PATIENT FACING SYSTEMS A usability framework for speech recognition technologies in clinical handover: A pre-implementation study Linda Dawson & Maree Johnson & Hanna Suominen & Jim Basilakis & Paula Sanchez & Dominique Estival & Barbara Kelly & Leif Hanlen Received: 29 October 2013 /Accepted: 24 April 2014 /Published online: 15 May 2014 # Springer Science+Business Media New York 2014 Abstract A multi-disciplinary research team is undertaking a trial of speech-to-text (STT) technology for clinical handover management. Speech-to-text technologies allow for the cap- ture of handover data from voice recordings using speech recognition software and systems. The text documents created from this system can be used together with traditional hand- over notes and checklists to enhance the depth and breadth of data available for clinical decision-making at the point of care and so improve patient care and reduce medical errors. This paper reports on a preliminary study of perceived usability by nurses of speech-to-text technology based on interviews at a “test day” and using a user-task-technology usability frame- work to explore expectations of nurses of the use of speech-to- text (STT) technology for clinical handover. The results of this study will be used to design field studies to test the use of speech-to-text (STT) technologies at the point of care in several hospital settings. Keywords Usability . Clinical handover . Speech recognition . Nursing . Action research Introduction Context of the study The study presented in this paper is part of a larger study where a multi-disciplinary research team, comprising nurses, nurse educators, IT specialists and social linguists, is undertaking a trial of speech-to-text (STT) technology for clinical handover management. This preliminary study aims to identify expec- tations of new users of speech-to-text technologies for clinical handover including expected benefits, costs, advantages, dis- advantages and changes in work practices. Speech-to-text technologies allow for the capture of handover data from voice recordings using speech recognition software and systems. The text documents created from this system can be used together with traditional handover notes and checklists to enhance the depth and breadth of data available for clinical decision-making at the point of care and so improve patient care and reduce medical errors. The objectives of this smaller study are to explore speech-to-text hardware and software with nurses who are experienced with traditional clinical handover procedures. Other publications [1] report on the technical perspectives such as hardware and voice quality, software, algorithms and machine learning, word accuracy, etc. This article is part of the Topical Collection on Patient Facing Systems L. Dawson (*) Faculty of Social Sciences, University of Wollongong, Wollongong, NSW, Australia e-mail: lindad@uow.edu.au H. Suominen : L. Hanlen 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, ACT, Australia M. Johnson Centre for Applied Nursing Research (a joint facility of the South Western Sydney Local Health District and the University of Western Sydney), Affiliated with the Ingham Institute Applied Medical Research, University of Western Sydney, Sydney, NSW, Australia J. Basilakis University of Western Sydney, Sydney, NSW, Australia P. Sanchez Centre for Applied Nursing Research, Sydney, Australia D. Estival The MARCS Institute, University of Western Sydney, Sydney, NSW, Australia B. Kelly School of Languages and Linguistics, The University of Melbourne, Melbourne, VIC, Australia J Med Syst (2014) 38:56 DOI 10.1007/s10916-014-0056-7