How can healthcare information be made available, where and when it is needed?
The modern era has provided easily accessible healthcare information through the collection and manipulation of data, but it has also created complexity. We see this in the amount and type of data that is available, the growing number of sources where it is captured and stored, and the more specialized ways in which it is being used. There is the emergence of ‘personalized medicine’, where advanced analytics can be applied to this information – including the person’s genome – giving management advice that is tailored to the individual rather than what has previously worked within a similar population. Following that advice often requires access to highly specialized services, but finding them can be a challenge.
And this material is complicated – it requires a deep understanding of specialized domains, vast quantities of information about both an individual and a population and the development of complex algorithms. With machine learning, facts are gleaned from this morass of data requiring powerful computing, more than a ‘normal’ Electronic Health Records (EHR) application can provide. How can this information and these capabilities be made available when, where and to whom they are needed?
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