The healthcare industry is amassing more data than ever before. Consider this: The U.S. healthcare system was already producing 150 exabytes of data several years ago and was expected to soon generate zettabytes (1021 gigabytes) and eventually yottabytes (1024 gigabytes) of data.1
Fraud, waste and abuse (FWA) in the healthcare industry has been estimated to cost anywhere from $80 billion to $272 billion each year.1,2 Unfortunately, fighting FWA is not easy. It typically takes dozens of highly trained experts months just to go through the amount of data required to successfully identify complex patterns of FWA.
In an “always-on” world, many patients suffer from information overload – a term used to describe the difficulty of understanding an issue and effectively making decisions when one has too much information about said issue.1 The conundrum: Healthcare organizations could still help patients significantly by communicating with them via electronic devices.
There’s been plenty of talk about the potential of machine learning. Consider the following: