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Value-based Radiology: What is the ESR Doing, and What Should We Do in the Future?

Overview
Publisher Springer
Specialty Radiology
Date 2021 Jul 28
PMID 34318401
Citations 9
Affiliations
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Abstract

Value-based radiology (VBR) is rapidly gaining ground as a means of considering the input of radiology practice into individual and societal healthcare, and represents a welcome move away from older metrics focused on counting studies performed, without consideration of whether these studies contributed positively to patient management or to society as a whole. Intrinsic to the process of considering whether radiology activity confers value is recognising the breadth of involvement of radiology in healthcare delivery; previous ESR and multi-society publications have explored this, and have sought to highlight the many ways in which our specialty contributes to patient welfare. This paper is intended to highlight some current ESR activities which already contribute substantially to value creation and delivery, and to outline a selection of practical steps which could be taken by the ESR in the future to enhance value.Patient summaryValue-based radiology (VBR) is a conceptual means of looking at the benefits conferred on patients and on society as a whole by provision of radiology services, as opposed to older means of counting numbers of radiology studies performed, without consideration of whether or not those studies contributed overall value. VBR will become increasingly important in the future as a means of determining resources. The ESR has been a leader in advancing VBR concepts and educating radiologists about this novel way of looking at what we do. This paper is designed to highlight current ESR activities which contribute value to healthcare, and to consider other ways in which the ESR could potentially support value enhancement in the future.

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References
1.
Brady A, Brink J, Slavotinek J . Radiology and Value-Based Health Care. JAMA. 2020; 324(13):1286-1287. DOI: 10.1001/jama.2020.14930. View

2.
Sarwar A, Boland G, Monks A, Kruskal J . Metrics for Radiologists in the Era of Value-based Health Care Delivery. Radiographics. 2015; 35(3):866-76. DOI: 10.1148/rg.2015140221. View

3.
Vasey B, Ursprung S, Beddoe B, Taylor E, Marlow N, Bilbro N . Association of Clinician Diagnostic Performance With Machine Learning-Based Decision Support Systems: A Systematic Review. JAMA Netw Open. 2021; 4(3):e211276. PMC: 7953308. DOI: 10.1001/jamanetworkopen.2021.1276. View

4.
Boonn W, Langlotz C . Radiologist use of and perceived need for patient data access. J Digit Imaging. 2008; 22(4):357-62. PMC: 3043710. DOI: 10.1007/s10278-008-9115-2. View

5.
Rubin D, Kahn Jr C . Common Data Elements in Radiology. Radiology. 2016; 283(3):837-844. DOI: 10.1148/radiol.2016161553. View