This Small Business Innovative Research SBIR Phase I project will use statistical analysis of historical medical records to create families of language models for each section of the traditional medical note and switch lexicons in and out of the automatic speech recognition (ASR) in real time based on the contextual position within the narrative note. Current speech recognition methods use a single, general-purpose medical lexicon to train a recognizer when identifying words. Medical context-specific probabilities are ignored. Because the DocTalk engine incorporates real time integrated ASR with natural language processing (NLP), there is an opportunity to utilize NLP contextual data to actually change the ongoing ASR process. This innovative text structuring method will exploit the statistical variability of language used in each section of the medical record. It is a unique opportunity to address delay in workflow; the largest barrier to a national electronic healthcare infrastructure, by using a cloud-based, open source leveraged solution. The broader impact/commercial potential of this project includes the ability of physicians to increase usable information, avoid third party transcription errors, and mitigate workflow delays. The majority of workflow delay in electronic medical records (EMR) is the need to perform manual operations to fill structured forms within the record, as opposed to simple unstructured narratives used in traditional written notes and transcriptions. Successful completion of this innovative proposed program of NLP-enhanced context based ASR will provide the accuracy required to deploy an integrated, interactive, intuitive, low-cost data entry system for small practice primary care physicians, and help overcome the largest obstacle to a national electronic healthcare infrastructure.
