An intelligent listening framework for capturing encounter notes from a doctor-patient dialog
dc.contributor.author | Klann, Jeffrey G. | |
dc.contributor.author | Szolovits, Peter | |
dc.contributor.department | BioHealth Informatics, School of Informatics and Computing | en_US |
dc.date.accessioned | 2021-01-25T20:42:27Z | |
dc.date.available | 2021-01-25T20:42:27Z | |
dc.date.issued | 2008-11-03 | |
dc.description.abstract | Background 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_US |
dc.eprint.version | Final published version | en_US |
dc.identifier.citation | Klann, 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. | en_US |
dc.identifier.uri | https://hdl.handle.net/1805/24979 | |
dc.language.iso | en_US | en_US |
dc.publisher | BioMed Central | en_US |
dc.relation.isversionof | 10.1186/1472-6947-9-S1-S3 | en_US |
dc.relation.journal | BMC Medical Informatics and Decision Making | en_US |
dc.rights | Attribution 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.source | Publisher | en_US |
dc.subject | Application Programming Interface | en_US |
dc.subject | Automate Speech Recognition | en_US |
dc.subject | Clinical Encounter | en_US |
dc.title | An intelligent listening framework for capturing encounter notes from a doctor-patient dialog | en_US |
dc.type | Article | en_US |