Please use this identifier to cite or link to this item: https://bspace.buid.ac.ae1234/652
Title: Research Paper in Speech Recognition Technology
Authors: Harbi, Ayla Zaid Mohammed
Keywords: speech recognition technology
Electronic Medical Records (EMR)
healthcare
Issue Date: Mar-2014
Publisher: The British Univesity in Dubai (BUiD)
Abstract: Introduction of electronic medical records (EMR) documentations is burdensome for physicians and clinicians in terms of time spent in typing, and adaption of this technology might be hard. Recent advances in speech recognition technology (SRT) had added positive impact to increase the user’s adaption toward EMR and to be as an alternative to the transcription services. The aim of this paper is to provide comprehensive details of a research study related to the SRT implementation in Ministry of Health (MOH) and to measure it is benefits in terms of productivity, quality of the reports and patient care, time efficiency, and cost saving, and to further investigate the level of satisfaction among the users. A combination of two study designs were utilized for this study which are cross-sectional study and cohort study and as a tool, self-administrated questionnaire was distributed on a random sample. Results of the study showed that the SRT was beneficial. It increased their productivity by 14%, increased number of patients seen for each user by 10%, quality of care increased by 10%, saving almost 20% of the users time in writing their reports, reducing the time between seeing the patients and validating the reports by 18%, and showed potential cost saving of about 22%. Overall user satisfaction was fairly acceptable as only 16% of the users were unsatisfied. Our results suggested that the benefits of SRT in healthcare field are tangible and that this innovative technology can be considered a tool to increase the adaption of EMR technologies and can add great value on the quality of work and productivity of healthcare providers.
URI: http://bspace.buid.ac.ae/handle/1234/652
Appears in Collections:Dissertations for IT Management (ITM)

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