IBM Watson

Every day, vast quantities of data are collected about patients as they pass through health service organizations—from operational data such as treatment history and medications to physiological data captured by medical devices. The insights hidden within this treasure trove of data can be used to support more personalized treatments, more accurate diagnosis and more advanced preparative care. But since the information is generated faster than most organizations can consume it, unlocking the power of this big data can be a struggle. This type of predictive approach not only improves patient care—it also helps to reduce costs, because in the healthcare industry, prevention is almost always more cost-effective than treatment. However, collecting, analyzing and presenting these data-streams in a way that clinicians can easily understand can pose a significant technical challenge.

To develop its next-generation flight management systems, CMC sought a new object-oriented, model-based process that would help comply with the DO-178C and ARP 4754A/4761 aviation industry standards.Over the years, CMC has developed a comprehensive range of FMS products, designed to meet the specific needs of different sectors of the aviation market. The benefit of this approach was that each product satisfied a unique set of requirements – but from a systems and software engineering perspective, it takes more time and effort to develop and maintain separate software for each target market.