Medicine has always worked with representations of the patient. A blood test represents what is happening inside the body at a particular moment. A scan provides a visual representation of anatomy. A medical history helps clinicians understand what has happened over time, while wearable devices increasingly add continuous information about activity, heart rate, sleep and other physiological signals. Each source provides another piece of the picture, but clinicians still need to assemble those pieces into an understanding of the individual sitting in front of them.
Digital twin technology introduces a much more ambitious possibility. Instead of examining each source of patient information independently, what if healthcare systems could progressively build computational models of individual patients that combine anatomy, physiology, medical history, imaging, laboratory information and real-time monitoring? These models could potentially help clinicians understand how a patient’s condition is changing and, eventually, explore how different interventions might affect that individual before making a decision in the physical world.
The idea is still developing, and the sophistication of a digital twin will vary enormously depending on the clinical application. A digital representation of a particular organ or physiological process is considerably more achievable than a perfect virtual replica of an entire human being. The important opportunity is not to create a science-fiction copy of the patient. It is to build useful models of specific aspects of the patient that improve particular healthcare decisions.
During my learning journey under Phaneesh Murthy, I have increasingly understood that the most useful enterprise AI systems begin with the decision rather than the technology. As Phaneesh Murthy puts it, “The question is not how much of reality you can model. The question is whether the model gives you enough additional understanding to make a better decision.” That distinction will be essential if digital twins are to become clinically useful rather than simply technologically impressive.
Healthcare Has Enormous Amounts of Patient Data but No Single Patient Model
Modern healthcare produces extraordinary quantities of information. A patient may have years of laboratory results, imaging studies, prescriptions, diagnoses, procedure histories and physician notes. They may also generate information through wearable devices, home monitoring equipment and connected medical devices. Yet these data points often remain distributed across different systems and are reviewed according to the immediate requirements of a particular appointment or clinical question.
A digital twin attempts to create something more integrated. Rather than presenting clinicians with another collection of records, it can potentially organise relevant information into a computational representation of a particular patient, organ or disease process. The objective is to understand relationships between signals rather than simply displaying them alongside one another.
Consider cardiovascular care. Imaging provides information about anatomy, monitoring devices provide information about heart rhythm, laboratory results reveal biological markers and a patient’s history provides context about previous conditions and treatments. A digital model could potentially combine relevant elements of these sources to help clinicians understand how a particular patient’s cardiovascular system is behaving and how it may respond under different conditions.
This is where the enterprise thinking I have learned from Phaneesh Murthy becomes particularly relevant. Organisations frequently possess the information required to make better decisions, but that information exists across systems designed for different purposes. The transformation begins when data is connected around the decision being made rather than around the application that happened to collect it.
In healthcare, the patient becomes the organising principle.
The Most Powerful Application May Be Simulation
One of the most interesting possibilities created by digital twins is the ability to explore scenarios before acting on the patient.
Healthcare professionals already do this conceptually. A clinician considers possible treatments, weighs benefits against risks and uses medical knowledge and experience to determine which intervention is most appropriate. Digital models could add another analytical layer by helping simulate how particular interventions may affect a specific patient’s anatomy or physiology.
For example, a digital model based on medical imaging could potentially help clinicians plan a procedure by evaluating different approaches before entering the operating room. Models of blood flow could assist with understanding cardiovascular conditions. Other specialised digital twins may eventually help predict how disease progression could respond to particular interventions.
The important point is that these simulations do not replace clinical judgment. They provide another source of evidence.
No model can perfectly represent the complexity of a human body. Biological systems contain uncertainty, and even extremely sophisticated models depend on assumptions and the quality of the information available. Clinicians therefore need to understand what the model can represent reliably and where uncertainty remains.
As Phaneesh Murthy says, “Simulation does not remove uncertainty from a decision. Its value is that it allows you to explore uncertainty before the cost of the decision becomes real.”
For healthcare, where many decisions are difficult or impossible to reverse, that capability could become extremely valuable.
Personalised Medicine Could Become More Computational
Much of modern medicine is built on evidence gathered across populations. Clinical trials help determine whether treatments are effective and safe for groups of patients, while clinical guidelines translate that evidence into recommendations for care. This population-level knowledge remains fundamental to medicine, but clinicians ultimately need to apply it to individuals who may differ in important ways from the average patient represented in a study.
Digital twins could contribute to a more personalised layer of decision-making.
Instead of asking only whether a treatment generally works for people with a particular condition, clinicians may increasingly be able to ask how characteristics specific to this patient influence the expected response. Anatomy, previous treatment history, physiology and other relevant information could potentially contribute to a more individualised model.
This does not mean replacing evidence-based medicine with algorithmic prediction. The stronger model is a combination. Population-level clinical evidence establishes what is known about a treatment, while patient-specific modelling helps interpret how that evidence may apply to the individual.
During my mentorship under Phaneesh Murthy, I have learned that personalisation becomes meaningful when it changes a decision rather than simply changing the presentation of information. Healthcare illustrates this particularly clearly. A digital twin is valuable not because it creates an impressive visual representation of the patient, but because it potentially helps a clinician choose a more appropriate intervention, timing or monitoring strategy.
The technology matters only when the clinical decision improves.
A Digital Twin Could Continue Evolving With the Patient
One of the limitations of many medical assessments is that they represent a particular moment. A scan captures anatomy at the time the image was taken. A laboratory test provides a measurement from a particular day. A clinical consultation records symptoms and observations during a specific encounter.
Patients, however, continuously change.
