21 ways medical digital twins will transform health care
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The health care industry is starting to adopt digital twins to improve personalized medicine, health care organization performance, and new medicines and devices. Although simulations have been around for some time, today’s medical digital twins represent an important new take. These digital twins can create useful models based on information from wearable devices, omics, and patient records to connect the dots across processes that span patients, doctors, and health care organizations, as well as drug and device manufacturers.
It is still early days, but the field of digital twins is expanding quickly based on advances in real-time data feeds, machine learning, and AR/VR. As a result, digital twins could dramatically shift how we diagnose and treat patients, and help realign incentives for improving health. Some proponents liken the current state of digital twins to where the human genome project was 20 years ago, and it may require a similar large-scale effort to take shape fully. A team of Swedish researchers recently wrote, “Given the importance of the medical problem, the potential of digital twins merits concerted research efforts on a scale similar to those involved in the HGP.”
While such a “moon shot” effort may not be immediately underway, there are many indicators that digital twins are gaining traction in medicine. Presented here are 21 ways digital twins are starting to shape health care today, broken roughly into personalized medicine, improving health care organizations, and drug and medical devices and development. In fact, many types of digital twins span multiple use cases and even categories; it is these cross-domain use-cases that form a major strength of digital twins.
Digital twins show tremendous promise in making it easier to customize medical treatments to individuals based on their unique genetic makeup, anatomy, behavior, and other factors. As a result, researchers are starting to call on the medical community to collaborate on scaling digital twins from one-off projects to mass personalization platforms on par with today’s advanced customer data platforms.
1. Virtual organs
Several vendors have all been working on virtual hearts that can be customized to individual patients and updated to understand the progression of diseases over time or understand the response to new drugs, treatments, or surgical interventions. Philip HeartModel simulates a virtual heart, starting with the company’s ultrasound equipment. Siemens Healthineers has been working on a digital twin of the heart to improve drug treatment and simulate cardiac catheter interventions. European startup FEops has already received regulatory approval and commercialized the FEops Heartguide platform. It combines a patient-specific replica of the heart with AI-enabled anatomical analysis to improve the study and treatment of structural heart diseases.
Dassault launched its Living Heart Project in 2014 to crowdsource a virtual twin of the human heart. The project has evolved as an open source collaboration among medical researchers, surgeons, medical device manufacturers, and drug companies. Meanwhile, the company’s Living Brain project is guiding epilepsy treatment and tracking the progression of neurodegenerative diseases. The company has organized similar efforts for lungs, knees, eyes, and other systems.
“This is a missing scientific foundation for digital health able to power technologies such as AI and VR and usher in a new era of innovation,” Dassault senior director of virtual human modeling Steve Levine told VentureBeat. He added that this “could have an even greater impact on society than what we have seen in telecommunications.”
2. Genomic medicine
Swedish researchers have been mapping mice RNA into a digital twin that can help predict the effect of different types and doses of arthritis drugs. The goal is to personalize human diagnosis and treatment using RNA. The researchers observed that medication does not work about 40% to 70% of the time. Similar techniques are also mapping the characteristics of human T-cells that play a crucial role in immune defense. These maps can help diagnose many common diseases earlier when they are more effective and cheaper to treat.
3. Personalized health information
The pandemic has helped fuel the growth of digital health services that help people assess and address simple medical conditions using AI. For example, Babylon Health‘s Healthcheck App captures health data into digital twins. It works with manually entered data such as health histories, a mood tracker, symptom tracker, and automatic capture from fitness devices and wearables like the Apple Watch. The digital twin can provide basic front-line information or help guide priorities and interactions with doctors to address more severe or persistent conditions.
4. Customize drug treatment
The Empa research center in Switzerland is working on digital twins to optimize drug dosage for people afflicted by chronic pain. Characteristics such as age and lifestyle help customize the digital twin to help predict the effects of pain medications. In addition, patient reports about the effectiveness of different dosages calibrate digital twin accuracy.
5. Scanning the whole body
Most approaches to digital twins build on existing equipment to capture the appropriate data, while Q Bio’s new Gemini Digital Twin platform starts with a whole-body scan. The company claims to capture a whole-body scan in 15 minutes without radiation or breath holds, using advanced computational physics models that are more precise than conventional MRI for many diagnoses. The company has received over $80 million from Andreessen Horowitz, Kaiser Foundation Hospitals, and others. Q Bio is also developing integrations to improve these models using data from genetics, chemistry, anatomy, lifestyle, and medical history.
6. Planning surgery
A Boston hospital has been working with Dassault’s digital heart to improve surgical procedure planning and assess the outcomes afterward. The digital twins also help them to generate the shape of a cuff between the heart and arteries.
