Rates of disagreement in imaging interpretation in a group of community hospitals

Siegle, Robert L.; Baram, E. M.; Reuter, Stephan; Clarke, Eleanor; Lancaster, Jeffrey; McMahan, C. Alex

doi:10.1016/s1076-6332(98)80277-8

Cited by 92 publications

(40 citation statements)

References 13 publications

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“…3,17 Also, as the disease prevalence increases, so does the expected error rate, approaching Garland's rate of 30% for case mixes with 100% disease prevalence. [4][5][6]12 Borgstede et al 3 showed that academic medical centers compared with community hospitals had error rates approximately 1.5% higher, presumably due to greater disease prevalence. In this study, the overall disease prevalence was 92%.…”

Section: Discussionmentioning

confidence: 99%

“…3,[12][13][14] Soffa et al 14 sampled approximately 7000 cases read by 26 radiologists and uncovered a 3% disagreement rate in general radiology, 3.6% in diagnostic mammography, 5.8% in screening mammography, and 4.1% in sonography, yielding the overall error rate of 3.5%. Robinson et al 13 compared reports for skeletal, chest, and abdominal radiographs completed by 3 radiologists and found a 3%-6% average error rate per observer.…”

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confidence: 99%

“…Robinson et al 13 compared reports for skeletal, chest, and abdominal radiographs completed by 3 radiologists and found a 3%-6% average error rate per observer. Siegle et al 12 reviewed radiologic studies performed during a 7-year period in 6 community hospitals, including general radiology, nuclear medicine, CT, and MR imaging, and calculated a mean rate of disagreement of 4.4%. In agreement with Garland's results, practices with arguably higher disease prevalence, like academic medical centers, tend to have higher error rates (Յ1.5% has been reported) than practices with a greater number of normal findings on studies, like community hospitals.…”

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confidence: 99%

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Quality Control in Neuroradiology: Discrepancies in Image Interpretation among Academic Neuroradiologists

Babiarz¹,

Yousem²

2011

AJNR Am J Neuroradiol

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SUMMARY:Prior studies have found a 3%-6% clinically significant error rate in radiology practice. We set out to assess discrepancy rates between subspecialty-trained university-based neuroradiologists. Over 17 months, university neuroradiologists randomly reviewed 1000 studies and reports of previously read examinations of patients in whom follow-up studies were read. The discrepancies between the original and "second opinion" reports were scored according to a 5-point scale: 1, no change; 2, clinically insignificant detection discrepancy; 3, clinically insignificant interpretation discrepancy; 4, clinically significant detection discrepancy; and 5, clinically significant interpretation discrepancy. Of the 1000 studies, 876 (87.6%) showed agreements with the original report. The neuroradiology division had a 2.0% (20/1000; 95% CI, 1.1%-2.9%) rate of clinically significant discrepancies involving 8 CTs and 12 MR images. Discrepancies were classified as vascular (n ϭ 7), neoplastic (n ϭ 9), congenital (n ϭ 2), and artifacts (n ϭ 2). Individual neuroradiologist's scores ranged from 0% to 7.7% Ϯ 2.3% (n ϭ 18). Both CT and MR imaging studies had a discrepancy rate of 2.0%. Our quality assessment study could serve as initial data before intervention as part of a PQI project.ABBREVIATIONS: ABR ϭ American Board of Radiology; ACGME ϭ Accreditation Council for Graduate Medical Education; CI ϭ confidence interval; PQI ϭ practice quality improvement R adiologic detection and interpretation errors will not be fully eliminated until the advent of "perfect diagnostic tests" and "perfect observers." 1,2 In the meantime, radiologists, similar to other physicians, struggle with assessing physician performance and reporting quality, to improve and deliver the best care possible.3 L. Henry Garland pioneered the work on radiologic errors more than 60 years ago. [4][5][6] He uncovered a 30% rate of missed radiologic findings in a series of radiographs with abnormal findings among expert reviewers. Subsequently Garland's results have been replicated by other researchers. [7][8][9] Most interesting, comparable rates of "mistakes" were discovered in other specialties. 10,11 In deriving the radiologic error rate, Garland used exclusively abnormal studies-that is, he tested radiologists in environments in which disease prevalence reached 100%. Because in the typical clinical setting, there are a substantial number of examinations with normal findings, Garland hypothesized that the expected radiologic error rate in everyday practice is closer to 5%.Subsequent studies confirmed the radiologic error rate in all-comers radiology practice to be in the 3%-6% range. 3,12-14Soffa et al 14 sampled approximately 7000 cases read by 26 radiologists and uncovered a 3% disagreement rate in general radiology, 3.6% in diagnostic mammography, 5.8% in screening mammography, and 4.1% in sonography, yielding the overall error rate of 3.5%. Robinson et al 13 compared reports for skeletal, chest, and abdominal radiographs completed by 3 radiologists and found ...

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Section: Discussionmentioning

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Quality Control in Neuroradiology: Discrepancies in Image Interpretation among Academic Neuroradiologists

Babiarz¹,

Yousem²

2011

AJNR Am J Neuroradiol

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“…Medical image interpretation consists of three key tasks: (1) perception of image findings, (2) interpretation of those findings to render a diagnosis or differential diagnosis, and (3) recommendations for clinical management (biopsy, follow up, etc.) or further imaging if a firm diagnosis has not been established.…”

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“…Significant inter-observer variation has been documented in numerous studies. 2 For example, in mammographic interpretation, there is variation in sensitivity, specificity, and area under the receiver operating characteristic curve among radiologists. 3 This variation results partly from the complexity of processing the vast amounts of knowledge needed to interpret imaging findings.…”

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Content-Based Image Retrieval in Radiology: Current Status and Future Directions

et al. 2010

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Diagnostic radiology requires accurate interpretation of complex signals in medical images. Content-based image retrieval (CBIR) techniques could be valuable to radiologists in assessing medical images by identifying similar images in large archives that could assist with decision support. Many advances have occurred in CBIR, and a variety of systems have appeared in nonmedical domains; however, permeation of these methods into radiology has been limited. Our goal in this review is to survey CBIR methods and systems from the perspective of application to radiology and to identify approaches developed in nonmedical applications that could be translated to radiology. Radiology images pose specific challenges compared with images in the consumer domain; they contain varied, rich, and often subtle features that need to be recognized in assessing image similarity. Radiology images also provide rich opportunities for CBIR: rich metadata about image semantics are provided by radiologists, and this information is not yet being used to its fullest advantage in CBIR systems. By integrating pixel-based and metadata-based image feature analysis, substantial advances of CBIR in medicine could ensue, with CBIR systems becoming an important tool in radiology practice.

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The Missed Diagnosis: Overview

Eisenberg

2004

Radiology and the Law

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Rates of disagreement in imaging interpretation in a group of community hospitals

Cited by 92 publications

References 13 publications

Quality Control in Neuroradiology: Discrepancies in Image Interpretation among Academic Neuroradiologists

Quality Control in Neuroradiology: Discrepancies in Image Interpretation among Academic Neuroradiologists

Content-Based Image Retrieval in Radiology: Current Status and Future Directions

The Missed Diagnosis: Overview

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