Comparing Digital Twins and Traditional Simulation in Healthcare

Simulation, as a technology, is nothing new and has been around for decades now. For example, when an oxygen tank exploded and put the astronauts at risk during the Apollo 13 mission in the 1960s, the ground engineers were able to safely bring them back to Earth by simulating a carbon dioxide filter replacement, which the astronauts then recreated on board.

There goes… Simulation back in the 1960s—half a century ago!

There are examples of simulations that predate this example, but the digital twins is comparatively a much newer concept, introduced at the turn of the 21st century. Though the primary objective of digital twins and traditional simulation is to enhance decision-making and operational efficiency, they differ significantly in their scope, capabilities, and real-time adaptability.

Also, a study by McKinsey & Company says that “digital twins reduce the time needed to deploy new AI-driven capabilities by up to 60 percent and capital expenditures and operating expenditures by up to 15 percent. They can improve commercial efficiency by about 10 percent.”

In this post, we shall look at digital twins vs traditional simulation in healthcare and how the application of both differs with regard to healthcare digital transformation.

Definition: Digital Twins vs Traditional Simulation in Healthcare

Traditional Simulation
This is a computer-based modeling technique used to recreate clinical processes, diseases, or operational scenarios. Traditional healthcare simulation is characterized by predefined rules and static inputs. It is most commonly used in surgical training, workflow modeling, and disease progression studies.

Digital Twins
Digital Twins in healthcare represents a real-time, continuously updating virtual replica of a patient, device, organ, or healthcare system. It synchronizes with live data from EHRs, wearables, IoT medical devices, imaging systems, etc., which uses AI, machine learning, and analytics to predict outcomes dynamically.

Traditional Simulation Vs Digital Twins: Key Differentiators

1. Data Dynamics: Static Vs Real-time:

Traditional simulation technology in healthcare heavily depends on static or historical datasets, but the limitation with it is that, once created, the model stays the same until someone manually reconfigures it.

On the other hand, digital twins in healthcare rely on real-time interoperability. Each new data input updates the twin, enabling it to adjust predictions instantly. This helps clinicians respond proactively to deteriorating conditions or risks.

Example: While a traditional simulator estimates how heart failure patients respond to a new treatment using generalized assumptions, digital twins are patient-specific digital models that can forecast unique responses based on patients’ vitals, lifestyle data, comorbidities, and medication history.

2. Personalization or Precision

Traditional simulations lack true personalization capabilities, as they are mostly designed for groups or generic use cases.

In contrast, digital twins in healthcare are designed to give an individual-specific view of a patient’s physiology, risk, and expected outcomes. This results in transforming generalized treatment into fully personalized patient care.

3. Intelligence and Predictive Power

Traditional simulation technology in healthcare is deterministic—it only predicts outcomes based on the scenarios it was programmed for.

Digital twins in healthcare combine real-time data with AI to:

• predict clinical deterioration
• estimate treatment response
• simulate multiple “what-if” interventions
• flag anomalies early
• optimize hospital operations dynamically

These predictive capabilities help improve outcomes, reduce medical errors, and enable proactive intervention.

4. Interactivity and Adaptability

Clinicians can simulate different scenarios in traditional healthcare simulation, but the results would remain static unless and until the model is recalculated.

A digital twin is more dynamic, as in, a clinician can change the dose of a drug, change the ventilator setting, or simulate a surgery, and the twin would show in real-time how the real patient would respond.

This adaptability is crucial in critical care, emergency medicine, and chronic disease management.

Benefits of Digital Twins over Traditional Simulation in Healthcare

The table below helps you understand the difference between digital twins and traditional twins and how the former wins over the latter in terms of benefits:

Applications in Healthcare

Traditional Simulation
Traditional simulations are comparatively simpler technology to set up and are cost-effective. They are good for:

• Basic disease modeling
• Surgical practice and rehearsal
• Clinical education and training
• Operational planning at a high level

Digital Twins
Digital twins is a more advanced technology and has wider applications beyond patient care. It can even be used to simulate an entire hospital ecosystem in real time. It finds its applications in:

• Virtual clinical trials
• Personalized treatment planning
• ICU and remote patient monitoring
• Predictive alert for the deteriorating condition of patients
• Hospital capacity and workflow optimization
• Medical device testing and calibration
• Chronic disease management using continuous data

Traditional Simulation Vs Digital Twins: Advantages and Limitations

Conclusion

As we can see, traditional modelling laid the foundation for digital modeling around the late 1950s, but it was not until the early 2000s that the idea of digital twins came into existence. Digital twins leveraged live data, AI-driven intelligence, and continuous learning to contribute to the sector more than what traditional simulation technology in healthcare could do earlier. Together, they represent an evolution of simulation—from static, generalized models to dynamic, personalized, predictive systems.

Nevertheless, be it traditional simulation or digital twins, data is the main fuel on which either runs, and proper integration of these technologies with your legacy systems would ensure optimum results. For those looking for healthcare digital transformation, you’d need the help of experienced partners like DeepKnit AI that have expertise in providing AI solutions to healthcare and enterprise clients of different scales.