Klann, Jeffrey G.Szolovits, Peter2021-01-252021-01-252008-11-03Klann, J. G., & Szolovits, P. (2009). An intelligent listening framework for capturing encounter notes from a doctor-patient dialog. BMC medical informatics and decision making, 9(1), 1-10.https://hdl.handle.net/1805/24979Background 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. Results This is very ambitious, and so far we have taken only the most preliminary steps. 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. The proof-of-concept, despite a few false positives, correctly recognized the proper category of single-and multi-word phrases in uncorrected ASR output. The more comprehensive system captures and transcribes speech and stores alternative phrase interpretations in an XML-based format used by a text-engineering framework. It does not yet use the framework to perform the language processing present in the proof-of-concept. Conclusion The work here encouraged us that the goal is reachable, so we conclude with proposed next steps.en-USAttribution 4.0 InternationalApplication Programming InterfaceAutomate Speech RecognitionClinical EncounterArticle