Digital twin technology is already widely used in engineering and manufacturing, researchers are now attempting to apply the same ideas to the field of medicine. Researchers can use digital twins to discover disease trends, simulate the impact of treatments, and find the most promising research areas among real people.
In the past, due to their excessively high building costs, it was unusual to see digital twins used for purposes other than industrial manufacture. Prolific and affordable new technologies have decreased the barrier to entry, expanding the use cases for this innovative technology and making digital twins more accessible. Thanks to that continued trend, digital twin technology is transforming how the life sciences and healthcare industry work to transform the lives of the people they serve.
Focusing on patient-centric engagement & creating personalized products/service offerings to improve patient outcomes and patient trust.
Accelerating adoption of decentralized / hybrid clinical trials by CROs, and biopharma, enabled through telehealth, RPM, mobile/wearable technologies.
Redesigning manufacturing and supply chain by scaling up smart manufacturing and end-to-end supply chain visibility through digitization and focusing on sustainability with initiatives like Energize program.
Evolving operating models and digital enablement by developing new capabilities that enhance their business and operating models while organizations are struggling with how to create a holistic, omnichannel customer experience.
It is widely known that the life sciences are undergoing a fundamental shift toward embracing digital technologies. With the limited time and resources at their disposal, researchers have been working hard for years to keep up with their field’s expanding complexity, even while new fields like genomics and personalized medicine dramatically speed up the process of discovery and development.
Digital twin applications in the life sciences can be divided into two types: biological and experimental. Even while it’s still a long shot that a complete biological model of a cell or a person will be realized, experimental digital twins have the power to transform clinical research. As of now much focus has been dedicated to using digital twins in manufacturing and modeling.
Physical experiments are hugely costly: This is where digital twins can be a useful asset in R&D. Digital twins can perform several test scenarios at once, unlike life science researchers who may require months or even years of concentrated effort to sort and analyze data. Additionally, automation of testing enables clinicians to quickly generate and reproduce trial settings across sites and employees, which are frequently carried out in extremely controlled environments.
Disparate Data, no single “source of truth”: With the use of AI-powered models and data collected from various sources, digital twins can offer more personalized and effective treatment suggestions. It may have a significant effect on how chronic diseases are managed. Additionally, it has a broad scope for developing drugs, manufacturing devices, hospital management, and personalized products/services.
Supply Chain Reliability & Resiliency: Digital twins help industry analysts understand a supply chain’s behavior, predict unusual situations, and provide an action plan to reduce costs and improve the efficiency of processes.
Keeping Up with Evolving Clinical Trials: Digital twins of patients in clinical trials are created by training machine-learning models on patient data from previous clinical trials and from individual patient records. The model predicts how the patient’s health would progress during the trial if they were given a placebo, essentially creating a simulated control group for a particular patient.
Digital twins in Devices Trials and Manufacturing: With digital twins, both aspects can be addressed easily while enhancing performance and unlocking more accurate monitoring of medical devices. In the devices segment, the digital twins of a medical device allow the developer or manufacturers to test the features or uses of a device as it can mimic the device’s behavior and functions.
The Digital Twin is now in its formative stages. Several key developments, innovations, and research are going on to execute its application in the industry. Life sciences organizations, providers, and companies looking to encourage the use of digital twins should focus on the below pointers:
Patient-Centricity and Co-Creation: Deliver connected care experience, personalized services, solutions, and experiences throughout the patient journey to improve patient trust and outcomes by proactively collaborating with the right partners including consumer technology or digital health companies.
Evolving Operating Models & Digital Enablement: Evolving digital strategy to meet new realities & shifting from ‘doing digital’ to ‘being digital. Scaling up investments in technologies like Cloud, AI, analytics & automation to gain a competitive edge.
Streamlined, Sustainable & Cost-Efficient Operations: Improve operational efficiency, support data sharing with customers/ partners, track carbon footprint across value chain & forecast supply chain disruptions to build resilience and gain competitive advantage.
Digital Twin enables Profound Transformation in Lifesciences by Virtualization, Simulation & Optimization of Products, Services & Processes across Value-Chain, Enabling Organizations to deliver Personalized & Disruptive Medical Innovations.
