Clinical Predictive Keyboard using Statistical and Neural Language Modeling
This work addresses the need to expedite clinical documentation for physicians, potentially reducing hospital infections, but it is incremental as it applies existing language modeling techniques to a specific domain.
The paper tackled the problem of assisting physicians in writing clinical reports by developing a predictive keyboard using language models, achieving up to 51.3% accuracy in predicting the next word in radiology reports.
A language model can be used to predict the next word during authoring, to correct spelling or to accelerate writing (e.g., in sms or emails). Language models, however, have only been applied in a very small scale to assist physicians during authoring (e.g., discharge summaries or radiology reports). But along with the assistance to the physician, computer-based systems which expedite the patient's exit also assist in decreasing the hospital infections. We employed statistical and neural language modeling to predict the next word of a clinical text and assess all the models in terms of accuracy and keystroke discount in two datasets with radiology reports. We show that a neural language model can achieve as high as 51.3% accuracy in radiology reports (one out of two words predicted correctly). We also show that even when the models are employed only for frequent words, the physician can save valuable time.