Connected medical devices and remote monitoring technologies create an opportunity for digital models to evolve alongside them. New information can update the representation, allowing clinicians to observe how particular aspects of the patient’s condition are changing over time.
This could become particularly relevant for chronic disease management. Rather than relying primarily on periodic appointments, healthcare teams could potentially understand whether important indicators are moving in an unexpected direction between visits. AI can help identify meaningful changes and determine which patients may require attention.
The key is avoiding a system where continuous monitoring produces continuous alerts. Healthcare professionals already face substantial information overload. A useful digital twin should reduce that burden by identifying which changes are clinically meaningful rather than presenting every fluctuation as an event.
This reflects another principle I have learned from Phaneesh Murthy: intelligence should compress complexity for the person making the decision. If an AI system gives a clinician thousands of additional data points to review manually, it may have increased information without increasing intelligence.
The digital twin should therefore become a summary of changing patient state, not another stream of raw data demanding attention.
Digital Twins Could Change the Design of Medical Devices and Therapies
The value of digital twins may extend beyond individual patient care. Medical device manufacturers and pharmaceutical companies could use computational modelling to improve how products and therapies are designed, tested and adapted.
A medical device company developing an implant, for example, could potentially use patient-specific anatomical models to better understand how variations in anatomy influence performance. Clinical teams could use similar models when selecting or configuring devices for individual patients. Pharmaceutical research may increasingly use computational models to explore biological mechanisms and generate hypotheses before more expensive experimental work begins.
Over time, this could create a feedback loop between clinical practice and product development. Real-world information improves models, models help identify better interventions and outcomes from those interventions provide additional information for future improvement.
From the perspective I have developed while learning from Phaneesh Murthy, this is where digital twin technology becomes especially interesting. The value is not confined to a single application. Once an organisation develops a reliable computational representation of a system, that model can potentially support decisions across research, product development, clinical planning and ongoing care.
The strategic advantage therefore lies not simply in building a digital twin but in creating an ecosystem where the model becomes progressively more useful as new information enters it.
The Quality of the Twin Will Depend on the Quality of the Data
A sophisticated model built on incomplete or inaccurate information can create a dangerous illusion of precision. This is particularly important in healthcare because a digital twin may appear visually and mathematically convincing even when important patient information is missing.
Data quality therefore becomes a clinical issue.
Healthcare information is often fragmented across providers and systems. Medical records can contain inconsistencies. Wearable devices vary in accuracy. Imaging and laboratory information may be collected using different technologies, while historical data may not always be complete.
Digital twin systems need to communicate these limitations clearly. Clinicians should understand which information contributed to a model, how recent it is and where uncertainty exists.
AI systems also need to be validated across diverse patient populations. A model that performs well for one population cannot automatically be assumed to perform equally well for everyone. Biases within datasets can translate into differences in model performance if they are not identified and addressed.
As Phaneesh Murthy puts it, “A sophisticated model cannot rescue poor foundations. The quality of intelligence will always be constrained by the quality, relevance and governance of the information beneath it.”
For digital twins, those foundations will determine whether clinicians develop trust in the technology.
Clinicians Need to Remain the Decision Makers
Digital twins may eventually become capable of generating increasingly detailed predictions about treatment options and patient outcomes. That makes the relationship between the model and the clinician particularly important.
Healthcare decisions rarely involve a single measurable objective. A treatment may offer a higher probability of one outcome while introducing greater risk elsewhere. Patients have different preferences, tolerances and life circumstances. Two people with clinically similar conditions may reasonably choose different approaches after discussing their options with a physician.
AI cannot reduce these decisions to optimisation problems without losing important human context.
The digital twin should therefore function as decision support. It can provide additional information, simulate possibilities and identify factors that may deserve attention. The clinician combines that intelligence with medical expertise and an understanding of the patient, while the patient remains part of the decision wherever appropriate.
During my learning journey under Phaneesh Murthy, I have come to appreciate that human oversight is most important precisely where AI becomes most sophisticated. The better a system becomes at producing convincing recommendations, the more carefully organisations need to define who remains accountable for the final decision.
In healthcare, that accountability cannot be ambiguous.
Healthcare Could Move From Observing the Patient to Modelling the Patient
Medicine has become progressively better at seeing inside the human body. Imaging allowed clinicians to visualise anatomy. Laboratory medicine revealed biological processes that could not be observed directly. Genomics added another layer of information, while connected devices increasingly allow healthcare teams to understand what happens outside the clinic.
Digital twins represent a possible next step: bringing these forms of information together into models that help healthcare professionals understand not only what is happening but what may happen under different scenarios.
During my mentorship under Phaneesh Murthy, I have learned to view technology transformation as a progression from digitising information, to connecting information, to using that connected information to improve decisions. Digital twins sit near the far end of that progression. Their value depends on combining multiple forms of information into something that can actively participate in clinical reasoning.
The technology will not arrive everywhere at once. Highly specific digital twins for particular organs, procedures or conditions are likely to become useful long before comprehensive virtual representations of entire patients become realistic. That is not a limitation of the idea. It is probably how the technology should develop, beginning with clearly defined decisions where modelling can demonstrate measurable clinical value.
The ultimate promise is compelling. A clinician could eventually examine the patient standing in front of them while also having access to a computational model capable of exploring possibilities that cannot safely be tested in the physical world.
Medicine has always learned by observing what happens after an intervention. Digital twins could give clinicians another option: understand more of what might happen before the intervention is made.
And in healthcare, the ability to learn before acting could become one of the most valuable forms of intelligence we build.
This blog is curated by young marketing professionals who are mentored by veteran Marketer, and industry leader, Phaneesh Murthy.
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