Literature DB >> 8883510

Information content and clarity of radiologists' reports for chest radiography.

J L Sobel1, M L Pearson, K Gross, K A Desmond, E R Harrison, L V Rubenstein, W H Rogers, K L Kahn.   

Abstract

RATIONALE AND
OBJECTIVES: We systematically characterized the information provided by chest radiography reports on a nationally representative sample of 822 elderly patients hospitalized in 297 acute-care hospitals in five states who had an admission diagnosis of congestive heart failure, acute myocardial infarction, or pneumonia.
METHODS: We studied the content of radiography reports, including mention of the type or adequacy of radiography; the presence or absence of a prior radiograph; comments about bones, the aorta, the mediastinum, and pleura and notation of the laterality of findings; and the presence of diagnosis. Two physicians reviewed each patient's report, and a third assigned the final rating when they disagreed.
RESULTS: Our analysis found wide variation in content of chest radiography reports, extensive variation in terms used to identify the presence or absence of abnormal findings, and a large degree of uncertainty in what was found.
CONCLUSION: With most hospitals introducing new information systems in response to technological advances and the need to generate more formal hospitalwide reports, the time is right to improve the quality of chest radiography reporting.

Entities:  

Mesh:

Year:  1996        PMID: 8883510     DOI: 10.1016/s1076-6332(96)80407-7

Source DB:  PubMed          Journal:  Acad Radiol        ISSN: 1076-6332            Impact factor:   3.173


  19 in total

1.  Enhancing the expressiveness of structured reporting systems.

Authors:  C P Langlotz
Journal:  J Digit Imaging       Date:  2000-05       Impact factor: 4.056

2.  Enhancing the expressiveness and usability of structured image reporting systems.

Authors:  C P Langlotz; L Meininger
Journal:  Proc AMIA Symp       Date:  2000

3.  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

4.  Critical finding capture in the impression section of radiology reports.

Authors:  Esteban F Gershanik; Ronilda Lacson; Ramin Khorasani
Journal:  AMIA Annu Symp Proc       Date:  2011-10-22

5.  Structured reporting using a shared indexed multilingual radiology lexicon.

Authors:  Roberto Stramare; Giuliano Scattolin; Valeria Beltrame; Marco Gerardi; Marco Sommavilla; Cristina Gatto; Paolo Mosca; Leopoldo Rubaltelli; Carlo Riccardo Rossi; Claudio Saccavini
Journal:  Int J Comput Assist Radiol Surg       Date:  2011-10-19       Impact factor: 2.924

6.  Automated retrieval of CT images of liver lesions on the basis of image similarity: method and preliminary results.

Authors:  Sandy A Napel; Christopher F Beaulieu; Cesar Rodriguez; Jingyu Cui; Jiajing Xu; Ankit Gupta; Daniel Korenblum; Hayit Greenspan; Yongjun Ma; Daniel L Rubin
Journal:  Radiology       Date:  2010-05-26       Impact factor: 11.105

7.  Unsupervised Topic Modeling in a Large Free Text Radiology Report Repository.

Authors:  Saeed Hassanpour; Curtis P Langlotz
Journal:  J Digit Imaging       Date:  2016-02       Impact factor: 4.056

8.  Six characteristics of effective structured reporting and the inevitable integration with speech recognition.

Authors:  David Liu; Mark Zucherman; William B Tulloss
Journal:  J Digit Imaging       Date:  2006-03       Impact factor: 4.056

9.  Structured reporting in petrous bone MRI examinations: impact on report completeness and quality.

Authors:  Marco Armbruster; Sebastian Gassenmaier; Mareike Haack; Maximilian Reiter; Dominik Nörenberg; Thomas Henzler; Nora N Sommer; Wieland H Sommer; Franziska Braun
Journal:  Int J Comput Assist Radiol Surg       Date:  2018-07-23       Impact factor: 2.924

10.  Predicting visual semantic descriptive terms from radiological image data: preliminary results with liver lesions in CT.

Authors:  Adrien Depeursinge; Camille Kurtz; Christopher Beaulieu; Sandy Napel; Daniel Rubin
Journal:  IEEE Trans Med Imaging       Date:  2014-05-01       Impact factor: 10.048

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