Mirror, Mirror: How Digital Twins Can Rescue Healthcare

Healthcare administrators may feel overwhelmed by the constant demands for innovation while trying to maintain daily operations. The focus on mere survival leaves little room for new ideas.
However, inaction only exacerbates current challenges. Embracing artificial intelligence (AI), particularly digital twins, must be part of healthcare survival strategy. A digital twin is not merely a digital replica or virtual model; it is an advanced representation that mirrors a real-world system in real-time. Using advanced simulation, machine learning, and reasoning, a digital twin analyzes behavior and provides predictive insights to aid decision-making. Implementing a digital twin might sound daunting, but doing so is surprisingly simple.
As someone deeply involved in biomedical informatics, AI, and value-based care, I’ve advised both for- and nonprofit organizations in the United States and abroad. As a busy working mother in survival mode for the past decade, I find many of my best insights come from observing my children and managing their playdates.
In both arenas, the key to overcoming complexity is simplicity.
A systematic review recently published in Nature analyzed 12 studies that collectively assessed 45 outcomes related to the use of digital twins in healthcare. These software systems allow healthcare providers to create personalized treatment plans for patients with conditions like type 2 diabetes by improving metrics such as time-in-range for blood glucose levels and enable researchers to simulate and optimize radiotherapy regimens for cancer patients, potentially extending tumor progression time and reducing radiation doses.The overall effectiveness rate across these outcomes was 80% (36 out of 45 outcomes showed positive effects). They categorized digital twin applications into three main areas: personalized health management, precision individual therapy effects, and predicting individual risks.
Another report identified additional functions for digital twins in healthcare, including information management, promotion of well-being, and operational control. In Indonesia, digital twins have been used to simulate the spread of viruses to develop targeted strategies for vaccination on the basis of social contacts. 
Digital twins enable healthcare providers to examine different solutions to common operational problems such as financial strains, clinician turnover, burnout, and declining quality of care. Various industries— including aerospace, construction, insurance, real estate, and biomedical research — have successfully used digital twins to analyze, predict, and optimize processes, saving money, boosting efficiency, and improving outcomes. 
Despite these successes, digital twin technology has not fully penetrated healthcare operations. Administrative leaders often find themselves frustrated and unsure about where to start. They argue that the technology is oversimplified and not applicable to healthcare or that the healthcare industry is not yet ready to embrace digital twins in real-life practices.
Healthcare leaders, administrators, policymakers, and clinicians can follow these steps to swiftly onboard digital twins.
The first step is to ensure quality input data. For example, for children’s playdates, the host prepares snacks, considering food allergies and the child’s preferences. In digital twin projects, the quality of the input data is crucial. Data are the “food” that fuel the system, enabling it to function effectively and produce quality outputs. Focus on data quality before considering AI-based solutions.
Next is a readiness check. Just as a parent or childcare provider cleans up to ensure a safe play area for children, process maps are essential for creating a valid and reliable digital representation of the institution’s environment. Process maps visually detail the steps and workflows within a system. They are traditionally used by business administrators to identify potential issues and ensure that all processes are well-structured and efficient.
Assessing capacity is crucial. Does the organization have sufficient computing power and other resources to meet the demand, and can those resources be scaled to grow with the institution? What are the limits of the available hardware and software to ensure smooth operation without overloading the system?
Defining and selecting the right and few use cases and prioritizing scenarios that offer immediate value and are easily manageable are also critical steps in the implementation planning. Measure outcomes that truly matter. Focus on value, which translates to outcomes over cost.
By leveraging digital twins, healthcare entities can simulate various approaches to patient care, identifying strategies that improve patient satisfaction and outcomes while keeping costs in check.
In a digital twin project, continuous monitoring of outputs is essential. The performance and outcomes of the system must be checked regularly to ensure that it operates as intended without causing harm. In digital twin technology, this translates to closed-loop learning. When the system identifies a flaw, bias, or error, it learns from this feedback, continuously improving its processes.
Data governance, bias assessment, and cybersecurity are critical in digital twin projects. Clear rules about data usage, access, and protection are essential to protect sensitive information.
Yes, this is an intentionally simplified metaphor on an intensely complicated possibility for huge process reforms. But healthcare leaders and clinicians as well as policymakers across the country can effectively emulate clinical operations and implement digital twin technology to enhance the sustainability of healthcare systems by embracing the simplicity and structure of a child’s playdate.
Lidia Moura, MD, PhD, MPH is an OpEd Project Public Voices Fellow, director of Neurology Population Health, and director of the Center for Value-based Healthcare and Sciences with Massachusetts General Hospital. She is also a clinical neurologist at MGH and associate professor of neurology at Harvard Medical School.