Reporting Scan Time Reduces Cardiac MR Examination Duration

Lumish, Heidi; Sidhu, Manavjot S.; Kallianos, Kimberly; Brady, Thomas J.; Hoffmann, Udo; Ghoshhajra, Brian B.

doi:10.1016/j.jacr.2013.05.037

Cited by 6 publications

(6 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our study not only documents report error rates at our institution but also details an automated notification system functioning as a quality improvement intervention that significantly decreased report error rates, significantly improved correction of report errors and thus the accuracy of the clinical record, and thereby positively impacted patient management. The fact that potential error detection and confirmed error rates decreased significantly after initiation of the notification system suggests the heightened awareness of these errors causes radiologists to more carefully avoid these errors, similar to those findings by Lumish et al [20]. In fact, several radiologists have told us anecdotally that they are much more careful to avoid gender and laterality errors on particular procedures which can be problematic after having received several notifications.…”

Section: Discussionsupporting

confidence: 74%

See 1 more Smart Citation

Improving Radiology Report Quality by Rapidly Notifying Radiologist of Report Errors

2015

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionsupporting

confidence: 74%

“…Luetmer [1] and Sangwaiga [13] have also reported on laterality error rates in radiology reports. Additionally, it has been shown in related fields that simply increasing awareness of a quality problem is enough to induce positive change without any other intervention [20].…”

Section: Introductionmentioning

confidence: 99%

Improving Radiology Report Quality by Rapidly Notifying Radiologist of Report Errors

2015

View full text Add to dashboard Cite

show abstract

“…It has been well demonstrated in similar cases such as flagging report errors or providing feedback on exam duration that analogous continuous quality assurance feedback results in consistent error correction and lower baseline error rates. Immediate and consistent feedback applications hold the potential to improve awareness and baseline functioning and are essential in a field where error must be minimized [16, 17].…”

Section: Discussionmentioning

confidence: 99%

Effectiveness of Deep Learning Algorithms to Determine Laterality in Radiographs

Filice

Frantz

2019

J Digit Imaging

View full text Add to dashboard Cite

Develop a highly accurate deep learning model to reliably classify radiographs by laterality. Digital Imaging and Communications in Medicine (DICOM) data for nine body parts was extracted retrospectively. Laterality was determined directly if encoded properly or inferred using other elements. Curation confirmed categorization and identified inaccurate labels due to human error. Augmentation enriched training data to semi-equilibrate classes. Classification and object detection models were developed on a dedicated workstation and tested on novel images. Receiver operating characteristic (ROC) curves, sensitivity, specificity, and accuracy were calculated. Study-level accuracy was determined and both were compared to human performance. An ensemble model was tested for the rigorous use-case of automatically classifying exams retrospectively. The final classification model identified novel images with an ROC area under the curve (AUC) of 0.999, improving on previous work and comparable to human performance. A similar ROC curve was observed for per-study analysis with AUC of 0.999. The object detection model classified images with accuracy of 99% or greater at both image and study level. Confidence scores allow adjustment of sensitivity and specificity as needed; the ensemble model designed for the highly specific use-case of automatically classifying exams was comparable and arguably better than human performance demonstrating 99% accuracy with 1% of exams unchanged and no incorrect classification. Deep learning models can classify radiographs by laterality with high accuracy and may be applied in a variety of settings that could improve patient safety and radiologist satisfaction. Rigorous use-cases requiring high specificity are achievable.

show abstract

“…It has also been shown that continuous feedback generally improves and sustains performance [9], and without such feedback, human nature is to accomplish the goal in the most straightforward manner possible. Indeed, we performed a manual quality improvement project at our institution several years prior to this work to optimize lens exclusion in head CT examinations for accreditation purposes; during the project, performance improved substantially but because feedback was manual and cumbersome, it was not sustained and performance returned to baseline when the project concluded.…”

Section: Introductionmentioning

confidence: 99%

Lens Identification to Prevent Radiation-Induced Cataracts Using Convolutional Neural Networks

Filice

2019

J Digit Imaging

View full text Add to dashboard Cite

Exposure of the lenses to direct ionizing radiation during computed tomography (CT) examinations predisposes patients to cataract formation and should be avoided when possible. Avoiding such exposure requires positioning and other maneuvers by technologists that can be challenging. Continuous feedback has been shown to sustain quality improvement and can remind and encourage technologists to comply with these methods. Previously, for use cases such as this, cumbersome manual techniques were required for such feedback. Modern deep learning methods utilizing convolutional neural networks (CNNs) can be used to develop models that can detect lenses in CT examinations. These models can then be used to facilitate automatic and continuous feedback to sustain technologist performance for this task, thus contributing to higher quality patient care. This continuous evaluation for quality purposes also surfaces other operational or process-based challenges that can be addressed. Given high-performance characteristics, these models could also be used for other tasks such as population health research.

show abstract

Reporting Scan Time Reduces Cardiac MR Examination Duration

Cited by 6 publications

References 14 publications

Improving Radiology Report Quality by Rapidly Notifying Radiologist of Report Errors

Improving Radiology Report Quality by Rapidly Notifying Radiologist of Report Errors

Effectiveness of Deep Learning Algorithms to Determine Laterality in Radiographs

Lens Identification to Prevent Radiation-Induced Cataracts Using Convolutional Neural Networks

Contact Info

Product

Resources

About