Comparing Digital Twins and Traditional Simulation in Healthcare

Simulation is an area of critical importance in healthcare education. From low-fidelity mannequins in nursing labs to high-fidelity human patient cadavers in advanced surgical labs, simulators of varying degree have been used by students for decades for better understanding of diseases and conditions.

Nevertheless, advancements in technology have introduced a new variant of simulator into the mix in the recent past—the digital twins (DT). Digital twins in healthcare training is a digital copy of a physical entity that bridges physical and virtual worlds. A digital twin accurately reflects the characteristics of its physical counterpart and serves as a digital transcript of the physical entity. It pairs real-world systems with live, data-driven virtual replicas capable of real-time feedback, personalization, and predictive modeling in healthcare.

Digital twins in healthcare training replicate organs, medical devices, patients, or even entire hospitals, allowing for:

• Simulated medical training for students and professionals
• Personalized treatment planning for patients
• Predictive analytics for disease progression
• Operational efficiency in healthcare facilities

Hence, considering the wide application possibilities of medical training with digital twins, the question many educators and clinical leaders are asking is simple: will digital twins replace simulation in healthcare training?

The answer is even shorter and simpler: no, but digital replicas in healthcare will definitely enhance and redefine the concept of simulation in healthcare training.

In this post, we will look at the difference between traditional simulation and digital twin technology in healthcare and the benefits and limitations of the latter.

Traditional Simulation Vs Digital Twins in Healthcare Training

With simulation being the primary objective, traditional simulation and digital twins serve overlapping but distinct purposes.

The following table explains the difference between the two approaches:

Aspect	Traditional Simulation	Digital Twins
Main Focus	Pedagogical: experiential, hands-on skill development	Data-centric: personalized modeling, predictive analytics
Environment	Controlled, safe, repeatable scenarios	Real-world, continuously updated with real patient/device data
Key Skills Developed	Psychomotor, crisis management, teamwork, communication, decision-making	Systems analysis, forecasting, scenario branching, personalization
Learning Method	Practice, feedback, debriefing; “See One, Practice Many, Do One”	Scenario branching via physiological modeling; predictive simulations
Data Integration	Typically static or scenario-driven; limited by setup	Dynamic, real-time from EHRs, sensors, telemetry, wearables
Outcome Focus	Skill mastery; learner reflection and behavioral change	System-level optimization, predictive outcome analysis
Scenario Flexibility	Repeatable but limited to predefined cases	Adaptable, multivariate branching based on real-time data

The comparison shows that the two excel at different learning objectives. While simulation teaches hands-on skills, teamwork, and the messy human side of care, digital twins augment those lessons with personalization, long-term outcome prediction, and scalable, data-rich scenarios that evolve in realistic ways.

How Digital Twins Are Transforming Healthcare Training

1. Scale and Repeatability with Realism: Digital replicas in healthcare have the capability to generate many distinct, realistic patient cases at scale, useful for competency testing and exposure to rare conditions, as they run in software and can ingest real EHR/device datasets.

2. Personalized, Longitudinal Training: As the conditions in which digital twins run can be controlled and manipulated, they can be made to mimic a specific patient’s physiology over hours or days, allowing trainees to watch the downstream effects of treatment decisions — important for chronic disease management and ICU care simulations that are hard to compress into single sessions.

3. Systems and Workflow Training: Beyond patient care, digital twins of hospitals let teams rehearse operational decisions like triage, surge capacity management, equipment allotment, etc. with realistic scenarios and outcomes.

4. Predictive Feedback: A twin can provide trainees with immediate, evidence-based feedback about probable outcomes and long-term consequences of an intervention on a patient.

Limitations of Digital Twins in Medical Training

With all its advantages, medical training with digital twins also comes with certain limitations:

1. Hands-on Psychomotor Skills: Psychomotor skills are abilities that involve the integration of physical movements with cognitive processes, such as hand-eye coordination, dexterity, and balance. They are movement-orientated activities that require a combination of mental processing and physical execution, from simple actions like writing or typing to complex tasks like conducting surgery or playing a musical instrument. They are learnt, refined through practice, and eventually performed automatically with proficiency. You can’t practice intubation or central line placement on a purely virtual twin. Tactile feedback, instrument handling, and muscle-memory learning remain the domain of physical simulation and cadaver labs.

2. Human Factors and Emotional Realism: While digital replicas in healthcare can provide a personalized experience, the same thing alienates you from the real-world scenario where you would be working with team members of different temperaments and dealing with family members of the patients who come from different walks of life. The social, emotional, and interpersonal dynamics of a clinical team or a distraught family member are hard to reproduce solely with mathematical models. Exposure to real-world patients and in-person debriefs teaches empathy, communication, and professionalism.

3. Data Quality and Interoperability: The accuracy of digital twins entirely depends on the quality of data they receive, and at any given point in time, a digital twin receives data from different sources like EHRs, wearables and others. The biggest roadblock to the efficient implementation of the digital twin technology in healthcare is that many institutions still face fragmentation of data from different sources, device silos, and privacy constraints that limit twin fidelity.

4. Model Validity and Trust: As digital twins rely heavily on data, data bias is another major factor that undermines their relevance. Complex physiological models require validation across populations. If a twin’s predictions are biased or poorly calibrated, they could teach incorrect clinical reasoning, which would result in loss of trust.

Practical Tips for Educators

• Prioritize Data Hygiene: Reliable twins need clean, interoperable data and explicit governance for privacy and bias mitigation.
• Start Small and Evaluate: Run pilot digital twin projects with specific learning objectives like asthma management and compare the learning outcomes against traditional simulation.
• Blend Curricula: Map competencies and decide where hands-on practice, virtual practice, or twin-augmented training is most appropriate.
• Train the Trainers: Last but not least, your trainers need to be amply equipped to interpret model outputs and to debrief learners on probabilistic predictions (not certainties).

Augmentation, Not Replacement Is the Future

The most probable and realistic future is one that is hybrid, where the healthcare industry would make use of the best of the two options – traditional simulation and digital twins – with each modality answering different educational needs: simulation bringing touch, emotion, and team dynamics, while digital twins provide data-rich personalization, scale, and prediction.

The future of healthcare training will be hybrid: simulations augmented by digital twins, creating richer, more realistic, and more effective learning experiences. Hence, reiterating the initial argument: digital twins will not replace simulation in healthcare but would rather augment it.

The successful implementation of a digital twins solution calls for smart integration with legacy systems, and this can be accomplished by partnering with AI solutions experts like DeepKnit AI that have experience and capabilities in catering to the digital requirements of healthcare institutions.