities faced by these Radiologists navigating this new hybrid role of academician and Radiologist in community-based private practice.
Megan Kalambo - One of the best experts on this subject based on the ideXlab platform.
-
integration of the community based academic Radiologist with the academic radiology department a strategic imperative
Journal of The American College of Radiology, 2020Co-Authors: Jay R Parikh, Megan KalamboAbstract:Abstract Academic radiology departments are expanding into the community with deployment of community-based academic Radiologists (CBARs). The remote practice locations, unique workplace challenges, and limited opportunities for meaningful collegial interactions can become drivers for Radiologist isolation, dissatisfaction, and burnout. Integration of CBARs with the academic radiology department with which they are affiliated is a strategic imperative to mitigate Radiologist isolation and potential burnout. Committed physician leadership by the academic radiology department can support integration. Strategies to strengthen integration include bidirectional clinical coverage systems, pairing new CBARs with established academic Radiologist mentors at the academic center, encouraging CBARs to serve on academic committees and collaborate on research projects with Radiologists at the academic center, and recognizing CBARs for their achievements in the areas of clinical productivity, practice development, community outreach, collegiality, and innovation.
-
The Community-Based Academic Radiologist.
Journal of the American College of Radiology : JACR, 2018Co-Authors: Megan Kalambo, Jay R ParikhAbstract:As academic radiology practices expand into the community, the lines that have historically distinguished the academic from private practice Radiologist are becoming increasingly blurred. In this article, we introduce the new concept of the community-based academic Radiologist and address some of the unique challenges and opportunities faced by these Radiologists navigating this new hybrid role of academician and Radiologist in community-based private practice.
Angélica Ángeles-llerenas - One of the best experts on this subject based on the ideXlab platform.
-
Radiographers supporting Radiologists in the interpretation of screening mammography: a viable strategy to meet the shortage in the number of Radiologists.
BMC cancer, 2015Co-Authors: Gabriela Torres-mejía, Robert A. Smith, María De La Luz Carranza-flores, Andy Bogart, Louis Martínez-matsushita, Diana L. Miglioretti, Karla Kerlikowske, Carolina Ortega-olvera, Ernesto Montemayor-varela, Angélica Ángeles-llerenasAbstract:An alternative approach to the traditional model of Radiologists interpreting screening mammography is necessary due to the shortage of Radiologists to interpret screening mammograms in many countries. We evaluated the performance of 15 Mexican radiographers, also known as radiologic technologists, in the interpretation of screening mammography after a 6 months training period in a screening setting. Fifteen radiographers received 6 months standardized training with Radiologists in the interpretation of screening mammography using the Breast Imaging Reporting and Data System (BI-RADS) system. A challenging test set of 110 cases developed by the Breast Cancer Surveillance Consortium was used to evaluate their performance. We estimated sensitivity, specificity, false positive rates, likelihood ratio of a positive test (LR+) and the area under the subject-specific Receiver Operating Characteristic (ROC) curve (AUC) for diagnostic accuracy. A mathematical model simulating the consequences in costs and performance of two hypothetical scenarios compared to the status quo in which a Radiologist reads all screening mammograms was also performed. Radiographer’s sensitivity was comparable to the sensitivity scores achieved by U.S. Radiologists who took the test but their false-positive rate was higher. Median sensitivity was 73.3 % (Interquartile range, IQR: 46.7–86.7 %) and the median false positive rate was 49.5 % (IQR: 34.7–57.9 %). The median LR+ was 1.4 (IQR: 1.3-1.7 %) and the median AUC was 0.6 (IQR: 0.6–0.7). A scenario in which a radiographer reads all mammograms first, and a Radiologist reads only those that were difficult for the radiographer, was more cost-effective than a scenario in which either the radiographer or Radiologist reads all mammograms. Given the comparable sensitivity achieved by Mexican radiographers and U.S. Radiologists on a test set, screening mammography interpretation by radiographers appears to be a possible adjunct to Radiologists in countries with shortages of Radiologists. Further studies are required to assess the effectiveness of different training programs in order to obtain acceptable screening accuracy, as well as the best approaches for the use of non-physician readers to interpret screening mammography.
Richard Duszak - One of the best experts on this subject based on the ideXlab platform.
-
Radiologist-Practice Separation: Recent Trends and Characteristics.
Journal of the American College of Radiology : JACR, 2020Co-Authors: Stefan Santavicca, Danny R. Hughes, Howard B. Fleishon, Frank J. Lexa, Eric Rubin, Andrew B. Rosenkrantz, Richard DuszakAbstract:Abstract Purpose To assess recent trends and characteristics in Radiologist-practice separation across the United States. Methods Using the Medicare Physician Compare and Medicare Physician and Other Supplier Public Use File data sets, we linked all Radiologists to associated group practices annually between 2014 and 2018 and assessed Radiologist-practice separation over a variety of physician and group characteristics. Multivariate logistic regression modeling was used to estimate the likelihood of Radiologist-practice separation. Results Of 25,228 unique Radiologists associated with 4,381 unique group practices, 41.1% separated from at least one group practice between 2014 and 2018, and annual separation rates increased 38.4% over time (13.8% from 2014 to 2015 to 19.2% from 2017 to 2018). Radiologist-practice separation rates ranged from 57.4% in Utah to 26.3% in Virginia. Separation rates were 42.8% for general Radiologists versus 38.2% for subspecialty Radiologists. Among subspecialists, separation rates ranged from 43.0% for breast imagers to 33.5% for cardiothoracic Radiologists. Early career status (odds ratio [OR] = 1.286) and late (OR = 1.554) career status were both independent positive predictors of Radiologist-practice separation (both P Conclusions With over 40% of Radiologists separating from at least one practice in recent years, the US Radiologist workforce is highly and increasingly mobile. Because reasons for separation (eg, resignation, practice acquisition) cannot be assessed using administrative data, further attention is warranted given the manifold financial, operational, and patient care implications.
