The Medical Scribe: Corpus Development and Model Performance Analyses

Amanda Perry
Ashley Robson Domin
Chris Co
Hagen Soltau
Justin Stuart Paul
Lauren Keyes
Linh Tran
Mark David Knichel
Mingqiu Wang
Nan Du
Rayman Huang
Proc. Language Resources and Evaluation, 2020


There has been a growing interest in creating tools to assist clinical note generation from the audio of provider-patient encounters. Motivated by this goal and with the help of providers and experienced medical scribes, we developed an annotation scheme to extract relevant clinical concepts. Using this annotation scheme, a corpus of about 6k clinical encounters was labeled, which was used to train a state-of-the-art tagging model. We report model performance and a detailed analyses of the results.