A systematic review of speech recognition technology in health care

BACKGROUND To undertake a systematic review of existing literature relating to speech recognition technology and its application within health care. METHODS A systematic review of existing literature from 2000 was undertaken. Inclusion criteria were: all papers that referred to speech recognition (SR) in health care settings, used by health professionals (allied health, medicine, nursing, technical or support staff), with an evaluation or patient or staff outcomes. Experimental and non-experimental designs were considered. Six databases (Ebscohost including CINAHL, EMBASE, MEDLINE including the Cochrane Database of Systematic Reviews, OVID Technologies, PreMED-LINE, PsycINFO) were searched by a qualified health librarian trained in systematic review searches initially capturing 1,730 references. Fourteen studies met the inclusion criteria and were retained. RESULTS The heterogeneity of the studies made comparative analysis and synthesis of the data challenging resulting in a narrative presentation of the results. SR, although not as accurate as human transcription, does deliver reduced turnaround times for reporting and cost-effective reporting, although equivocal evidence of improved workflow processes. CONCLUSIONS SR systems have substantial benefits and should be considered in light of the cost and selection of the SR system, training requirements, length of the transcription task, potential use of macros and templates, the presence of accented voices or experienced and in-experienced typists, and workflow patterns.Funding for this study was provided by the University of Western Sydney. NICTA is funded by the Australian Government through the Department of Communications and the Australian Research Council through the ICT Centre of Excellence Program. NICTA is also funded and supported by the Australian Capital Territory, the New South Wales, Queensland and Victorian Governments, the Australian National University, the University of New South Wales, the University of Melbourne, the University of Queensland, the University of Sydney, Griffith University, Queensland University of Technology, Monash University and other university partners

The Impact of Speech Recognition Systems on The Productivity and The Workflow in Radiology Departments: A Systematic Review

Speech Recognition dictation systems are becoming more popular and provide a viable alternative to conventional transcription services. On the other hand, there is a scarcity in the studies that address the impact of speech recognition on productivity and the workflow in the radiology department. To review the most updated literature in the past 10 years regarding the effects of Speech Recognition systems on the productivity and workflow of radiology departments. A systematic literature review was done using PRISMA, including 14 articles in total, and they were divided into four main themes. The finding confirmed the positive effect of Speech Recognition on departmental productivity, with decreased report turnaround time and an increase in the number of reports available per unit of time

Improving the translation environment for professional translators

When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these aspects, both from a human-computer interaction point of view, as well as from a purely technological side. This paper describes the SCATE research with respect to improved fuzzy matching, parallel treebanks, the integration of translation memories with machine translation, quality estimation, terminology extraction from comparable texts, the use of speech recognition in the translation process, and human computer interaction and interface design for the professional translation environment. For each of these topics, we describe the experiments we performed and the conclusions drawn, providing an overview of the highlights of the entire SCATE project

A systematic review of speech recognition technology in health care

Background To undertake a systematic review of existing literature relating to speech recognition technology and its application within health care. Methods A systematic review of existing literature from 2000 was undertaken. Inclusion criteria were: all papers that referred to speech recognition (SR) in health care settings, used by health professionals (allied health, medicine, nursing, technical or support staff), with an evaluation or patient or staff outcomes. Experimental and non-experimental designs were considered. Six databases (Ebscohost including CINAHL, EMBASE, MEDLINE including the Cochrane Database of Systematic Reviews, OVID Technologies, PreMED-LINE, PsycINFO) were searched by a qualified health librarian trained in systematic review searches initially capturing 1,730 references. Fourteen studies met the inclusion criteria and were retained. Results The heterogeneity of the studies made comparative analysis and synthesis of the data challenging resulting in a narrative presentation of the results. SR, although not as accurate as human transcription, does deliver reduced turnaround times for reporting and cost-effective reporting, although equivocal evidence of improved workflow processes. Conclusions SR systems have substantial benefits and should be considered in light of the cost and selection of the SR system, training requirements, length of the transcription task, potential use of macros and templates, the presence of accented voices or experienced and in-experienced typists, and workflow patterns

EHR Quality Documentation Research Thesis

This research proposal contains and introduction to my proposed research, background of the issue at hand, purpose of the research, significance of the research, research questions that I will be asking, definition of terms, limitations that I may be facing, literature review, SWOT Analysis, and Work Flow diagram on literature review. Also discussed are my proposed research methodology, research design, population sample, data collection procedures, as well as a table of survey variables that I propose to include. This proposal will go over why documentation quality and quantity is so important and the history behind why we are facing a documentation crisis. I am proposing to be able to conduct my research to see how the documentation quality and quantity has improved, or declined since the implementation of EHRs and voice recognition systems across the North Eastern Pennsylvania Region

Translation dictation vs. post-editing with cloud-based voice recognition: a pilot experiment

In this paper, we report on a pilot mixed-methods experiment investigating the effects on productivity and on the translator experience of integrating machine translation (MT) postediting (PE) with voice recognition (VR) and translation dictation (TD). The experiment was performed with a sample of native Spanish participants. In the quantitative phase of the experiment, they performed four tasks under four different conditions, namely (1) conventional TD; (2) PE in dictation mode; (3) TD with VR; and (4) PE with VR (PEVR). In the follow-on qualitative phase, the participants filled out an online survey, providing details of their perceptions of the task and of PEVR in general. Our results suggest that PEVR may be a usable way to add MT to a translation workflow, with some caveats. When asked about their experience with the tasks, our participants preferred translation without the ‘constraint’ of MT, though the quantitative results show that PE tasks were generally more efficient. This paper provides a brief overview of past work exploring VR for from-scratch translation and PE purposes, describes our pilot experiment in detail, presents an overview and analysis of the data collected, and outlines avenues for future work

Speech recognition software and electronic psychiatric progress notes: physicians' ratings and preferences

<p>Abstract</p> <p>Background</p> <p>The context of the current study was mandatory adoption of electronic clinical documentation within a large mental health care organization. Psychiatric electronic documentation has unique needs by the nature of dense narrative content. Our goal was to determine if speech recognition (SR) would ease the creation of electronic progress note (ePN) documents by physicians at our institution.</p> <p>Methods</p> <p>Subjects: Twelve physicians had access to SR software on their computers for a period of four weeks to create ePN. Measurements: We examined SR software in relation to its perceived usability, data entry time savings, impact on the quality of care and quality of documentation, and the impact on clinical and administrative workflow, as compared to existing methods for data entry. Data analysis: A series of Wilcoxon signed rank tests were used to compare pre- and post-SR measures. A qualitative study design was used.</p> <p>Results</p> <p>Six of twelve participants completing the study favoured the use of SR (five with SR alone plus one with SR via hand-held digital recorder) for creating electronic progress notes over their existing mode of data entry. There was no clear perceived benefit from SR in terms of data entry time savings, quality of care, quality of documentation, or impact on clinical and administrative workflow.</p> <p>Conclusions</p> <p>Although our findings are mixed, SR may be a technology with some promise for mental health documentation. Future investigations of this nature should use more participants, a broader range of document types, and compare front- and back-end SR methods.</p

Scott & White Healthcare: Opening Up and Embracing Change to Improve Performance

Offers a case study of a multispeciality system with the attributes of an ideal healthcare delivery system as defined by the Fund. Describes a culture of continuous improvement, collaboration and peer accountability, and a comprehensive approach to care