-
Increasing Subspecialization of the National Radiologist Workforce.
Journal of the American College of Radiology : JACR, 2019Co-Authors: Andrew B. Rosenkrantz, Danny R. Hughes, Richard DuszakAbstract:Abstract Purpose The aim of this study was to assess recent trends in the generalist versus subspecialist composition of the national Radiologist workforce. Methods Practicing Radiologists were identified using 2012 to 2017 CMS Physician and Other Supplier Public Use Files. Work relative value units associated with Radiologists’ billed claims were mapped to subspecialties using the Neiman Imaging Types of Service to classify Radiologists as subspecialists when exceeding a 50% work effort in a given subspecialty and as generalists otherwise. Additional practice characteristics were obtained from CMS Physician Compare. Chi-square statistics were computed. Results The percentage of Radiologists practicing as subspecialists increased from 37.1% in 2012 and 2013 to 38.8% in 2014, 41.0% in 2015, 43.9% in 2016, and 44.6% in 2017. By subspecialty, 2012 to 2017 workforce changes were as follows: breast, +3.7%; abdominal, +2.4%; neuroradiology, +1.8%; musculoskeletal, +0.8%; cardiothoracic, +0.2%; nuclear, −0.2%; and interventional, −1.2%. Increased subspecialization overall was consistently observed (P Conclusions In recent years, the national Radiologist workforce has become increasingly subspecialized, particularly related to shifts toward breast imaging, abdominal imaging, and neuroradiology. Although growing subspecialization may advance more sophisticated imaging care, a diminishing supply of generalists could affect patient access and potentially separate Radiologists across workforce sectors.
-
a county level analysis of the us Radiologist workforce physician supply and subspecialty characteristics
Journal of The American College of Radiology, 2018Co-Authors: Andrew B. Rosenkrantz, Danny R. Hughes, Wenyi Wang, Richard DuszakAbstract:Abstract Purpose 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 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 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.
-
private practice Radiologist subspecialty classification using medicare claims
Journal of The American College of Radiology, 2017Co-Authors: Andrew B. Rosenkrantz, Danny R. Hughes, Wenyi Wang, Sudheshna Bodapati, Richard DuszakAbstract:Abstract Purpose 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. Methods 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. Results 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. Conclusions 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.
-
academic Radiologist subspecialty identification using a novel claims based classification system
American Journal of Roentgenology, 2017Co-Authors: Andrew B. Rosenkrantz, Danny R. Hughes, Wenyi Wang, Luke A Ginocchio, David A Rosman, Richard DuszakAbstract: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 ...
Pawan Tiwari - One of the best experts on this subject based on the ideXlab platform.
-
Artificial Intelligence–assisted chest X-ray assessment scheme for COVID-19
European Radiology, 2021Co-Authors: Krithika Rangarajan, Sumanyu Muku, Amit Kumar Garg, Pavan Gabra, Sujay Halkur Shankar, Neeraj Nischal, Kapil Dev Soni, Ashu Seith Bhalla, Anant Mohan, Pawan TiwariAbstract:Objectives To study whether a trained convolutional neural network (CNN) can be of assistance to Radiologists in differentiating Coronavirus disease (COVID)–positive from COVID-negative patients using chest X-ray (CXR) through an ambispective clinical study. To identify subgroups of patients where artificial intelligence (AI) can be of particular value and analyse what imaging features may have contributed to the performance of AI by means of visualisation techniques. Methods CXR of 487 patients were classified into [4] categories—normal, classical COVID, indeterminate, and non-COVID by consensus opinion of 2 Radiologists. CXR which were classified as “normal” and “indeterminate” were then subjected to analysis by AI, and final categorisation provided as guided by prediction of the network. Precision and recall of the Radiologist alone and Radiologist assisted by AI were calculated in comparison to reverse transcriptase-polymerase chain reaction (RT-PCR) as the gold standard. Attention maps of the CNN were analysed to understand regions in the CXR important to the AI algorithm in making a prediction. Results The precision of Radiologists improved from 65.9 to 81.9% and recall improved from 17.5 to 71.75 when assistance with AI was provided. AI showed 92% accuracy in classifying “normal” CXR into COVID or non-COVID. Analysis of attention maps revealed attention on the cardiac shadow in these “normal” radiographs. Conclusion This study shows how deployment of an AI algorithm can complement a human expert in the determination of COVID status. Analysis of the detected features suggests possible subtle cardiac changes, laying ground for further investigative studies into possible cardiac changes. Key Points • Through an ambispective clinical study, we show how assistance with an AI algorithm can improve recall (sensitivity) and precision (positive predictive value) of Radiologists in assessing CXR for possible COVID in comparison to RT-PCR. • We show that AI achieves the best results in images classified as “normal” by Radiologists. We conjecture that possible subtle cardiac in the CXR, imperceptible to the human eye, may have contributed to this prediction. • The reported results may pave the way for a human computer collaboration whereby the expert with some help from the AI algorithm achieves higher accuracy in predicting COVID status on CXR than previously thought possible when considering either alone.
