Interactive NLP in Clinical Care: Identifying Incidental Findings in Radiology Reports

• Published in: Applied clinical informatics Vol. 10; no. 4; pp. 655 - 669
• Main Authors: Trivedi, Gaurav, Dadashzadeh, Esmaeel R., Handzel, Robert M., Chapman, Wendy W., Visweswaran, Shyam, Hochheiser, Harry
• Format: Journal Article
• Language: English
• Published: Stuttgart · New York Georg Thieme Verlag KG 01-08-2019
• Subjects: Data Mining - methods; Humans; Incidental Findings; Natural Language Processing; Radiology; Research Article; Research Report; User-Computer Interface; data display; medical records systems; statistical; data interpretation; computerized; workflow
• ISSN: 1869-0327; 1869-0327
• DOI: 10.1055/s-0039-1695791

Abstract
Background
 Despite advances in natural language processing (NLP), extracting information from clinical text is expensive. Interactive tools that are capable of easing the construction, review, and revision of NLP models can reduce this cost and improve the utility of clinical reports for clinical and secondary use.
Objectives
 We present the design and implementation of an interactive NLP tool for identifying incidental findings in radiology reports, along with a user study evaluating the performance and usability of the tool.
Methods
 Expert reviewers provided gold standard annotations for 130 patient encounters (694 reports) at sentence, section, and report levels. We performed a user study with 15 physicians to evaluate the accuracy and usability of our tool. Participants reviewed encounters split into intervention (with predictions) and control conditions (no predictions). We measured changes in model performance, the time spent, and the number of user actions needed. The System Usability Scale (SUS) and an open-ended questionnaire were used to assess usability.
Results
 Starting from bootstrapped models trained on 6 patient encounters, we observed an average increase in F1 score from 0.31 to 0.75 for reports, from 0.32 to 0.68 for sections, and from 0.22 to 0.60 for sentences on a held-out test data set, over an hour-long study session. We found that tool helped significantly reduce the time spent in reviewing encounters (134.30 vs. 148.44 seconds in intervention and control, respectively), while maintaining overall quality of labels as measured against the gold standard. The tool was well received by the study participants with a very good overall SUS score of 78.67.
Conclusion
 The user study demonstrated successful use of the tool by physicians for identifying incidental findings. These results support the viability of adopting interactive NLP tools in clinical care settings for a wider range of clinical applications.