Author:
Wu Y.,Denny J. C.,Rosenbloom S. T.,Miller R. A.,Giuse D. A.,Song M.,Xu H.
Abstract
SummaryObjective: To save time, healthcare providers frequently use abbreviations while authoring clinical documents. Nevertheless, abbreviations that authors deem unambiguous often confuse other readers, including clinicians, patients, and natural language processing (NLP) systems. Most current clinical NLP systems “post-process” notes long after clinicians enter them into electronic health record systems (EHRs). Such post-processing cannot guarantee 100% accuracy in abbreviation identification and disambiguation, since multiple alternative interpretations exist.Methods: Authors describe a prototype system for real-time Clinical Abbreviation Recognition and Disambiguation (rCARD) – i.e., a system that interacts with authors during note generation to verify correct abbreviation senses. The rCARD system design anticipates future integration with web-based clinical documentation systems to improve quality of healthcare records. When clinicians enter documents, rCARD will automatically recognize each abbreviation. For abbreviations with multiple possible senses, rCARD will show a ranked list of possible meanings with the best predicted sense at the top. The prototype application embodies three word sense disambiguation (WSD) methods to predict the correct senses of abbreviations. We then conducted three experments to evaluate rCARD, including 1) a performance evaluation of different WSD methods; 2) a time evaluation of real-time WSD methods; and 3) a user study of typing clinical sentences with abbreviations using rCARD.Results: Using 4,721 sentences containing 25 commonly observed, highly ambiguous clinical abbreviations, our evaluation showed that the best profile-based method implemented in rCARD achieved a reasonable WSD accuracy of 88.8% (comparable to SVM – 89.5%) and the cost of time for the different WSD methods are also acceptable (ranging from 0.630 to 1.649 milliseconds within the same network). The preliminary user study also showed that the extra time costs by rCARD were about 5% of total document entry time and users did not feel a significant delay when using rCARD for clinical document entry.Conclusion: The study indicates that it is feasible to integrate a real-time, NLP-enabled abbreviation recognition and disambiguation module with clinical documentation systems.Citation: Wu Y, Denny JC, Rosenbloom ST, Miller RA, Giuse DA, Song M, Xu H. A preliminary study of clinical abbreviation disambiguation in real time. Appl Clin Inf 2015; 6: 364–374http://dx.doi.org/10.4338/ACI-2014-10-RA-0088
Subject
Health Information Management,Computer Science Applications,Health Informatics
Reference23 articles.
1. Stetson PD, Johnson SB, Scotch M, Hripcsak G. The sublanguage of cross-coverage. Proc AMIA Symp 2002: 742-746
2. Xu H, Stetson PD, Friedman C. A study of abbreviations in clinical notes. AMIA Annu Symp Proc 2007: 821-825
3. Dawson Kp Fau - Capaldi N, Capaldi N Fau - Haydon M, Haydon M Fau - Penna AC, Penna AC. The paediatric hospital medical record: a quality assessment. 19920731 DCOM- 19920731(0726-3139 (Print))
4. Medical abbreviations: writing little and communicating less
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