Manufacturing technology
Collaboration between academia and manufacturers will drive digital twin innovation.
a regulated medical device. You can’t just take the same software tool used by design engineers and put it into the clinic. That software is heavyweight, big, expensive, takes big hardware to run. It doesn’t run on a tablet at the bedside.”
He also says there are questions, still being worked through, on appropriate business models for twins used by clinicians. The software programs traditionally used to create digital twins do not have a license model by which users can ‘sell’ their creations, “so I’m spending much more of my working time than ever before coding new things in Python; writing new software”.
“Clinical support is a very different game because you can’t wait ten hours for the simulation to finish. It’s got to be nearly real time.”
Jeff Bodner
Then there is the question of how users pay for a digital twin product. “In the US, reimbursement [for services] is a big deal. So, could a clinic be reimbursed for offering a [digital twin] service? We have to demonstrate the value proposition so that they could be reimbursed and physicians pay for it. I think there will eventually be a strong enough business case for clinics, but we’ve got to demonstrate it.” Numbers also come into play when considering another possible application of digital twins in the healthcare sphere. Clinical trials are an essential part of testing new interventions, including medical devices. But recruiting sufficiently large and diverse groups to these trials can be challenging, particularly in rare diseases, and the need to have a control group can present challenging ethical dilemmas. What if instead some of those trial subjects could be digital replicas that have been proved to be effective at forecasting the outcome of the intervention? The result would potentially be trials
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of much larger groups, and possibly much more swiftly and at much lower cost. King’s College London’s de Vecchi stresses she
doesn’t see that approach supplanting traditional clinical trials. “But it will definitely be helpful in making a real clinical trial more targeted and in refining the hypothesis behind it,” she says. “Maybe in silico [digital simulation] trials show certain things we might not need to prove and then we can use clinical trials to focus on other hypotheses and other questions. It would make it more cost-effective, because you would avoid going down routes that don’t lead anywhere.” Either way, she believes digital twins have hit a tipping point in medical device development, becoming “totally accepted” as a crucial tool by manufacturers. She and her colleagues actively collaborate with a number of such companies, “large and small”. Niederer says he and his team do the same. “A range of manufacturers is interested in, and using, modelling and simulation in their device development,” he reports.
What he hopes is that even greater collaboration between academia and manufacturers might be possible. “Having better access to data from devices would certainly make creating digital twins more viable. And the more that device companies promote and tell us that they are using the models we are creating, the more than helps me in being able to write grant applications and get funding for the next generation of digital twins.”
What that next generation will look like is yet to be determined. But it’s clear they will be a long way from a digital replica of a device, as well as from how the medical device industry has typically approached simulation.
“If I tell somebody at a conference I’m an engineer and do simulation work, that immediately creates a picture of what I do for a living,” concludes Bodner. “But this [digital twins] is such a departure from that. It’s a whole different skill set.” ●
www.medicaldevice-developments.com
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