Literature DB >> 22438447

Radiology's value chain.

Dieter R Enzmann1.   

Abstract

A diagnostic radiology value chain is constructed to define its main components, all of which are vulnerable to change, because digitization has caused disaggregation of the chain. Some components afford opportunities to improve productivity, some add value, while some face outsourcing to lower labor cost and to information technology substitutes, raising commoditization risks. Digital image information, because it can be competitive at smaller economies of scale, allows faster, differential rates of technological innovation of components, initiating a centralization-to-decentralization technology trend. Digitization, having triggered disaggregation of radiology's professional service model, may soon usher in an information business model. This means moving from a mind-set of "reading images" to an orientation of creating and organizing information for greater accuracy, faster speed, and lower cost in medical decision making. Information businesses view value chain investments differently than do small professional services. In the former model, producing a better business product will extend image interpretation beyond a radiologist's personal fund of knowledge to encompass expanding external imaging databases. A follow-on expansion with integration of image and molecular information into a report will offer new value in medical decision making. Improved interpretation plus new integration will enrich and diversify radiology's key service products, the report and consultation. A more robust, information-rich report derived from a "systems" and "computational" radiology approach will be facilitated by a transition from a professional service to an information business. Under health care reform, radiology will transition its emphasis from volume to greater value. Radiology's future brightens with the adoption of a philosophy of offering information rather than "reads" for decision making. Staunchly defending the status quo via turf wars is unlikely to constitute a forward-looking, competitive strategy. © RSNA, 2012.

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Year:  2012        PMID: 22438447     DOI: 10.1148/radiol.12110227

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  8 in total

Review 1.  Academic radiology in the new health care delivery environment.

Authors:  Aliya Qayyum; John-Paul J Yu; Akash P Kansagra; Nathaniel von Fischer; Daniel Costa; Matthew Heller; Stamatis Kantartzis; R Scooter Plowman; Jason Itri
Journal:  Acad Radiol       Date:  2013-12       Impact factor: 3.173

2.  Preferences of referring physicians regarding the role of radiologists as direct communicators of test results.

Authors:  Nuri Erdoğan; Hakan İmamoğlu; Süreyya Burcu Görkem; Serap Doğan; Serkan Şenol; Ahmet Öztürk
Journal:  Diagn Interv Radiol       Date:  2017 Jan-Feb       Impact factor: 2.630

Review 3.  Mobile devices and their prospective future role in emergency radiology.

Authors:  Timothy W O'Connell; Michael N Patlas
Journal:  Br J Radiol       Date:  2016-02-01       Impact factor: 3.039

Review 4.  Methods and challenges in quantitative imaging biomarker development.

Authors:  Richard G Abramson; Kirsteen R Burton; John-Paul J Yu; Ernest M Scalzetti; Thomas E Yankeelov; Andrew B Rosenkrantz; Mishal Mendiratta-Lala; Brian J Bartholmai; Dhakshinamoorthy Ganeshan; Leon Lenchik; Rathan M Subramaniam
Journal:  Acad Radiol       Date:  2015-01       Impact factor: 3.173

5.  Using Time as a Measure of Impact for AI Systems: Implications in Breast Screening.

Authors:  William Hsu; Anne C Hoyt
Journal:  Radiol Artif Intell       Date:  2019-07-31

6.  Classifying Safety Events Related to Diagnostic Imaging From a Safety Reporting System Using a Human Factors Framework.

Authors:  Ronilda Lacson; Laila Cochon; Ivan Ip; Sonali Desai; Allen Kachalia; Jack Dennerlein; James Benneyan; Ramin Khorasani
Journal:  J Am Coll Radiol       Date:  2018-12-07       Impact factor: 5.532

7.  ESR concept paper on value-based radiology.

Authors: 
Journal:  Insights Imaging       Date:  2017-08-30

Review 8.  AI MSK clinical applications: spine imaging.

Authors:  Florian A Huber; Roman Guggenberger
Journal:  Skeletal Radiol       Date:  2021-07-15       Impact factor: 2.199

  8 in total

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