{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T07:21:02Z","timestamp":1777879262996,"version":"3.51.4"},"reference-count":13,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/100000133","name":"Agency for Healthcare Research and Quality","doi-asserted-by":"publisher","award":["1R03HS027680"],"award-info":[{"award-number":["1R03HS027680"]}],"id":[{"id":"10.13039\/100000133","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Proceedings of the Human Factors and Ergonomics Society Annual Meeting"],"published-print":{"date-parts":[[2023,9]]},"abstract":"<jats:p>Adverse events caused by medical errors pose a significant threat to patient safety, with estimates of 251,454 deaths and a cost of $17.1 billion to the healthcare system annually in the United States. Patient safety event (PSE) reports play a vital role in identifying measures to prevent adverse events, but their utility is dependent on the accurate classification of PSE reports. Recent studies have used static natural language processing (NLP) and machine learning (ML) techniques to automate PSE report classification. However, the use of static NLP has limitations in differentiating the meaning of words in disparate contexts, which can lead to inferior classification results. Thus, this study proposes to utilize contextual text representation produced from neural NLP methods to improve the accuracy of PSE report classification. The results suggest that the contextual text representation can further improve the performance of PSE classifiers. The best-performing classifier, a support vector machine trained with contextual text representation (Roberta-base) reaches an accuracy of 0.75 and a ROCAUC score of 0.94, surpassing all ML classifiers trained with static text representations. Furthermore, the confusion matrix of the best classifier exposes latent deficiencies in the PSE reports' classification taxonomy, such as the multi-class nature of PSE and conceptually related event types. The study's findings can save time for PSE reclassification, enhance the learning capabilities of the reporting system, ultimately improve patient safety<\/jats:p>","DOI":"10.1177\/21695067231193645","type":"journal-article","created":{"date-parts":[[2023,10,20]],"date-time":"2023-10-20T05:07:11Z","timestamp":1697778431000},"page":"1063-1069","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":4,"title":["Improving Patient Safety Event Report Classification with Machine Learning and Contextual Text Representation"],"prefix":"10.1177","volume":"67","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-5823-9406","authenticated-orcid":false,"given":"Hongbo","family":"Chen","sequence":"first","affiliation":[{"name":"Mechanical and Industrial Engineering, University of Toronto, Toronto, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5767-6683","authenticated-orcid":false,"given":"Eldan","family":"Cohen","sequence":"additional","affiliation":[{"name":"Mechanical and Industrial Engineering, University of Toronto, Toronto, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dulaney","family":"Wilson","sequence":"additional","affiliation":[{"name":"Public Health Sciences, Medical University of South Carolina, Charleston, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Myrtede","family":"Alfted","sequence":"additional","affiliation":[{"name":"Mechanical and Industrial Engineering, University of Toronto, Toronto, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2023,10,20]]},"reference":[{"key":"e_1_3_3_2_1","doi-asserted-by":"crossref","unstructured":"Chawla N. V. Bowyer K. W. Hall L. O. Kegelmeyer W. P. (2002). SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research 16 321\u2013357. https:\/\/doi.org\/10.1613\/jair.953","DOI":"10.1613\/jair.953"},{"key":"e_1_3_3_3_1","doi-asserted-by":"crossref","unstructured":"Evans H. P. Anastasiou A. Edwards A. Hibbert P. Makeham M. Luz S. Sheikh A. Donaldson L. Carson-Stevens A. (2020). Automated classification of primary care patient safety incident report content and severity using supervised machine learning (ML) approaches. Health Informatics Journal 26(4) 3123\u20133139. https:\/\/doi.org\/10.1177\/1460458219833102","DOI":"10.1177\/1460458219833102"},{"key":"e_1_3_3_4_1","doi-asserted-by":"crossref","unstructured":"Fong A. Behzad S. Pruitt Z. Ratwani R. M. (2021). A Machine Learning Approach to Reclassifying Miscellaneous Patient Safety Event Reports. Journal of Patient Safety 17(8) e829\u2013e833. https:\/\/doi.org\/10.1097\/PTS.0000000000000731","DOI":"10.1097\/PTS.0000000000000731"},{"key":"e_1_3_3_5_1","doi-asserted-by":"crossref","unstructured":"Kumar P. Bhatnagar R. Gaur K. Bhatnagar A. (2021). Classification of Imbalanced Data:Review of Methods and Applications. IOP Conference Series: Materials Science and Engineering 1099(1) 012077. https:\/\/doi.org\/10.1088\/1757-899X\/1099\/1\/012077","DOI":"10.1088\/1757-899X\/1099\/1\/012077"},{"key":"e_1_3_3_6_1","doi-asserted-by":"crossref","unstructured":"Lee K. Yoon K. Yoon B. Shin E. (2020). Differences in the perception of harm assessment among nurses in the patient safety classification system. PLoS ONE 15(12) e0243583. https:\/\/doi.org\/10.1371\/journal.pone.0243583","DOI":"10.1371\/journal.pone.0243583"},{"key":"e_1_3_3_7_1","unstructured":"Liu Q. Kusner M. J. Blunsom P. (2020). A Survey on Contextual Embeddings (arXiv:2003.07278). arXiv. https:\/\/doi.org\/10.48550\/arXiv.2003.07278"},{"key":"e_1_3_3_8_1","unstructured":"Liu Y. Ott M. Goyal N. Du J. Joshi M. Chen D. Levy O. Lewis M. Zettlemoyer L. Stoyanov V. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach (arXiv:1907.11692). arXiv. https:\/\/doi.org\/10.48550\/arXiv.1907.11692"},{"key":"e_1_3_3_9_1","doi-asserted-by":"crossref","unstructured":"Makary M. A. Daniel M. (2016). Medical error-the third leading cause of death in the US. BMJ (Clinical Research Ed.) 353 i2139. https:\/\/doi.org\/10.1136\/bmj.i2139","DOI":"10.1136\/bmj.i2139"},{"key":"e_1_3_3_10_1","doi-asserted-by":"crossref","unstructured":"Puthumana J. S. Fong A. Blumenthal J. Ratwani R. M. (2021). Making Patient Safety Event Data Actionable: Understanding Patient Safety Analyst Needs. Journal of Patient Safety 17(6) e509\u2013e514. https:\/\/doi.org\/10.1097\/PTS.0000000000000400","DOI":"10.1097\/PTS.0000000000000400"},{"key":"e_1_3_3_11_1","doi-asserted-by":"crossref","unstructured":"Sari A. B.-A. Sheldon T. A. Cracknell A. Turnbull A. Dobson Y. Grant C. Gray W. Richardson A. (2007). Extent nature and consequences of adverse events: Results of a retrospective casenote review in a large NHS hospital. BMJ Quality & Safety 16(6) 434\u2013439. https:\/\/doi.org\/10.1136\/qshc.2006.021154","DOI":"10.1136\/qshc.2006.021154"},{"key":"e_1_3_3_12_1","doi-asserted-by":"crossref","unstructured":"Rafter N. Hickey A. Condell S. Conroy R. O\u2019Connor P. Vaughan D. Williams D. (2015). 2. Adverse events in healthcare: Learning from mistakes. https:\/\/doi.org\/10.1093\/qjmed\/hcu145","DOI":"10.1093\/qjmed\/hcu145"},{"key":"e_1_3_3_13_1","doi-asserted-by":"crossref","unstructured":"Van Den Bos J. Rustagi K. Gray T. Halford M. Ziemkiewicz E. Shreve J. (2011). 3. 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