Sleep Disorder Diagnosis: An Analytical Approach

Sleep Disorder Diagnosis: An Analytical Approach

Jacqueline Blake (University of the Sunshine Coast, Australia) and Don Kerr (University of the Sunshine Coast, Australia)
Copyright: © 2015 |Pages: 20
DOI: 10.4018/978-1-4666-6316-9.ch008
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Abstract

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. Physicians are under pressure to find ways of dealing with the backlog of patients. The purpose of this chapter is to investigate the operational, administrative, and medical environment within which sleep physicians diagnose patients with sleep disorders and develop an online support system that would efficiently gather patient history data and improve the effectiveness of patient-physician consultations, the diagnoses, and patients' self-management of any subsequent treatment plans. Investigations confirm that the physicians spend a large portion of the available consultation time on routine questions. In the new system, the patient information is captured by the patient completing an online questionnaire. Due to the reduction in time given for data collection, the physician can spend time with the patients discussing patient-specific symptoms and life-styles.
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Increasing Demand For Services

This increasing service demand and a shortage of sleep disorder specialists (Australian Medical Workforce Advisory Committee 2000; Patlak 2005) have driven the sleep community interest in changing the traditional model of care for sleep disorders to one that leverages information systems to provide efficiencies and timely communication flows (Colten & Alterogt 2006). Abidi (2001) refers to the strategic use of knowledge derived from healthcare data as being “… pertinent towards the improvement of the operational efficiency of the said healthcare enterprise” (Abidi 2001, p. 6) while Hirakis & Karakounos (2006) and Haux (2010) state that knowledge management in healthcare allows the development of best practice models, making guidelines explicit and encourages innovation through development of the resultant knowledge base.

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