Literature DB >> 30237302

The ASNR-ACR-RSNA Common Data Elements Project: What Will It Do for the House of Neuroradiology?

A E Flanders1, J E Jordan2.   

Abstract

The American Society of Neuroradiology has teamed up with the American College of Radiology and the Radiological Society of North America to create a catalog of neuroradiology common data elements that addresses specific clinical use cases. Fundamentally, a common data element is a question, concept, measurement, or feature with a set of controlled responses. This could be a measurement, subjective assessment, or ordinal value. Common data elements can be both machine- and human-generated. Rather than redesigning neuroradiology reporting, the goal is to establish the minimum number of "essential" concepts that should be in a report to address a clinical question. As medicine shifts toward value-based service compensation methodologies, there will be an even greater need to benchmark quality care and allow peer-to-peer comparisons in all specialties. Many government programs are now focusing on these measures, the most recent being the Merit-Based Incentive Payment System and the Medicare Access Children's Health Insurance Program Reauthorization Act of 2015. Standardized or structured reporting is advocated as one method of assessing radiology report quality, and common data elements are a means for expressing these concepts. Incorporating common data elements into clinical practice fosters a number of very useful downstream processes including establishing benchmarks for quality-assurance programs, ensuring more accurate billing, improving communication to providers and patients, participating in public health initiatives, creating comparative effectiveness research, and providing classifiers for machine learning. Generalized adoption of the recommended common data elements in clinical practice will provide the means to collect and compare imaging report data from multiple institutions locally, regionally, and even nationally, to establish quality benchmarks.
© 2019 by American Journal of Neuroradiology.

Entities:  

Mesh:

Year:  2018        PMID: 30237302      PMCID: PMC7048600          DOI: 10.3174/ajnr.A5780

Source DB:  PubMed          Journal:  AJNR Am J Neuroradiol        ISSN: 0195-6108            Impact factor:   3.825


  10 in total

1.  Improving communication of diagnostic radiology findings through structured reporting.

Authors:  Lawrence H Schwartz; David M Panicek; Alexandra R Berk; Yuelin Li; Hedvig Hricak
Journal:  Radiology       Date:  2011-04-25       Impact factor: 11.105

2.  Toward best practices in radiology reporting.

Authors:  Charles E Kahn; Curtis P Langlotz; Elizabeth S Burnside; John A Carrino; David S Channin; David M Hovsepian; Daniel L Rubin
Journal:  Radiology       Date:  2009-09       Impact factor: 11.105

3.  Value of a standardized lexicon for reporting levels of diagnostic certainty in prostate MRI.

Authors:  Andreas Wibmer; Hebert Alberto Vargas; Ramon Sosa; Junting Zheng; Chaya Moskowitz; Hedvig Hricak
Journal:  AJR Am J Roentgenol       Date:  2014-12       Impact factor: 3.959

4.  Creation of an Open Framework for Point-of-Care Computer-Assisted Reporting and Decision Support Tools for Radiologists.

Authors:  Tarik K Alkasab; Bernardo C Bizzo; Lincoln L Berland; Sujith Nair; Pari V Pandharipande; H Benjamin Harvey
Journal:  J Am Coll Radiol       Date:  2017-06-23       Impact factor: 5.532

5.  Medicare Program; Merit-Based Incentive Payment System (MIPS) and Alternative Payment Model (APM) Incentive Under the Physician Fee Schedule, and Criteria for Physician-Focused Payment Models. Final rule with comment period.

Authors: 
Journal:  Fed Regist       Date:  2016-11-04

6.  Common Data Elements in Radiology.

Authors:  Daniel L Rubin; Charles E Kahn
Journal:  Radiology       Date:  2016-11-10       Impact factor: 11.105

7.  Contextual Radiology Reporting: A New Approach to Neuroradiology Structured Templates.

Authors:  M D Mamlouk; P C Chang; R R Saket
Journal:  AJNR Am J Neuroradiol       Date:  2018-06-14       Impact factor: 3.825

8.  Enabling the Next-Generation Radiology Report: Description of Two New System Standards.

Authors:  James Y Chen; Teri M Sippel Schmidt; Christopher D Carr; Charles E Kahn
Journal:  Radiographics       Date:  2017-10-02       Impact factor: 5.333

9.  Management-Based Structured Reporting of Posttreatment Glioma Response With the Brain Tumor Reporting and Data System.

Authors:  Brent D Weinberg; Ashwani Gore; Hui-Kuo G Shu; Jeffrey J Olson; Richard Duszak; Alfredo D Voloschin; Michael J Hoch
Journal:  J Am Coll Radiol       Date:  2018-03-02       Impact factor: 5.532

10.  Structured Reporting in Neuroradiology: Intracranial Tumors.

Authors:  Andrea Bink; Jan Benner; Julia Reinhardt; Anthony De Vere-Tyndall; Bram Stieltjes; Nicolin Hainc; Christoph Stippich
Journal:  Front Neurol       Date:  2018-02-06       Impact factor: 4.003

  10 in total
  2 in total

1.  Fostering a Healthy AI Ecosystem for Radiology: Conclusions of the 2018 RSNA Summit on AI in Radiology.

Authors:  Falgun H Chokshi; Adam E Flanders; Luciano M Prevedello; Curtis P Langlotz
Journal:  Radiol Artif Intell       Date:  2019-03-27

Review 2.  Multispecialty Enterprise Imaging Workgroup Consensus on Interactive Multimedia Reporting Current State and Road to the Future: HIMSS-SIIM Collaborative White Paper.

Authors:  Christopher J Roth; David A Clunie; David J Vining; Seth J Berkowitz; Alejandro Berlin; Jean-Pierre Bissonnette; Shawn D Clark; Toby C Cornish; Monief Eid; Cree M Gaskin; Alexander K Goel; Genevieve C Jacobs; David Kwan; Damien M Luviano; Morgan P McBee; Kelly Miller; Abdul Moiz Hafiz; Ceferino Obcemea; Anil V Parwani; Veronica Rotemberg; Elliot L Silver; Erik S Storm; James E Tcheng; Karen S Thullner; Les R Folio
Journal:  J Digit Imaging       Date:  2021-06-15       Impact factor: 4.056

  2 in total

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