Why Digital Twins Are the Future of Healthcare/Pharma Innovation

For decades, the pharmaceutical industry has grappled with long development cycles, high R&D costs, manufacturing variability, and increasingly complex therapeutic modalities. Digital twins in healthcare are becoming a practical tool for addressing these pressures with greater accuracy and fewer assumptions.

Because of their versatility, digital twins have seen a huge boom in market value. The global digital twin market is projected to grow from $17.73 billion in 2024 to $259.32 billion by 2032, showing a compound annual growth rate (CAGR) of 39.8%.

In the pharmaceutical and healthcare sectors specifically, digital twins are seeing an even greater rate of expansion—the global healthcare digital twin market was valued at $1.17 billion in 2022 and is estimated to reach $38.43 billion by 2032, growing at an impressive CAGR of 42.2%.

As the life sciences and healthcare industries shift toward personalization, automation, and predictive analytics, digital twins stand out as a technology capable of providing real‑time insights, reducing risk, and accelerating scientific progress. But before we talk about just how this is accomplished, let’s explore what digital twins actually are.

What are Digital Twins?

Digital twins are virtual models that mirror the structure, behavior, and performance of physical systems using real‑time data, simulation engines, AI, and advanced analytics. Their architecture typically includes:

A physical element (such as a manufacturing line or biological system)
A virtual model capable of simulating behavior
Bidirectional data integration connecting the two

Digital twins can transform traditionally reactive pharmaceutical systems into intelligent, predictive, and resilient operations. This real‑time coupling differentiates digital twins from older computational models, enabling more comprehensive system‑level insight and enhanced predictive accuracy.

Digital Twin Use Cases in Pharma

Digital twins can enhance decision‑making, optimize processes, and strengthen regulatory compliance, making them a cornerstone of next‑generation pharma and healthcare operations.

Let’s take a look at how digital twins are used in different areas of pharmaceutical and healthcare innovation.

Drug Discovery and Development

Digital twins support early discovery by reducing dependence on time‑consuming experimental cycles. These models help researchers explore drug–target interactions, assess molecular behavior through in silico methods, and reduce early attrition by identifying potential issues before they move into the lab.

Digital Patient Twins (DPTs) expand this capability. These individualized models integrate biological, physiological, and behavioral data to simulate patient‑specific treatment effects. With DPTs, researchers can test dosing strategies, assess therapeutic responses, and anticipate safety considerations with more clarity than traditional population‑level models allow.

Clinical Trials

Digital twins offer practical ways to improve study design and reduce operational friction during clinical trials. Virtual cohorts, built from historical and real‑world evidence, can serve as synthetic control groups. This approach is particularly helpful in rare disease research, where patient recruitment and placebo use pose ethical and logistical challenges.

Trial sponsors also use simulation to test protocol changes before implementation. This helps teams anticipate enrollment challenges, refine eligibility criteria, and understand how different design choices may influence outcomes.

Pharmaceutical Manufacturing

Manufacturing environments benefit from digital twins through improved oversight and stronger process reliability. Digital twins connect to key systems such as PLCs, MES platforms, and cloud‑based analytics—which support stronger equipment monitoring, more consistent process performance, and clearer visibility into production conditions.

These models also shorten technology transfer timelines by allowing scientists and engineers to experiment with scale‑up conditions virtually before applying them to physical systems. Through simulating production processes and reducing trial‑and‑error efforts, digital twins can cut technology transfer timelines from 12–18 months to as little as 6–9 months.

Benefits of Digital Twins

Digital twins have a variety of benefits, which is why their use in pharmaceutical and healthcare innovation has continually increased. Here are some key benefits of digital twin technology:

Shorter R&D timelines: Digital twins streamline workflows by reducing unnecessary experiments and offering a more organized way to test ideas before allocating physical resources. This has shown significant improvements in discovery speed, trial optimization, and regulatory readiness.
More reliable manufacturing output: Digital twins improve batch quality, reduce deviations, and enable proactive intervention through predictive analytics and PAT integration. Advanced digital systems—including digital twins—have reduced batch failure rates from 5–7% to under 2% in pilot programs.
Stronger regulatory compliance: Regulators increasingly emphasize lifecycle model validation, transparency, and explainability in AI systems. Digital twins, when properly validated, meet these expectations by providing interpretable, data‑rich insights into processes and outcomes.
Improved personalization in medicine: DPTs expand the industry’s capacity to personalize medicine. They allow researchers to simulate individual responses and examine how genetic or physiological characteristics influence outcomes. This approach strengthens treatment planning and supports safer dosing strategies.
Better supply chain optimization: Digital twins have the ability to strengthen supply chain resilience, support sustainability initiatives, and optimize logistics networks. These end‑to‑end insights help manufacturers navigate market volatility and regulatory pressures more effectively.

However, like all technology, even with all these benefits there are still some limitations to be aware of.

Limitations of Digital Twins

Since digital twins are an evolving technology, there are still obstacles to overcome when implementing these models. Limitations and risks of digital twin models include:

Data quality and integration: Twins depend on reliable, well‑structured data. Programs need consistent ontologies, metadata, and lineage tracking, along with risk‑based controls for data acquisition and bias—otherwise their results will beinaccurate.
Validation, verification, and uncertainty quantification (VVUQ): Digital twin model performance can degrade as real‑world conditions change. To combat this, FDA recommendations include lifecycle governance, model versioning, continuous monitoring, and inspection‑ready documentation.
Human‑system interaction: Poor interface design and inadequate operator training can lead to misunderstanding or misuse of model outputs. This is where the need for human‑in‑the‑loop design and simulation‑based training comes in.
Compute, cost, and legacy constraints: High‑fidelity or hybrid twins can be expensive to build and run. In addition, migrating legacy frameworks over to new digital twin models can also be costly and time-consuming.

Although the application of digital twins still has barriers to overcome, the benefits and potential for even greater advancement of the technology has made these models a worthwhile investment for the pharmaceutical and healthcare industries.

Where Digital Twin Adoption Currently Stands

Within the pharmaceutical industry, adoption of digital twins is growing but remains uneven. Hexagon’s 2025 Transforming Pharmaceutical Manufacturing Survey found that only 17% of pharma manufacturing decision-makers currently operatea facility‑level digital twin, while 79% use twins in new projects, especially for design and collaboration. Respondents reported ongoing obstacles such as data silos, audit burdens, and IoT cybersecurity requirements.

Meanwhile, ISPE’s Pharma 4.0 Survey shows that maturity is progressing in stages. They found consistent interest in enabling technologies with wide variation in site readiness, reinforcing the idea that adoption unfolds over multiple capability layers.

Although progress isn’t linear across the industry, it’s certainly seen as a goal to strive for to advance innovation capabilities. As more organizations implement digital twin technology in their operations, it becomes a race to outpace competition by unlocking their full potential.

Digital Twins Can Enhance Pharma and Healthcare Operations

Digital twins are redefining innovation within the pharmaceutical and healthcare sectors. But companies that want to adopt digital twins effectively need more than technology alone. They need teams equipped with scientific, digital, and operational expertise to apply these tools within real‑world constraints.

Insight Global can support organizations looking to make this move. As a trusted functional service provider in the life sciences industry, Insight Global delivers the talent and support needed to operationalize advanced technologies like digital twins. Contact us to get started.