Assisting radiologists with reporting urgent findings to referring physicians: A machine learning approach to identify cases for prompt communication

• Published in: Journal of biomedical informatics 2019-05, Vol.93, p.103169-103169, Article 103169
• Main Authors: Meng, Xing, Ganoe, Craig H., Sieberg, Ryan T., Cheung, Yvonne Y., Hassanpour, Saeed
• Format: Article
• Language: English
• Subjects: Cluster analysis; Distributional semantics; Radiologist prompt communication; Radiology report; Semi-supervised learning
• ISSN: 1532-0464; 1532-0480
• DOI: 10.1016/j.jbi.2019.103169

[Display omitted] •The proposed approach can identify radiological cases for prompt communication.•Our semi-unsupervised methodology relies on a limited amount of labeled data.•Cluster-based approach outperforms the comparative nearest neighbor-based method.•Our evaluation showed the generalizability of our approach across institutions.•Our approach can improve communication between radiologists and clinicians. Radiologists are expected to expediently communicate critical and unexpected findings to referring clinicians to prevent delayed diagnosis and treatment of patients. However, competing demands such as heavy workload along with lack of administrative support resulted in communication failures that accounted for 7% of the malpractice payments made from 2004 to 2008 in the United States. To address this problem, we have developed a novel machine learning method that can automatically and accurately identify cases that require prompt communication to referring physicians based on analyzing the associated radiology reports. This semi-supervised learning approach requires a minimal amount of manual annotations and was trained on a large multi-institutional radiology report repository from three major external healthcare organizations. To test our approach, we created a corpus of 480 radiology reports from our own institution and double-annotated cases that required prompt communication by two radiologists. Our evaluation on the test corpus achieved an F-score of 74.5% and recall of 90.0% in identifying cases for prompt communication. The implementation of the proposed approach as part of an online decision support system can assist radiologists in identifying radiological cases for prompt communication to referring physicians to avoid or minimize potential harm to patients.