Some vendors may be able to partner with academic institutions, which generally have more structured and rare data types available. In addition, there are a growing number of vendors, such as Medexprim, Epic and Flatiron, with data aggregation platforms designed to aggregate and structure clinical data across healthcare networks and even provide access to third parties. Some cloud vendors have similar offerings to support structuring data as part of an organisation’s migration to cloud infastructure.

Patient Privacy Concerns Can Inhibit Data Collection
Patient privacy is a particularly sensitive topic today, and with regulations like HIPAA and GDPR in place, vendors must not misstep when dealing with data, especially if it is being used in external settings. This issue becomes particularly acute when live databases are used, and although advanced techniques like federated learning have been offered as a solution, the nascent state of these technologies inevitably will contribute to the slower uptake of related solutions.
Regulatory Hurdles, Legislation Around AI is Still Unclear
Following on from this, there are significant regulatory challenges, especially when AI is concerned. Regulatory authorities are already grappling with the challenges associated with governance of AI, and digital twins use a number of emerging technologies that make oversight difficult. For this reason, there are few clinically-orientated digital twins approved for us by the FDA today, which limits confidence from customers and stymies addressable markets.

Digital Twins are Linked to Several Emerging Technologies and Trends
Infrastructure is a significant rate-limiting step to the adoption of digital twin software, which will dictate how quickly these solutions can be deployed at scale. Across healthcare, cloud architecture, 5G networks, and blockchain implementations are few and far between, and it is ultimately these technologies which will pave the way for larger and more complex models to be implemented.

As was shown in Figure 1, digital twin software can utilise adjacent technologies such as 5G infrastructure, which enables faster and more reliable connectivity, and blockchain which can help ensure data integrity. Both these technologies support the use of live databases to keep training data continually supplemented, which should also contribute to improving the model’s accuracy. However, both aren’t very common in healthcare settings today, which has a corresponding impact on where digital twins can be implemented successfully at scale.

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