Pythia PREDICT™ is our subscription-based Advanced Analytics platform using machine learning technology and massive amounts of data to help improve outcomes and minimize risk and safety concerns for medical device manufacturers, payers, pharmaceutical companies, providers, and most importantly, patients
MedDev PREDICT™, powered by Pythia, uses massive amounts of data and machine learning to find the weak signals - seemingly insignificant factors that would otherwise not be found - to address medical device faults identifying when, where, and what type of fault is likely to occur.
GeroPREDICT™ powered Pythia, focuses on the usage of medical devices in the older population, typically afflicted with multiple chronic conditions and taking multiple medications enabling you to determine which outcomes to be the most effective going forward.
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The number of recalls related to defective medical devices nearly doubled from 2003 to 2012.. The ramifications for the device manufacturers, payers, and patients are significant. The cost for a single event of this type has been as high as $600 million for a manufacturer. The cost impact can be almost as significant for public and private payers who must bear the burden of reimbursements for replacement devices and surgical procedures. A 2007 recall of one device resulted in almost one billion dollars in reimbursements by Medicare. Nor is the patient spared. In addition to the need for repeat surgery, and the occasional fatality associated with that surgery, there are the out-of-pocket expenses for which the patient is not reimbursed.
When, where, and what type of medical device fault is likely to occur, enabling proactive remediation
The factors under which a device could be most effective, ineffective, or dangerous (potentially fatal)
The risk of adverse reactions in patients with multiple conditions taking multiple medications simultaneously
...New York Times, Kaiser Health News 10/02/17
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