Description

Digital Twin technology, or the application of predictive analytics to fields as diverse as manufacturing and space science, is now being considered in the context of healthcare. The attraction is obvious: if one can develop a computer model of a patient, perhaps one can test treatments on the model before applying them to the individual. Rather than using a patient as a beta test for a clinical approach, potential complications can be identified before they can become life threatening. Treatments could be optimized, then applied. At least, that is the hope.

Unfortunately, early results have not been so promising because it is extremely hard to model an individual patient. Not only is the required data pervasive, but any attempt to collect enough data to populate a numerical model has the potential of adversely impacting, perhaps even killing, the patient. This has led many medical experts to totally discount the possibility of ever applying Digital Twins to healthcare.

However, Frost & Sullivan believes that Digital Twin technology does have a place in the delivery of healthcare. Rather than thinking in terms of modeling the individual, a Digital Twin that models a population or a process has the potential of vastly improving both the healthcare delivery process as well as the decisions of the clinician. The trick, of course, is in identifying appropriate targets of opportunity.


Digital Twins are not simply marketing hype: they are the next stage of advanced analytics and they bring the concepts of predictive analytics to the healthcare space. While humans are usually not amenable to the sort of detailed analysis that a manufacturing process might entail, there are many places in the clinical workflow that a Digital Twin approach will offer insights that can drive efficiency and efficacy improvements.

Frost & Sullivan believes that there are significant growth opportunities in the digital health market. For independent software vendors (ISVs), there is a largely unsatisfied need for Digital Twin technology that is easy to use and self-configuring. Large IT solution providers that can integrate Digital Twins into their current offerings will also find an increasingly interested market. However, these opportunities should be considered tempered by the need to satisfy the stringent demands of the healthcare environment. The closer a Digital Twin is to a diagnostic tool, the more likely regulators will demand oversight.

In spite of its promise, Digital Twins are not a panacea and when used in something as potentially life-impacting as healthcare delivery, they demand the involvement of informed decision-makers. Digital Twins are simply a tool that can offer insight and should not be trusted as the sole arbiter of patient treatment. Nevertheless, a Digital Twin can be of great value simply by its ability to provide a baseline against which clinical decisions can be compared. In this sense, as a tool for public health applications, Digital Twins can improve the display and evaluation of patient data.

At this point though, the most valuable healthcare application of Digital Twins may be associated with the improvement of process. As this report has demonstrated, process improvements alone may justify the expense of adopting this technology. Digital Twins will initially prove themselves in healthcare as cost reduction and revenue-enhancing tools—ultimately, they will be a way to improve the patient-physician relationship.

Author: Michael Jude