Preventing Errors in Laterality

Landau, Elliot; Hirschorn, David; Koutras, Iakovos; Malek, Alexander; Demissie, Seleshie

doi:10.1007/s10278-014-9738-4

Cited by 9 publications

(13 citation statements)

References 4 publications

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“…The cumulative detected laterality and gender mismatch error rates were 0.04 % (141/391,657) and 0.07 % (260/391,657), respectively (Table 2). Our laterality error rate is much higher than previously published data by Sangwaiya (0.00008 %) [13] and Landau (0.0007 %) [19] and more in line with the rate described by Luetmer (0.055 %) [1]. The differences compared to Sangwaiya and Landau are likely attributed to differences in study design and inclusion criteria.…”

Section: Resultscontrasting

confidence: 55%

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Improving Radiology Report Quality by Rapidly Notifying Radiologist of Report Errors

2015

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Radiology report errors occur for many reasons including the use of pre-filled report templates, wrong-word substitution, nonsensical phrases, and missing words. Reports may also contain clinical errors that are not specific to the speech recognition including wrong laterality and genderspecific discrepancies. Our goal was to create a custom algorithm to detect potential gender and laterality mismatch errors and to notify the interpreting radiologists for rapid correction. A JavaScript algorithm was devised to flag gender and laterality mismatch errors by searching the text of the report for keywords and comparing them to parameters within the study's HL7 metadata (i.e., procedure type, patient sex). The error detection algorithm was retrospectively applied to 82, 353 reports 4 months prior to its development and then prospectively to 309,304 reports 15 months after implementation. Flagged reports were reviewed individually by two radiologists for a true gender or laterality error and to determine if the errors were ultimately corrected. There was significant improvement in the number of flagged reports (pre, 198/82,353 [0.24 %]; post, 628/309,304 [0.20 %]; P=0.04) and reports containing confirmed gender or laterality errors (pre, 116/82, 353 [0.014 %]; post, 285/309,304 [0.09 %]; P<0.0001) after implementing our error notification system. The number of flagged reports containing an error that were ultimately corrected improved dramatically after implementing the notification system (pre, 17/116 [15 %]; post, 239/285 [84 %]; P<0.0001). We developed a successful automated tool for detecting and notifying radiologists of potential gender and laterality errors, allowing for rapid report correction and reducing the overall rate of report errors.

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Section: Resultscontrasting

confidence: 55%

“…Landau et al developed a system to reduce laterality errors where radiologists have the opportunity to re-review the report prior to signature in a separate window that displays terms of laterality highlighted in separate colors [19]. Luetmer [1] and Sangwaiga [13] have also reported on laterality error rates in radiology reports.…”

Section: Introductionmentioning

confidence: 99%

Improving Radiology Report Quality by Rapidly Notifying Radiologist of Report Errors

2015

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“…Our study establishes the infrequent but quantifiable prevalence of wrong side errors in the radiology reports and applies systemic clinical improvement process strategies to implement mitigating interventions and to assess their impact. The infrequent occurrence of side errors in radiology procedures and reports have been reported in prior publications (5,8,12). Likewise, CT as the dominant contributory modality towards these errors has also been observed (5).…”

Section: Discussionmentioning

confidence: 84%

“…Prior studies have reported the prevalence of errors related to laterality or left and right discrepancies in radiology and non-radiology specialties (5)(6)(7)(8). While surgical and procedural errors related to wrong side treatment are rare, side discrepancies in radiology reports can contribute to these errors leading to catastrophic consequences for both physicians and patients (5,7,(9)(10)(11)(12). We defined laterality errors related to side discrepancies as lack of consistent side labeling of an abnormality between the "Findings" and "Impression" sections of the radiology reports.…”

