Sleep disorder diagnosis: the design and implications of online tools

Jacqueline Blake; Don Kerr

doi:10.1186/2193-8636-1-7

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Sleep disorder diagnosis: the design and implications of online tools

Journal article

Open access

Peer reviewed

Sleep disorder diagnosis: the design and implications of online tools

Jacqueline Blake and Don Kerr

Decision Analytics, Vol.1(7)

2014

DOI: https://doi.org/10.1186/2193-8636-1-7

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Abstract

Information Systems

health informatics

chronic condition

sleep disorders

evidence base

decision analytics

Sleep disorders are a significant and growing problem, both for the economy of the nation and for the physical and psychological well-being of individual sufferers. The purpose of this research was to investigate the operational, administrative and medical environment within which sleep physicians currently diagnose patients with sleep disorders and develop an online support system that would more efficiently gather patient history data, and so improve the effectiveness of patient-physician consultations, the diagnoses, and patients' selfmanagement of any subsequent treatment plans. The development of the online tools used a design science approach and those tools evaluated for usefulness and ease of use by physicians and the general population. The physicians found that the patient history information was in a form that facilitated assimilation with the removal of routine data collection allowing a more targeted or shorter consultation. The users found the application easy to use and that it made them think about their sleep quality. The implications for future practice of sleep disorder diagnosis and the value of the collection of patient history which would be available for analysis is also discussed.

Details

Title: Sleep disorder diagnosis: the design and implications of online tools
Authors: Jacqueline Blake (Author) - University of the Sunshine Coast - Faculty of Arts and Business
Don Kerr (Author) - University of the Sunshine Coast - Faculty of Arts and Business
Publication details: Decision Analytics, Vol.1(7)
Publisher: SpringerOpen
Date published: 2014
DOI: 10.1186/2193-8636-1-7
ISSN: 2193-8636
Copyright note: Copyright © 2014 Blake and Kerr. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.
Organisation Unit: University of the Sunshine Coast, Queensland; USC Business School - Legacy; School of Science, Technology and Engineering
Language: English
Record Identifier: 99448797902621
Output Type: Journal article

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