modeling of the processes taking place in the physical entity. While in the first
approach the focus was on the design and construction of the physical entity here the
focus will usually be more on the understanding and optimization of the system with
a much stronger business goal. Having the experience of how the physical entity
operates and changes also makes it easier to construct the capabilities to manage
change in each component of the system. This kind of digital twin can then be
enriched to provide more predictive and simulation capabilities but once again it will
usually start hitting limitations.
Similar dynamics will exist in digital twins of biological systems but with the
added challenge of lower level of control over design and of understanding of the
physical entities internal mechanisms.
The ideal lifecycle is at a higher level of maturity where in the initial design equal
focus is given to the physical entity and the virtual model as well as their interactions. This also requires designing into the system from the beginning of how the
virtual simulations can influence and direct the physical reality. In the case of
preexisting physical phenomena such as biology, this of course requires a very
deep understanding of that entity in order to formalize upfront what are the characteristics of the physical phenomenon that can be influenced or controlled and
embedding in the virtual model a control strategy where outcomes of virtual
model simulations can be applied to the physical entity with a continuous
feedback loop.
5 Digital Twin: Potential Applications in Healthcare
and Biopharma Industry
Digital Twins acting as a digital replica for the physical object, product, and/or
service are the next source of competitive advantage for the Healthcare sector.
They are key to accelerate the move towards preventive and personalized medical
treatments by modeling reality with advanced analytics techniques in such a way that
problems can/will be predicted whether and when they occur, providing the time
necessary to treat the patient in advance.
Furthermore, digital twins can provide a safe (virtual) environment for testing the
impact of changes on the performance of a specific system or living body. This will
enable optimal solutions, as they imply minimal capital investment, but more
importantly will drastically limit risks which are obviously critical in the health
sector.
A digital twin can be designed and implemented to improve “care-delivery”
services, patient experience, and specific treatments as well as the overall Healthcare
value chain. The potentials are unlimited as today we are just “looking” at the peak
of the iceberg.
However, like in any technology, the misuse of digital twin can drive issues both
on the data privacy standpoint as well as accessibility of the potential solution that,
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M. Canzoneri et al.
approach the focus was on the design and construction of the physical entity here the
focus will usually be more on the understanding and optimization of the system with
a much stronger business goal. Having the experience of how the physical entity
operates and changes also makes it easier to construct the capabilities to manage
change in each component of the system. This kind of digital twin can then be
enriched to provide more predictive and simulation capabilities but once again it will
usually start hitting limitations.
Similar dynamics will exist in digital twins of biological systems but with the
added challenge of lower level of control over design and of understanding of the
physical entities internal mechanisms.
The ideal lifecycle is at a higher level of maturity where in the initial design equal
focus is given to the physical entity and the virtual model as well as their interactions. This also requires designing into the system from the beginning of how the
virtual simulations can influence and direct the physical reality. In the case of
preexisting physical phenomena such as biology, this of course requires a very
deep understanding of that entity in order to formalize upfront what are the characteristics of the physical phenomenon that can be influenced or controlled and
embedding in the virtual model a control strategy where outcomes of virtual
model simulations can be applied to the physical entity with a continuous
feedback loop.
5 Digital Twin: Potential Applications in Healthcare
and Biopharma Industry
Digital Twins acting as a digital replica for the physical object, product, and/or
service are the next source of competitive advantage for the Healthcare sector.
They are key to accelerate the move towards preventive and personalized medical
treatments by modeling reality with advanced analytics techniques in such a way that
problems can/will be predicted whether and when they occur, providing the time
necessary to treat the patient in advance.
Furthermore, digital twins can provide a safe (virtual) environment for testing the
impact of changes on the performance of a specific system or living body. This will
enable optimal solutions, as they imply minimal capital investment, but more
importantly will drastically limit risks which are obviously critical in the health
sector.
A digital twin can be designed and implemented to improve “care-delivery”
services, patient experience, and specific treatments as well as the overall Healthcare
value chain. The potentials are unlimited as today we are just “looking” at the peak
of the iceberg.
However, like in any technology, the misuse of digital twin can drive issues both
on the data privacy standpoint as well as accessibility of the potential solution that,
176
M. Canzoneri et al.