Intelligent Risk Detection for Healthcare

Intelligent Risk Detection for Healthcare

Fatemeh Hoda Moghimi (RMIT University, Australia) and Nilmini Wickramasinghe (Epworth HealthCare, Australia & RMIT University, Australia)
Copyright: © 2014 |Pages: 13
DOI: 10.4018/978-1-4666-5202-6.ch118


Healthcare is an information rich industry where successful outcomes require the processing of multi-spectral data and sound decision making. The exponential growth of data coupled with a 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. 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 Orthopaedics and Congenital Heart Disease.
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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 because of the importance of real time outcomes and the multi-spectral nature of care teams (Wickramasinghe, Bali, Kirn, & Sumoi, 2012), the following key features become most essential:

  • Intelligent timing

  • Multidimensional views of data

  • Calculation-intensive capabilities

Hence, these systems will 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 (Garg, Adhikari, McDonald, Rosas-Arellano, Devereaux, Beyene, & Haynes, 2005; Fichman, Kohli, & Krishnan, 2011; Restuccia, Cohen, Horwitt, & Shwartz, 2012). 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, 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 (Makoul & Clayman, 2006; Légaré, Stacey et al., 2011; Barry & Edgman-Levitan, 2012).

Key Terms in this Chapter

Decision Support Systems (DSS): A computer based systems or applications to facilitate complex decision making processes to improve processing outcomes.

Congenital Heart Disease (CHD): A life treating heart condition that can affect many newborns babies.

Intelligent Risk Detection (IRD): A proposed IT solution to improve surgical decision efficiency in healthcare contexts by (Moghimi F.H, Wickramasinghe, N. 2011)

Risk Detection: Capturing risk factors by using computer based solutions.

Data Mining (DM): An IT technique to discover hidden knowledge or patters through historical data through even huge data bases.

Real Time Intelligent Solution: IT solutions capable to make ad-hoc services smartly while their outputs would be accessible on-line.

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