Section: Introductionmentioning

confidence: 99%

Process improvement for reducing side discrepancies in radiology reports

Digumarthy

Vining

Tabari

et al. 2018

Acta Radiologica Open

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BackgroundLaterality errors in radiology reports can lead to serious errors in management.PurposeTo reduce errors related to side discrepancies in radiology reports from thoracic imaging by 50% over a six-month period with education and voice recognition software tools.Material and MethodsAll radiology reports at the Thoracic Imaging Division from the fourth quarter of 2016 were reviewed manually for presence of side discrepancies (baseline data). Side discrepancies were defined as a lack of consistency in side labeling of any abnormality in the “Findings” to “Impression” sections of the reports. Process map and Ishikawa fishbone diagram (Microsoft Visio) were created. All thoracic radiologists were educated on side-related errors in radiology reports for plan–design–study–act cycle 1 (PDSA #1). Two weeks later, voice recognition software was configured to capitalize sides (RIGHT and LEFT) in the reports during dictated (PDSA# 2). Radiology reports were analyzed to determine side-discrepancy errors following each PDSA cycle (post-interventional data). Statistical run charts were created using QI Macros statistical software.ResultsBaseline data revealed 33 side-discrepancy errors in 47,876 reports with an average of 2.5 errors per week (range = 1–8 errors). Following PDSA #1, there were seven errors pertaining to side discrepancies over a two-week period. Errors declined following implementation of PDSA #2 to meet the target of 0.85 side-discrepancy error per week over seven weeks.ConclusionAutomated processes (such as capitalization of sides) help reduce left/right errors substantially without affecting reporting turnaround time.

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“…Several custom-developed software solutions aiming to decrease this error type have been devised. These include color-coded laterality discriminator crosschecks prior to report signing [16] and algorithms comparing report content to patients' Health Level 7 metadata [17]. However, no software solution is available to detect discrepancies between findings and impression report sections in real time.…”

Section: Discussionmentioning

confidence: 99%

Revealing the most common reporting errors through data mining of the report proofreading process

et al. 2020

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Objectives To investigate the most common errors in residents’ preliminary reports, if structured reporting impacts error types and frequencies, and to identify possible implications for resident education and patient safety. Material and methods Changes in report content were tracked by a report comparison tool on a word level and extracted for 78,625 radiology reports dictated from September 2017 to December 2018 in our department. Following data aggregation according to word stems and stratification by subspecialty (e.g., neuroradiology) and imaging modality, frequencies of additions/deletions were analyzed for findings and impression report section separately and compared between subgroups. Results Overall modifications per report averaged 4.1 words, with demonstrably higher amounts of changes for cross-sectional imaging (CT: 6.4; MRI: 6.7) than non-cross-sectional imaging (radiographs: 0.2; ultrasound: 2.8). The four most frequently changed words (right, left, one, and none) remained almost similar among all subgroups (range: 0.072–0.117 per report; once every 9–14 reports). Albeit representing only 0.02% of analyzed words, they accounted for up to 9.7% of all observed changes. Subspecialties solely using structured reporting had substantially lower change ratios in the findings report section (mean: 0.2 per report) compared with prose-style reporting subspecialties (mean: 2.0). Relative frequencies of the most changed words remained unchanged. Conclusion Residents’ most common reporting errors in all subspecialties and modalities are laterality discriminator confusions (left/right) and unnoticed descriptor misregistration by speech recognition (one/none). Structured reporting reduces overall error rates, but does not affect occurrence of the most common errors. Increased error awareness and measures improving report correctness and ensuring patient safety are required. Key Points • The two most common reporting errors in residents’ preliminary reports are laterality discriminator confusions (left/right) and unnoticed descriptor misregistration by speech recognition (one/none). • Structured reporting reduces the overall the error frequency in the findings report section by a factor of 10 (structured reporting: mean 0.2 per report; prose-style reporting: 2.0) but does not affect the occurrence of the two major errors. • Staff radiologist review behavior noticeably differs between radiology subspecialties.

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Preventing Errors in Laterality

Cited by 9 publications

References 4 publications

Improving Radiology Report Quality by Rapidly Notifying Radiologist of Report Errors

Improving Radiology Report Quality by Rapidly Notifying Radiologist of Report Errors

Process improvement for reducing side discrepancies in radiology reports

Revealing the most common reporting errors through data mining of the report proofreading process

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