eHealthAnalytics uses machine learning and decision intelligence to produce a complete prediction for the interaction and safety of medical devices in conjunction with all aspects of patient demographics and treatment modalities. Our platform, Pythia PREDICT™, will help enable superior outcomes, while reducing costs, and mitigating risk, providing a distinct value proposition for device manufacturers, providers, payers, and patients. 

Pythia PREDICT

MedDev PREDICT

Patient360 PREDICT

Pythia PREDICT™ is a subscription-based advanced analytics platform designed to make healthcare more predictive, prescriptive, and personalized. Leveraging massive amounts of data from multiple sources, we use deep machine learning to find the weak signals - seemingly insignificant factors that would otherwise not be found - to focus on the measures that impact outcomes and applies advanced analytics to identify the right patients to target with the right interventions at the right time based on patient behavior and history. The interaction of medical devices considered in conjunction with all other medical factors is something that has not been done before. 

There has been an alarming increase in the number of medical device recalls in recent years that is costing the device industry between $2.5 and $5 billion a years. The cost impact can be almost as significant for payers and patients subject to replacement surgeries. MedDev PREDICT™, powered by Pythia, uses deep machine learning to address medical device faults, identifying when, where, and what type of fault is likely to occur. Being able to scientifically anticipate and prevent defects before they occur limits the financial risk for all stakeholders and can minimize health and safety concerns for patients.

Patient360 PREDICT, also powered by Pythia, provides a holistic approach to the treatment and care of  , patients afflicted with multiple chronic illnesses and taking multiple medications simultaneously and using a medical device. We provide stakeholders with the possibility of predicting the development of a disease in advance, enabling early interventions and the ability to view various treatment options and the rationale behind each option, enabling them to select those that provide better outcomes while managing cost and risk.

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Non-routine quality events are costing the medical device industry an average of $2.5 - $5 billion a year

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.    

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PREDICT

When, where, and what type of medical device fault is likely to occur, enabling proactive remediation

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PREDICT

The factors under which a device could be most effective, ineffective, or dangerous (potentially fatal)

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PREDICT

The risk of adverse reactions in patients with multiple conditions  taking multiple medications simultaneously

Replacing Faulty Heart Devices Costs Medicare $1.5 Billion in Ten Years

...New York Times, Kaiser Health News 10/02/17

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