ArticleJournal of digital imaging2015
Improving Radiology Report Quality by Rapidly Notifying Radiologist of Report Errors.
Article in Journal of digital imaging, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 3 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Improving Clinical Documentation with Artificial Intelligence: A Systematic Review.Perspectives in health information management · 2024Pooled it
- A systematic review of natural language processing applied to radiology reports.BMC medical informatics and decision making · 2021Pooled it
- Speech recognition for clinical documentation from 1990 to 2018: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2019Pooled it
- Error Detection and Correction in Chinese Radiology Reports Using Large Language Models: Real-World Clinical Validation Study.Journal of medical Internet research · 2026Article
- Reasoning Model-Assisted Second-Reader Quality Control of Chinese-Language Ultrasound Reports: A Retrospective Imaging Informatics Study.Journal of imaging informatics in medicine · 2026Article
- AI application in radiology beyond imaging: What is the role of text-based large language models?European radiology · 2026Article
- Improving radiology reporting accuracy: use of GPT-4 to reduce errors in reports.Abdominal radiology (New York) · 2026Article
- Detecting Laterality Errors in Combined Radiographic Studies by Enhancing the Traditional Approach With GPT-4o: Algorithm Development and Multisite Internal Validation.JMIR formative research · 2025Article
- Article
- Improving the quality of Persian clinical text with a novel spelling correction system.BMC medical informatics and decision making · 2024Article
- Streamlining Radiology Workflows Through the Development and Deployment of Automated Microservices.Journal of imaging informatics in medicine · 2024Article
- Generative Large Language Models for Detection of Speech Recognition Errors in Radiology Reports.Radiology. Artificial intelligence · 2024Article
- Using automated methods to detect safety problems with health information technology: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Application of a Domain-specific BERT for Detection of Speech Recognition Errors in Radiology Reports.Radiology. Artificial intelligence · 2022Article
- Radiology Reporting Errors: Learning from Report Addenda.The Indian journal of radiology & imaging · 2021Article
- Revealing the most common reporting errors through data mining of the report proofreading process.European radiology · 2021Article
- Automated Misspelling Detection and Correction in Persian Clinical Text.Journal of digital imaging · 2020Article
- Effectiveness of Deep Learning Algorithms to Determine Laterality in Radiographs.Journal of digital imaging · 2019Article
- Detecting Technical Image Quality in Radiology Reports.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2018Article
- 'WhenThe Ulster medical journal · 2018Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 gender-specific 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.