Abstract
Critical results reporting guidelines demand that certain critical findings are communicated to the responsible provider within a specific period of time. In this paper, we discuss a generic report processing pipeline to extract critical findings within the dictated report to allow for automation of quality and compliance oversight using a production dataset containing 1,210,858 radiology exams. Algorithm accuracy on an annotated dataset having 327 sentences was 91.4% (95% CI 87.6–94.2%). Our results show that most critical findings are diagnosed on CT and MR exams and that intracranial hemorrhage and fluid collection are the most prevalent at our institution. 1.6% of the exams were found to have at least one of the ten critical findings we focused on. This methodology can enable detailed analysis of critical results reporting for research, workflow management, compliance, and quality assurance.
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References
Gordon, J.R., et al., Failure to recognize newly identified aortic dilations in a health care system with an advanced electronic medical record. Ann Intern Med, 2009. 151(1): p. 21-7, W5.
The Joint Commission, National Patient Safety Goals Effective January 2018. 2018.
Khorasani, R., Optimizing communication of critical test results. J Am Coll Radiol, 2009. 6(10): p. 721-3.
American College of Radiology, ACR Practice Parameter for Communication of Diagnostic Imaging Findings. 2014.
Larson, P.A., et al., Actionable findings and the role of IT support: report of the ACR Actionable Reporting Work Group. J Am Coll Radiol, 2014. 11(6): p. 552-8.
Rajpurkar P. I. J., Zhu K, Yang B, Mehta H, Duan T, et al. CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning. 2017 [cited 2020 Feb 2]; Available from: https://arxiv.org/abs/1711.05225.
Critical Results Reporting. 2011 [cited 2020 Jan 25]; Available from: http://depts.washington.edu/pacshelp/docs/Crit_Res_rpt.html.
Lakhani, P., W. Kim, and C.P. Langlotz, Automated detection of critical results in radiology reports. J Digit Imaging, 2012. 25(1): p. 30-6.
Lacson, R., et al., Retrieval of radiology reports citing critical findings with disease-specific customization. Open Med Inform J, 2012. 6: p. 28-35.
Tiwari, A., et al., Automatic classification of critical findings in radiology reports, in Proceedings of The First Workshop Medical Informatics and Healthcare held with the 23rd SIGKDD Conference on Knowledge Discovery and Data Mining, F. Samah and R. Daniela Stan, Editors. 2017, PMLR: Proceedings of Machine Learning Research. p. 35--39.
Chang, P., et al., Deep-learning convolutional neural networks accurately classify genetic mutations in gliomas. AJNR Am J Neuroradiol, 2018. 39(7): p. 1201-1207.
Lakhani, P. and B. Sundaram, Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology, 2017. 284(2): p. 574-582.
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Authors would like to acknowledge the contributions from Dr. Martin Gunn (Professor, and Vice Chair of Informatics at UW Radiology) for all his support and guidance on this work.
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Mabotuwana, T., Hall, C.S. & Cross, N. Framework for Extracting Critical Findings in Radiology Reports. J Digit Imaging 33, 988–995 (2020). https://doi.org/10.1007/s10278-020-00349-7
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DOI: https://doi.org/10.1007/s10278-020-00349-7