– Support equipment maintenance and logistics through flagging when and where demand may increase.
– Improve education programmes through providing trainees more examples to practice techniques on.
– Speed up development timelines for therapeutics through improving product design and trial success rates.

To summarise, at its simplest, the digital twin can be defined as a set of algorithms trained by input data which can be fed a hypothetical scenario to produce a predicted result that is designed to be as close to "reality" as possible.
But with the number of potential applications, and therefore products being so vast, where should investors and collaborators focus their attention in the short term?

Polishing the surface
Differentiation and value proposition between digital twins will always be somewhat unique to all potential stakeholders. For example, a medical imaging equipment manufacturer is much more likely to derive short-term benefits from digital twins designed to support the function and maintenance of the equipment it produces, compared to digital twins designed to simulate the effects of a drug on a candidate (where a pharmaceutical company might make more use of it).

As is shown above, there are many ways digital twins can impact healthcare, and whilst it can be hard to differentiate between all the solutions out there, there are some general lessons that can be applied across the spectrum.

Digital Twins designed to simulate operational requirements will likely scale quicker.
Digital twins for operational analyses are perhaps the most prolific form of digital twins used in healthcare today. Whilst these products can’t be considered common by any means, the vendors focused on these types of products also tend to be much larger and commercially mature than startups. This implies that such solutions will be able to scale in deployment to existing customer bases more easily.

These tools can be used to help with forecasting across equipment fleets, hospital departments or entire neighbourhoods, and generally involve being able to monitor a sequence of events that drive supply and demand. Highlighting inefficiencies and prompt improvements at "failure points" can have operational implications on equipment capacities, staffing, and care delivery models.

Siemens Healthineers is one example of a multibillion-dollar healthtech giant that is involved in developing operational solutions. For example, it has partnered with Mater Private Hospital in Dublin to improve their radiology operations. In 2019 the partnership released a white paper revealing that Siemens and Mater had performed an overall assessment of the current operations and used this information to simulate potential improvements to workflows, which ultimately led to reduced waiting times, faster patient turnaround, and lower staffing costs for MR and CT scans.

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