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There has been an alarming increase in the number of medical device recalls, especially in Class III devices, cases where the defective product carried a reasonable probability of death. The number of medical device recalls related to defective medical devices nearly doubled between 2003 and 2012 according to the Food and Drug Administration. This increase is being driven by the increasing complexity and sophistication of these devices – especially the software components, the logistical difficulty in managing global supply and sub-supply networks, and the race to market with new devices that is often accompanied by cutting corners.
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 manufacturer's defibrillator 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 out-of-pocket expenses for which the patient is not reimbursed
Using data from sources including a client’s embedded device sensors, social media and blogs, adverse event reports, device safety databases, and the FDA’s MAUDE database, MedDev PREDICT™ will deploy our Deep Machine Learning models to predict when, where, and what type of fault is likely to occur in a medical device. Post-market surveillance analytics will further help ensure the efficacy and safety of devices. While potentially providing a positive impact to the bottom line of device manufacturers, MedDev PREDICT™ should also reduce the financial risk to payers, and minimize the health, safety, and financial risk to patients.
MedDev PREDICT™ contains modules that are designed to enhance an organizations’ ability to meet various government regulations and international standards by using Predictive Analytics to address post-market surveillance and predictive maintenance, potentially reducing both the chance of device adverse events and the number of recalls by identifying when, where, and what type of fault is likely to occur. The standards that we follow include: EN ISO 13485:2016, EN ISO 14971:2012, FDA CFR Title 21 Parts 820 and 822, and EU MEDDEV 2.12-1 and 2.12-1 and 2.12-2.
Post-market surveillance analytics will enable a manufacturer to collect, review, and assess information about the device and related competitor’s devices once the device is on the market, further helping to ensure the efficacy and safety of devices. The combination of being able to predict potential adverse events and alternatives for correction provides a very powerful competitive advantage to introduce changes to prevent defects that result in adverse events and recalls as well as to have the advanced statistical data to validate corrective actions. Being able to scientifically anticipate and prevent harm before it occurs will only enhance the brands’ appeal to payers, providers, and consumers.
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