Intelligent Risk Detection in Healthcare Contexts of Hip and Knee Athroplasty and Paediatric Congenital Heart Disease

Intelligent Risk Detection in Healthcare Contexts of Hip and Knee Athroplasty and Paediatric Congenital Heart Disease

Hoda Moghimi (RMIT University, Australia), Nilmini Wickramasinghe (Deakin University & Epworth HealthCare, Melbourne, Australia) and Jonathan L. Schaffer (The Cleveland Clinic, USA)
DOI: 10.4018/978-1-4666-9446-0.ch001
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Abstract

Rapid increase of service demands in healthcare contexts today requires a robust framework enabled by IT (information technology) solutions as well as real-time service handling in order to ensure superior decision making and successful healthcare outcomes. Contemporaneous with the challenges facing healthcare, we are witnessing the development of very sophisticated intelligent tools and technologies such as Business Analytics techniques. Therefore, it would appear to be prudent to investigate the possibility of applying such tools and technologies into various healthcare contexts to facilitate better risk detection and support superior decision making. The following serves to do this in the context of Total Hip and Knee Arthroplasty and Congenital Heart Disease.
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Background

Clinical Decision Support Systems (CDSS) are computer driven technology solutions, developed to provide support to physicians, nurses and patients using medical knowledge and patient-specific information (De Backere, De Turck, Colpaert, & Decruyenaere, 2012). Decision Support systems can be found in widely divergent functional areas. However, in e-health contexts, key features such as intelligent timing, multidimensional views of data and calculation-intensive capabilities become important features given the need for real time outcomes and the multi-spectral nature of care teams (Wickramasinghe, Chalasani, & Koritala, 2012). Hence, systems for healthcare must give advice and support rather than decision making replacing that of clinical staff.

Studies have already proved that CDSS enhance quality, safety and effectiveness of medical decisions through providing higher performance of the medical staff and patient care as well as more effective clinical services. A variety of CDSS programs designed to assist clinical staff with drug dosing, health maintenance, diagnosis, and other clinically relevant healthcare decisions have been developed for the medical workplace (Haug, Gardner, Evans, Rocha, & Rocha, 2007). On the other hand, patients’ demand for participation in medical decisions has been increasing (Kuhn, Wurst, Bott, & Giuse, 2006). Therefore, to be respectful of patients and parents/guardians participation and decisions, shared decision-making (SDM) between health care professionals, patients, parents and guardians is widely recommended today (Lai, 2012). SDM is defined as the active participation of both clinicians and families in treatment decisions, the exchange of information, discussion of preferences, and a joint determination of the treatment plan (Barry & Edgman-Levitan, 2012; Charles, Gafni, & Whelan, 1997; Légaré et al., 2011; Makoul & Clayman, 2006).

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