Synesa Solutions Ltd was established in a commercialisation project of the University of Jyväskylä funded by Tekes (Finnish Funding Agency for Innovation). The development project of ReMaster, a tool for visual data analysis, was executed within the research from June 2013 to December 2014.
Our name comes from the Ancient Greek word synesi, which stands for wisdom and knowledge-based wise decisions.
Our specialists are pioneers in simulation and construction of optimal operation models for social and health care needs. We are specialised in computational process analytics with added expertise in health care and statistics. The development of social and health care operations requires multidimensional analysis, because it is an environment with many contingencies where people and technology come together in a manifold way. Our main goal is to provide health care managers with valid and fact-based data and knowledge of the effects of different operational changes before the actual implementation.
Our CEO, Toni Ruohonen (PhD), applied computational process analysis already in 2004 in the NOVA project (quick response project). Simulation models were constructed and exploited to evaluate different development ideas in a virtual environment for the emergency department before implementing the solutions in real life. The simulation helped reduce the average throughput time in the emergency unit by 37 %. In 2010 a new set of data was collected from the real operation and the accuracy of the simulation results was proven to be 99%.
After the NOVA project the focus has actively been in social and health care. For the last ten years we have conducted many different development projects both in Finland and in the US. All the projects have been conducted in close collaboration with public and private service providers. These projects have covered both primary and special health care and comprehensive social care.
Although our focus is currently in the social and health care sector, the same approach can be applied to other domains. In fact, social and health care can be seen as the most complex environment that is difficult to predict. Because our methods work in such a challenging environment, they will work in other domains as well.