A probabilistic model for risk forecasting in Medical Informatics

• Main Authors: Shahzad, Basit, Azween, Abdullah
• Format: Conference or Workshop Item
• Published: 2011
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://eprints.utp.edu.my/5597/1/A_Probabilistic_Model_for_Risk_Forecasting_in_Medical_Informatics.pdf; http://eprints.utp.edu.my/5597/
• Institution: Universiti Teknologi Petronas

The history of software project failures is very old. In recent past huge emphasis has been given to investigate the possible causes of failure. In this regard an empirical study was conducted in 1996 on a group of 13, 000 projects and it was concluded that almost 25% project were either delayed or fail projects [5]. The project failure in any organization and in any environment dents the firms so badly that occasionally it becomes harder for them to survive. This means that delayed and failed projects not only put the name and reputation of the firm at question but also the firm's revenues, jobs of the employees, moral and prospects to gain future projects is also greatly damaged. With the advancement of technology and ease in development of small and medium level systems has strongly encouraged the development of information systems and the Health specific Information Systems (IS), generally referred to as Health Information System (HIS) or Medical Information System (MedIS). The science that governs the development of MedIS or HIS is known as Medical Informatics (MI) that has emerged as a consolidated branch of IS during last thirty years. In precise terms (MI) "is primarily intended to provide solutions for problems of processing data, information and knowledge in medicine and healthcare" [4]. There are a lot of risks in developing the projects that are related to the MI. The research is focused to provide the ability to forecast the risks in MI by categorizing the project in small, medium amd large scae and they by applying the probabilistic model calculating the outcome value for each risk factor and ultimately mapping it back to the project factors to reduce the risks by adjusting the values assigned to them.