title: Capturing patient information at nursing shift changes: methodological evaluation of speech recognition and information extraction.
abstract: OBJECTIVE
  We study the use of speech recognition and information extraction to generate drafts of Australian nursing-handover documents.
  
  
  METHODS
  Speech recognition correctness and clinicians' preferences were evaluated using 15 recorder-microphone combinations, six documents, three speakers, Dragon Medical 11, and five survey/interview participants. Information extraction correctness evaluation used 260 documents, six-class classification for each word, two annotators, and the CRF++ conditional random field toolkit.
  
  
  RESULTS
  A noise-cancelling lapel-microphone with a digital voice recorder gave the best correctness (79%). This microphone was also the most preferred option by all but one participant. Although the participants liked the small size of this recorder, their preference was for tablets that can also be used for document proofing and sign-off, among other tasks. Accented speech was harder to recognize than native language and a male speaker was detected better than a female speaker. Information extraction was excellent in filtering out irrelevant text (85% F1) and identifying text relevant to two classes (87% and 70% F1). Similarly to the annotators' disagreements, there was confusion between the remaining three classes, which explains the modest 62% macro-averaged F1.
  
  
  CONCLUSIONS
  We present evidence for the feasibility of speech recognition and information extraction to support clinicians' in entering text and unlock its content for computerized decision-making and surveillance in healthcare.
  
  
  CONCLUSIONS
  The benefits of this automation include storing all information; making the drafts available and accessible almost instantly to everyone with authorized access; and avoiding information loss, delays, and misinterpretations inherent to using a ward clerk or transcription services.
authors.affiliation: Machine Learning Research Group, NICTA, College of Engineering and Computer Science, The Australian National University, Faculty of Health, University of Canberra, and Department of Information Technology, University of Turku, Canberra, Australian Capital Territory, Australia.
authors.forename: Hanna
authors.initials: H
authors.lastname: Suominen
authors.affiliation: Research Faculty of Health Sciences, Australian Catholic University, Sydney, New South Wales, Australia.
authors.forename: Maree
authors.initials: M
authors.lastname: Johnson
authors.affiliation: Machine Learning Research Group, NICTA, Canberra, Australian Capital Territory, Australia.
authors.forename: Liyuan
authors.initials: L
authors.lastname: Zhou
authors.affiliation: Centre for Applied Nursing Research (University of Western Sydney and South Western Sydney Local Health District), Sydney, New South Wales, Australia.
authors.forename: Paula
authors.initials: P
authors.lastname: Sanchez
authors.affiliation: Institute of Estonian and General Linguistics, University of Tartu, Tartu, Estonia.
authors.forename: Raul
authors.initials: R
authors.lastname: Sirel
authors.affiliation: School of Computing, Engineering and Mathematics, University of Western Sydney, Sydney, New South Wales, Australia.
authors.forename: Jim
authors.initials: J
authors.lastname: Basilakis
authors.affiliation: Machine Learning Research Group, NICTA, College of Engineering and Computer Science, The Australian National University, Faculty of Health, University of Canberra, Canberra, Australian Capital Territory, Australia.
authors.forename: Leif
authors.initials: L
authors.lastname: Hanlen
authors.affiliation: The MARCS Institute, University of Western Sydney and Department of Linguistics, University of Sydney, Sydney, New South Wales, Australia.
authors.forename: Dominique
authors.initials: D
authors.lastname: Estival
authors.affiliation: Faculty of Social Sciences, University of Wollongong, Wollongong, New South Wales, Australia.
authors.forename: Linda
authors.initials: L
authors.lastname: Dawson
authors.affiliation: School of Languages and Linguistics, The University of Melbourne, Melbourne, Victoria, Australia.
authors.forename: Barbara
authors.initials: B
authors.lastname: Kelly
mesh_terms: D001315:Australia; D020000:Decision Support Systems, Clinical; D005240:Feasibility Studies; D005260:Female; D006801:Humans; D008297:Male; D009736:Nursing Process / Q000458:organization & administration*; D062209:Patient Handoff; D049250:Speech Recognition Software
keywords: computer systems evaluation; information extraction; nursing records; patient handoff; speech recognition software