By examining and integrating all relevant, interdependent influences on a healthcare system into a coherent model, a much deeper level of insight is achievable than by other methods.
By applying predictive models to historical and current populations, a powerful explorative tool is available for predicting both the range and likelihood of possible outcomes.
By evaluating how different strategies might perform in a myriad of scenarios, an optimum approach to healthcare services delivery with associated likely outcomes and inherent risks can be identified.
Analytic Cube was founded with the purpose of providing a better understanding of healthcare services delivery by developing robust, realistic predictive models. We use these models to provide the foresight needed by stakeholders to make better decisions to shape the future of healthcare in Canada. We call this platform Posyden.
In order to build powerful, informative models we have assembled a team that brings together skills and experience across a diverse range of disciplines: computer science, data science, information technology, statistics, medicine, and psychiatry.
Analytic Cube was founded out of a desire to understand healthcare services delivery; to specifically answer the question "how can patient outcomes be improved at lower cost?" Consequently, this has driven us to develop new approaches and new tools, bringing together people with disparate interests in medical services delivery, data analytics, statistics, and computer science integrated with emerging fields such as cloud computing, data science, and machine learning.