This month, the FDA issued a report that focused on the quality, safety, and effectiveness of servicing of medical devices. When I read it, I got reminded of one of the famous tag lines we use in the market – “Machines Never Lie”. The ultimate truth about machine health, performance, and usage is constantly recorded by sophisticated software running inside machines such as MRI, CT, Ultrasound, X-Ray, Cathlabs etc. The report had quite a few noteworthy findings especially in relation to not only the servicing of medical devices but also the impact of highly accurate and timely data can have in providing high-quality care to patients and efficient operation of equipment. Below are three important findings from the FDA report that underscore the value predictive and prescriptive insights generated through Glassbeam’s solutions to the healthcare industry.
Significance of Machine Data
The report highlighted the value that machine data can provide regarding the performance of medical devices, “The FDA finds, as a result of reviewing service records, that the data resulting from the maintenance and repair of medical devices provide valuable insight into the adequacy of the performance of devices.” The report notes that among its priorities for the FDA’s Center for Devices and Radiological Health (CDRH) is the establishment of “collaborative communities” composed of public and private sector members that should work together to address the challenges associated with delivering high-quality, safe and effective servicing of medical devices. Sharing of machine data and the insights solutions, such as what Glassbeam generates, are a cornerstone of these communities.
Advantages of Service Providers
The FDA found that “healthcare establishments identified three leading factors that contribute to their decision to use a particular service provider: quality, cost, and timeliness.” More than ever, healthcare providers and Independent Service Organizations (ISOs) are using predictive technology to improve their cost savings and quality. The ability to confirm an issue with a medical device becomes much more difficult when it is not directly evaluated. Having the power of AI and ML in medical devices can help establish a cause-and-effect relationship that is necessary to improve the quality, cost, and timeliness of a particular medical device. As the FDA reported, “OEMs also communicated that lack of service history records can negatively impact the ability to troubleshoot or identify the root cause of device performance concerns, provide future servicing, and track device performance.” This inability to identify a potential issue with a medical device leads us to how specifically Glassbeam can play a preventive role.
Applying Glassbeam Technology to Real-World Scenarios
Where exactly is Glassbeam’s predictive analytics and machine learning technology being put to use? The FDA’s report also acknowledged specific events where technology, like Glassbeam’s, would have been extremely effective, “An x-ray film developer began to smoke during use. As the technician unplugged the device, it caught fire. Upon investigation of the incident, it was determined that the internal fan was not functioning and the thermal safety fuse had been improperly wired by a third party servicer to bypass the device’s safety feature.” Events like these can be preventable and Glassbeam is opening up a new wave of much needed predictive and preventive capabilities.