title: An intelligent listening framework for capturing encounter notes from a doctor-patient dialog.
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.
  
  
  CONCLUSIONS
  The work here encouraged us that the goal is reachable, so we conclude with proposed next steps.Some challenging steps include acquiring a corpus of doctor-patient conversations, exploring a workable microphone setup, performing user interface research, and developing a multi-speaker version of our tools.
authors.affiliation: Medical Informatics, Regenstrief Institute, Indianapolis, IN 46202, USA. jklann@regenstrief.org
authors.forename: Jeffrey G
authors.initials: JG
authors.lastname: Klann
authors.forename: Peter
authors.initials: P
authors.lastname: Szolovits
mesh_terms: D003142:Communication; D057286:Electronic Health Records; D006801:Humans; D009323:Natural Language Processing; D010817:Physician-Patient Relations; D011381:Programming Languages; D012985:Software Design; D049250:Speech Recognition Software