The majority of medical devices are monitoring devices. Therefore, data communication and analysis are playing a crucial rule in predicting the effectiveness and reliability of a device. Device related data, patient related data and device-patient related data stored in Data Bases (DBs) are great sources for enhancing either new designs or improving already existing ones. Analyzing such data can provide researchers and device development teams with a complete justification and patterns of interest about a device’s performance, life and reliability. Data can be formulated into stochastic models based their statistical characteristics to consider the variability in data and the uncertainty about processes and procedures during early stages of the design process. This strengthens the device’s ability to function under a broader range of operating conditions. The work herein aims at targeting unwanted variations in device performance during the device development process. It employs a novel technique for variation risk management of device performance based historical process data modeling and visualization. The introduced technique is a proactive systematic procedure comprises a tool set that is being placed in the larger framework of the risk management procedure and fully utilizing data from the DBs to predict and address the risk of variations at the early stages of the design process rather than at the end of each major stage.

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