Regulatory
Philips’ 2021–2023 CPAP and CBAP recall drew criticism for slow action, leaving patients unknowingly exposed to harmful foam.
and usage could also be identified quickly using AI, for instance. “Wearables, types of hospitals at home, internal medical devices with an app…this could transform medicine,” he says.
Here, the MHRA is experimenting with this technology. They’re leading an AI airlock experiment which allows device manufacturers to explore how they can collect evidence to support the safety of products. While this is primarily concerned with readying products for approval, and focuses on AI devices, the approach could potentially help inform recall decisions, too – for instance, in helping manufacturers understand what they’re required to deliver to ensure device safety. “It shows we’re trying to get better in this area,” says Gilbert. Gilbert is confident these technologies could modernise data-sharing processes – currently, the process centres around preparing paperwork and filing it. But with digital systems, manufacturers and regulators could move towards a process where performance and safety feedback can more easily be given on devices and systems, much the way pop-ups work online. This would be a much simpler way of logging reports. It could also help to standardise reporting systems and make them more transparent. With clear processes for collecting, storing, and sharing information with the right people, firms would have little excuse for inaction. Transparency matters here, adds Gilbert. “If this is out in the open, companies have to take it seriously… [they] have to make sure their laundry isn’t dirty on the line.” The subtext being: if the right data is made accessible to all relevant parties, it wouldn’t be possible to obscure information about faulty devices – or to drag one’s feet in implementing a recall. While AI could potentially be used to automate processes and sort through large datasets in device recalls – for instance, an AI chatbot could collect
16
feedback on devices while another model might automatically flag performance deviations – we’d need to figure out how we’d regulate these systems, too. Especially when integrated into a medical device. “We can’t regulate an AI decision support system the same way as a hip implant,” notes Gilbert. It’s perhaps something on the horizon in years to come.
The tools to act
As recall processes evolve, Gilbert warns they mustn’t double down on an already documentation- heavy framework. For instance, manufacturers must report incidents with their devices to regulators, document risk assessments, submit their strategy for performing the recall, deliver notification letters and more. “Manufacturers are rightly saying these documents [they have to provide] don’t do much for patient safety,” he explains, noting that even for the most comprehensive audits of devices, which could spot the need for recall or not, it’s all “the manufacturer’s filtered opinion”. Clearer reporting standards could help make this process less opaque for both manufacturers and regulators. And having streamlined reporting channels that make it easy to log data could cut down the admin burden and get things moving quicker. It gives all parties what they need to take action, faster – and if that data is more widely available, firms could potentially be held accountable should they fail to act. Plus, it gives patients and practitioners peace of mind that their concerns have been filed and received by the right people.
In its 2011 report, the GAO recommended the FDA should regularly assess how a recall is progressing and develop criteria for gauging its effectiveness. A solid flow of information from the devices, to the manufacturer and onto the regulator would certainly be a big help in tackling this problem. ●
Medical Device Developments /
www.medicaldevice-developments.com
Ann Marie Walkington/
Shutterstock.com
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53 |
Page 54 |
Page 55 |
Page 56 |
Page 57 |
Page 58 |
Page 59 |
Page 60 |
Page 61 |
Page 62 |
Page 63 |
Page 64 |
Page 65 |
Page 66 |
Page 67 |
Page 68 |
Page 69 |
Page 70 |
Page 71 |
Page 72 |
Page 73 |
Page 74 |
Page 75 |
Page 76 |
Page 77 |
Page 78 |
Page 79 |
Page 80 |
Page 81 |
Page 82 |
Page 83 |
Page 84 |
Page 85 |
Page 86 |
Page 87 |
Page 88 |
Page 89 |
Page 90 |
Page 91 |
Page 92 |
Page 93 |
Page 94 |
Page 95 |
Page 96 |
Page 97 |
Page 98 |
Page 99 |
Page 100
