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Appeared in: Proceedings of the 42nd European Conference on Information Retrieval Research (ECIR 2020)
Abstract:
Medical errors are a major public health concern and a leading cause of death worldwide. Many healthcare centers and hospitals have been using reporting systems where medical practitioners write a preliminary medical report and the report is later reviewed, revised, and finalized by a more experienced physician. The revisions range from stylistic to corrections of critical errors or misinterpretations of the case. Due to the large number of daily written reports, it is often difficult to manually and thoroughly review all the finalized reports to find such errors and learn from them. To address this challenge, we propose a novel ranking approach, consisting of textual and ontological overlaps between the preliminary and final versions of reports, to rank them based on the degree of discrepancies. This allows medical practitioners to easily identify and learn from the reports in which their interpretation most substantially differed from the attending physician's who finalized the report. This is a crucial step towards uncovering potential errors and helping medical practitioners to learn from such errors, thus improving patient-care in the long run. We evaluate our model on a dataset of radiology reports and show that our approach outperforms both previously-proposed approaches and more recent language models by 4.5% to 15.4%.
BibTeX @inproceedings{macavaney:ecir2020-radsigdiff, author = {MacAvaney, Sean and Cohan, Arman and Goharian, Nazli and Filice, Ross}, title = {Ranking Significant Discrepancies in Clinical Reports}, booktitle = {Proceedings of the 42nd European Conference on Information Retrieval Research}, year = {2020}, url = {https://link.springer.com/chapter/10.1007/978-3-030-45442-5_30}, doi = {10.1007/978-3-030-45442-5_30}, pages = {238----245} }