Capturing accurate and machine-interpretable primary data from clinical encounters is a challenging task, yet critical to the integrity of the practice of medicine. We explore the intriguing possibility that technology can help accurately capture structured data from the clinical encounter using a combination of automated speech recognition (ASR) systems and tools for extraction of clinical meaning from narrative medical text. Our goal is to produce a displayed evolving encounter note, visible and editable (using speech) during the encounter. This is very ambitious, and so far we have taken only the most preliminary steps. Here we report a simple proof-of-concept system and the design of the more comprehensive one we are building, discussing both the engineering design and challenges encountered. Without a formal evaluation, we were encouraged by our initial results, so we conclude with proposed next steps.
['Jeffrey G. Klann', 'Peter Szolovits']
An intelligent listening framework for capturing encounter notes from a doctor-patient dialog