Error in radiology—where are we now?

Maskell, Giles

doi:10.1259/bjr.20180845

Cited by 18 publications

(16 citation statements)

References 9 publications

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“…Remarkably, even when considering technological advancements in scanners, little has changed in the rate of reader discordance over time. Radiological disagreement rates of approximately 30–40% reported by several papers since 1959 are consistent with the rates shown by Ford in 2016 across a variety of oncologic indications [ 45 – 47 ] [, holding steady across different response criteria, whether quantitative or subjective in form [ 26 , 35 , 45 – 49 ]. This consistency may be due to only 5–10% of the information for visual perception coming from the retina while 90–95% comes from different regions of the brain including the cortex and brain stem [ 50 ].…”

Section: Reader Disagreements Are Consistent With Diagnostic Disagreements In Other Areas Of Medicinesupporting

confidence: 79%

“…A search of clinicaltrials.gov for Phase 2 and 3 studies using RECIST for PFS resulted in 3429 studies over the last 10 years for an average of 175 subjects/trial which may be used to approximate the number of clinical trial cases read when the reader curriculum vitae indicates only the number of clinical trials experience and not the total number of cases. 3 Interestingly, the interaction of reader experience and fatigue having a greater influence on performance for readers with less experience [ 29 , 30 , 47 ]. However, this may not always be true since, at times, information on newer scanning techniques that were previously unavailable may compensate for a reader’s lack of experience [ 65 ].…”

Section: Why Expert Readers Disagreementioning

confidence: 99%

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Radiologists and Clinical Trials: Part 1 The Truth About Reader Disagreements

Schmid

Raunig

Miller³

et al. 2021

Ther Innov Regul Sci

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The debate over human visual perception and how medical images should be interpreted have persisted since X-rays were the only imaging technique available. Concerns over rates of disagreement between expert image readers are associated with much of the clinical research and at times driven by the belief that any image endpoint variability is problematic. The deeper understanding of the reasons, value, and risk of disagreement are somewhat siloed, leading, at times, to costly and risky approaches, especially in clinical trials. Although artificial intelligence promises some relief from mistakes, its routine application for assessing tumors within cancer trials is still an aspiration. Our consortium of international experts in medical imaging for drug development research, the Pharma Imaging Network for Therapeutics and Diagnostics (PINTAD), tapped the collective knowledge of its members to ground expectations, summarize common reasons for reader discordance, identify what factors can be controlled and which actions are likely to be effective in reducing discordance. Reinforced by an exhaustive literature review, our work defines the forces that shape reader variability. This review article aims to produce a singular authoritative resource outlining reader performance’s practical realities within cancer trials, whether they occur within a clinical or an independent central review.

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Section: Reader Disagreements Are Consistent With Diagnostic Disagreements In Other Areas Of Medicinesupporting

confidence: 79%

Section: Why Expert Readers Disagreementioning

confidence: 99%

Radiologists and Clinical Trials: Part 1 The Truth About Reader Disagreements

Schmid

Raunig

Miller³

et al. 2021

Ther Innov Regul Sci

View full text Add to dashboard Cite

show abstract

“…This may leave an idea of gross medical malpractice in the minds of patients, even when it is not. 29 In such situations, direct verbal communication of the changed or additional findings with an apology to the patients and the referring physician can prevent loss of trust.…”

Section: Discussionmentioning

confidence: 99%

Radiology Reporting Errors: Learning from Report Addenda

Patra

Premkumar

Keshava

et al. 2021

Indian J Radiol Imaging

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Background The addition of new information to a completed radiology report in the form of an “addendum” conveys a variety of information, ranging from less significant typographical errors to serious omissions and misinterpretations. Understanding the reasons for errors and their clinical implications will lead to better clinical governance and radiology practice. Aims This article assesses the common reasons which lead to addenda generation to completed reports and their clinical implications. Subjects and Methods Retrospective study was conducted by reviewing addenda to computed tomography (CT), ultrasound, and magnetic resonance imaging reports between January 2018 to June 2018, to note the frequency and classification of report addenda. Results Rate of addenda generation was 1.1% (n = 1,076) among the 97,003 approved cross-sectional radiology reports. Errors contributed to 71.2% (n = 767) of addenda, most commonly communication (29.3%, n = 316) and observational errors (20.8%, n = 224), and 28.7% were nonerrors aimed at providing additional clinically relevant information. Majority of the addenda (82.3%, n = 886) did not have a significant clinical impact. CT and ultrasound reports accounted for 36.9% (n = 398) and 35.2% (n = 379) share, respectively. A time gap of 1 to 7 days was noted for 46.8% (n = 504) addenda and 37.6% (n = 405) were issued in less than a day. Radiologists with more than 6-year experience created majority (1.5%, n = 456) of addenda. Those which were added to reports generated during emergency hours contributed to 23.2% (n = 250) of the addenda. Conclusion The study has identified the prevalence of report addenda in a radiology practice involving picture archiving and communication system in a tertiary care center in India. The etiology included both errors and non-errors. Results of this audit were used to generate a checklist and put protocols that will help decrease serious radiology misses and common errors.

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“…Worldwide, a 4% error rate would translate to approximately 40 million errors per year (Imanzadeh et al, 2020). Computer aided detection and machine learning methods promise to improve diagnostic accuracy, yet these same technologies place new demands on radiologists and can introduce novel sources of perceptual error (Slater et al, 2006;McGurk et al, 2008;Maskell, 2019).…”

Section: Type Of Illusionmentioning

confidence: 99%

Visual Illusions in Radiology: Untrue Perceptions in Medical Images and Their Implications for Diagnostic Accuracy

et al. 2021

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Errors in radiologic interpretation are largely the result of failures of perception. This remains true despite the increasing use of computer-aided detection and diagnosis. We surveyed the literature on visual illusions during the viewing of radiologic images. Misperception of anatomical structures is a potential cause of error that can lead to patient harm if disease is seen when none is present. However, visual illusions can also help enhance the ability of radiologists to detect and characterize abnormalities. Indeed, radiologists have learned to exploit certain perceptual biases in diagnostic findings and as training tools. We propose that further detailed study of radiologic illusions would help clarify the mechanisms underlying radiologic performance and provide additional heuristics to improve radiologist training and reduce medical error.

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Error in radiology—where are we now?

Abstract: Error is inherent in radiological practice. Our awareness of the extent of this and the reasons behind it has increased in recent times. Our next step must be the development of a shared understanding with our patients of the limitations as well as the huge benefits of medical imaging.

Cited by 18 publications

References 9 publications

Radiologists and Clinical Trials: Part 1 The Truth About Reader Disagreements

Radiologists and Clinical Trials: Part 1 The Truth About Reader Disagreements

Radiology Reporting Errors: Learning from Report Addenda

Visual Illusions in Radiology: Untrue Perceptions in Medical Images and Their Implications for Diagnostic Accuracy

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