Each year one in three individuals over the age of seventy-five will experience a fall. In four out of ten incidents the result will lead to hospitalization, a long period of immobilization and recovery, surgery or in some cases death. As the population ages, this problem becomes more acute and more expensive.
Learn how a senior care agency applied semantics and predictive analytics to their disparate data sources to identify individuals at risk of falling in order to improve patient care and reduce overall costs.
The information was:
Using Semaphore they automated the process of extracting relevant facts from the individual data stores and associated them to identify patients at risk. They combined the facts with next-generation graph-based search tools, to explore the content, identify patterns, look for relationships and arrange preventive intervention before a serious event occurred.
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