Condition based remote service + Machine learning on failure modes

Numerous medical devices operate in healthcare facilities across globe. With modern day technology ,they are likely to have all possible remote services. Centers around the world providing first and second-level product support. Often, the third-level deep knowledge about a specific product is in a single manufacturing or research and development site. The tremendous advantage of real-time network-connected remote service is the speed with which a manufacturer’s support organization can get answers from all three levels of expertise. In the case of automatic reporting of system condition, only system status information is transferred – for example the monitoring of system parameters to anticipate component problems or out-of-bounds operation that demands service attention. This saves both cost and time. The technical experts required to solve a technical problem may be located on the other side of the globe. Today, it is unrealistic to expect that experts who are trained to address all technical problems are located in every country. Instead, they are distributed globally in a cost- and service-effective way to maximize the provision of needed services to the healthcare provider organizations. Adding machine learning models in a cloud environment help improve the predictability of the failures and help customers to plan their workload very effectively. It is always better to resolve an issue before even it occur or for that matter replace a spare part before it fail !!!!