Literature DB >> 19710005

Recommendations for additional imaging in radiology reports: multifactorial analysis of 5.9 million examinations.

Christopher L Sistrom1, Keith J Dreyer, Pragya P Dang, Jeffrey B Weilburg, Giles W Boland, Daniel I Rosenthal, James H Thrall.   

Abstract

PURPOSE: To quantify the rates of recommendation for additional imaging (RAI) in a large number of radiology reports of different modalities and to estimate the effects of 11 clinically relevant factors.
MATERIALS AND METHODS: This HIPAA compliant research was approved by the institutional review board under an expedited protocol for analyzing anonymous aggregated radiology data. All diagnostic imaging examinations (n = 5 948 342) interpreted by radiologists between 1995 and 2008 were studied. A natural language processing technique specifically designed to extract information about any recommendations from radiology report texts was used. The analytic data set included three quantitative variables: the interpreting radiologist's experience, the year of study, and patient age. Categoric variables described patient location (inpatient, outpatient, emergency department), whether a resident dictated the case, patient sex, modality, body area studied, ordering service, radiologist's specialty division, and whether the examination result was positive. A multivariable logistic regression model was used to determine the effect of each of these factors on likelihood of RAI while holding all others equal.
RESULTS: Recommendations increased during the 13 years of study, with the unadjusted rate rising from roughly 6% to 12%. After accounting for all other factors, the odds of any one examination resulting in an RAI increased by 2.16 times (95% confidence interval: 2.12, 2.21) from 1995 to 2008. As radiologist experience increased, the odds of an RAI decreased by about 15% per decade. Studies that had positive findings were more likely (odds ratio = 5.03; 95% confidence interval: 4.98, 5.07) to have an RAI. The remaining factors also had significant effects on the tendency for an RAI.
CONCLUSION: The likelihood of RAI increased by 15% for each decade of radiologist experience and roughly doubled over 13 years of study. (c) RSNA, 2009.

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Year:  2009        PMID: 19710005     DOI: 10.1148/radiol.2532090200

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


  35 in total

1.  Automatic identification of critical follow-up recommendation sentences in radiology reports.

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Journal:  AMIA Annu Symp Proc       Date:  2011-10-22

2.  Time trends in radiologists' interpretive performance at screening mammography from the community-based Breast Cancer Surveillance Consortium, 1996-2004.

Authors:  Laura E Ichikawa; William E Barlow; Melissa L Anderson; Stephen H Taplin; Berta M Geller; R James Brenner
Journal:  Radiology       Date:  2010-05-26       Impact factor: 11.105

Review 3.  Natural Language Processing Technologies in Radiology Research and Clinical Applications.

Authors:  Tianrun Cai; Andreas A Giannopoulos; Sheng Yu; Tatiana Kelil; Beth Ripley; Kanako K Kumamaru; Frank J Rybicki; Dimitrios Mitsouras
Journal:  Radiographics       Date:  2016 Jan-Feb       Impact factor: 5.333

4.  A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop.

Authors:  Curtis P Langlotz; Bibb Allen; Bradley J Erickson; Jayashree Kalpathy-Cramer; Keith Bigelow; Tessa S Cook; Adam E Flanders; Matthew P Lungren; David S Mendelson; Jeffrey D Rudie; Ge Wang; Krishna Kandarpa
Journal:  Radiology       Date:  2019-04-16       Impact factor: 11.105

5.  Improving Quality of Follow-Up Imaging Recommendations in Radiology.

Authors:  Thusitha Mabotuwana; Christopher S Hall; Joel Tieder; Martin L Gunn
Journal:  AMIA Annu Symp Proc       Date:  2018-04-16

6.  Introduction: quality in diagnostic imaging: learning from worldwide initiatives.

Authors:  Ruth C Carlos; Stacy Goergen
Journal:  J Am Coll Radiol       Date:  2010-08       Impact factor: 5.532

7.  Recommendations for additional imaging on emergency department CT examinations: comparison of emergency- and organ-based subspecialty radiologists.

Authors:  Andrew B Rosenkrantz; Brent W Matza; Mark P Foran; John M McMenamy
Journal:  Emerg Radiol       Date:  2012-10-05

8.  A Web Application for Adrenal Incidentaloma Identification, Tracking, and Management Using Machine Learning.

Authors:  Wasif Bala; Jackson Steinkamp; Timothy Feeney; Avneesh Gupta; Abhinav Sharma; Jake Kantrowitz; Nicholas Cordella; James Moses; Frederick Thurston Drake
Journal:  Appl Clin Inform       Date:  2020-09-16       Impact factor: 2.342

9.  Interpreting the interpretations: the use of structured reporting improves referring clinicians' comprehension of coronary CT angiography reports.

Authors:  Brian B Ghoshhajra; Ashley M Lee; Maros Ferencik; Sammy Elmariah; Ronan J P Margey; Oyere Onuma; Marcello Panagia; Suhny Abbara; Udo Hoffmann
Journal:  J Am Coll Radiol       Date:  2013-02-26       Impact factor: 5.532

10.  The fate of radiology report recommendations at a pediatric medical center.

Authors:  Bonmyong Lee; Hansel J Otero; Matthew T Whitehead
Journal:  Pediatr Radiol       Date:  2017-08-29
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