Accountable Care and Evidence-Based Decision Making

Accountable Care and Evidence-Based Decision Making

Nilmini Wickramasinghe (Epworth HealthCare, Australia & Deakin University, Australia) and Steve L. Goldberg (INET Intl. Inc., Canada)
Copyright: © 2017 |Pages: 10
DOI: 10.4018/978-1-5225-0920-2.ch034
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In today's cost challenged healthcare environment accountable care and evidence-based decision making have become important considerations. Contemporaneous to this is the fact that the superior management of diabetes has become a global priority especially given the exponential increase in the number of diabetes patients as well as the financial implications of treating this silent epidemic. Thus, this research focuses on trying to address these respective yet critical issues by examining the possibility of using a mobile web-based reporting system that taps into existing widely available resources to monitor and manage gestational diabetes. To test this solution, we adopted a randomized control trial with two-arm cross over applied to a not-for profit hospital in Victoria, Australia. From the perspective of practice, we have uncovered far reaching implications for hospital management's cost vs. quality care to patients. In particular, it appears that the adoption of smartphones to support many aspects of care and patient-clinician interactions is prudent.
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Background And Literature Review

The relevant key bodies of literature include management of gestational diabetes and the accountable care system and are presented in turn below.

Management of Gestational Diabetes

Managing diabetes using information technologies is a relatively recent priority within the healthcare domain as diabetes has become one of the most prevalent and exponentially increasing diseases around the world. Further, the cost of managing patients with diabetes is also considerable. In Australia, it costs over $14.6 billion to treat a diabetes patient with complications running up to $9,645 per year (Diabetes Australia, 2013). Therefore, an effective management system for diabetes is an imperative.

As diabetes is a chronic disease there is by definition no cure. This makes the adoption of various management strategies paramount in the successful care of diabetic patients (Britt, 2007, AIHW, 2007, AIHW, 2008, Diabetes Australia, 2008). Moreover, the prudent adoption of these strategies can dramatically affect the cost of care without significantly impacting the quality of care. Typically, the management of diabetes is based on a mixture of self-management protocols linked with the support of a dedicated medical care team (Victorian Government, 2007). An essential element of self-management relies on regular testing of blood glucose, using a glucometer or blood glucose monitor.

Gestational Diabetes Mellitus (GDM) requires tight control of Blood Sugar levels, with a preferred range of readings between 4-6 mmol/L (Hoffman, et al, 1998; Siri and Thomas, 1999). The management of GDM is conducted over a short period of time (that of the pregnancy) and thus a tighter management of sugar levels is required while the impact for both mother and baby of poor control can be far reaching and unpleasant. The need for such control draws into question the information that a diabetic may need when self-managing their condition.

Key Terms in this Chapter

Information Communication Technologies (ICTs): Computers and mobile devices such as iPads and mobile phones that support electronic data and communication exchanges.

Smartphones: Mobile devices that enable the use of many applications.

Chronic Disease: Disease that has no foreseeable cure and thus the patient must live with the disease for the rest of their life.

Accountable Care: The policies in healthcare delivery that ensure that any intervention is justified and someone is held responsible for making the key decisions.

Gestational Diabetes Mellitus (GDM): A type of diabetes, problem with sugar in the blood that is experienced by women who are pregnant only.

Evidence Based Decision Making: A practice of making decisions in clinical contexts based on used medical cases.

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