Sim&Cure’s Sim&Size is a digital twin to help brain surgeons treat aneurysms using simulations to improve patient safety. Aneurysms are enlarged blood vessels that can result in clots or strokes. These digital twins can improve the ability to plan and execute less invasive surgery using catheters to install unique implants. Data from individual patients helps customize simulations that run on an embedded simulation package from Ansys. Preliminary results have dramatically reduced the need for follow-up surgery.
Improving health care organizations
Digital twins also show promise in improving the way health care organizations deliver care. Gartner coined the term digital twin of the organizations to describe this process of modeling how an organization operates to improve underlying processes.
In most industries, this can start by using process mining to discover variations in business processes. New health care-specific tools can complement these techniques.
7. Improving caregiver experience
Digital twins can also help caregivers capture and find information shared across physicians and multiple specialists. John Snow Labs CTO David Talby said, “We’re generating more data than ever before, and no one has time to sort through it all.” For example, if a person sees their regular primary care physician, they will have a baseline understanding of the patient, their medical history, and medications. If the same patient sees a specialist, they may be asked many of the same repetitive questions.
A digital twin can model the patient and then use technologies like NLP to understand all of the data and cut through the noise to summarize what’s going on. This saves time and improves the accuracy of capturing and presenting information like specific medications, health conditions, and more details that providers need to know in context to make clinical decisions.
8. Driving efficiency
The GE Healthcare Command Center is a major initiative to virtualize hospitals and test the impact of various decisions on changes in overall organizational performance. Involved are modules for evaluating changes in operational strategy, capacities, staffing, and care delivery models to objectively determine which actions to take. For example, they have developed modules to estimate the impact of bed configurations on care levels, optimize surgical schedules, improve facility design, and optimize staff levels. This allows managers to test various ideas without having to run a pilot. Dozens of organizations are already using this platform, GE said.
9. Shrinking critical treatment window
Siemens Healthineers has been working with the Medical University of South Carolina to improve the hospital’s daily routine through workflow analysis, system redesign, and process improvement methodologies. For example, they are working to reduce the time to treat stroke patients. This is important since early treatment is critical but requires the coordination of several processes to perform smoothly.
10. Value-based health care
The rising cost of health care has many nations exploring new incentive models to better align new drugs, interventions, and treatments with outcomes. Value-based health care is one approach that is growing in popularity. The basic idea is that participants, like drug companies, will only get compensation proportionate to their impact on the outcomes. This will require the development of new types of relationships across multiple players in the health delivery systems. Digital twins could provide the enabling infrastructure for organizing the details for crafting these new types of arrangements.
11. Supply chain resilience
The pandemic illustrated how brittle modern supply chains could be. Health care organizations immediately faced shortages of essential personal protection equipment owing to shutdowns and restrictions from countries like China. Digital twins of a supply chain can help health care organizations model their supply chain relationships to understand better how to plan around new events, shutdowns, or shortages. This can boost planning and negotiations with government officials in a pinch, as was the case in the recent pandemic. A recent Accenture survey found that 87% of health care executives say digital twins are becoming essential to their organization’s ability to collaborate in strategic ecosystem partnerships.
12. Faster hospital construction
Digital twins could also help streamline construction of medical facilities required to keep up with rapid changes, such as were seen in the pandemic. Atlas Construction developed a digital twin platform to help organize all the details for health care construction. The project was inspired long before the pandemic when Atlas founder Paul Teschner saw how hard it was to get new facilities built in remote areas of the world. The platform helps organize design, procurement, and construction processes. It is built on top of the Oracle Cloud platform and Primavera Unifier asset lifecycle management service.
13. Streamlining call center interactions
Digital twins can make it easier for customer service agents to understand and communicate with patients. For example, a large insurance provider used a TigerGraph graph database to integrate data from over 200 sources to create a full longitudinal health history of every member. “This level of detail paints a clear picture of the members current and historical medical situation,” said TigerGraph health care industry practice lead Andrew Anderson.
A holistic view of all diagnosis claims prescriptions, refills, follow-up visits, and outstanding claims reduced call handling time by 10%, TigerGraph claimed, resulting in over $100 million in estimated savings. Also, shorter but more relevant conversations between the agents and members have increased Net Promoter Score and lowered churn.
Drug and medical device development
There are many ways that digital twins can improve the design, development, testing, and monitoring of new medical devices and drugs. The U.S. FDA has launched a significant program to drive the adoption of various types of digital approaches. Regulators in the U.S. and Europe are also identifying frameworks for including modeling and simulation as sources of evidence in new drug and device approvals.
14. Software-as-a-medical device
The FDA is creating the regulatory framework to allow companies to certify and sell software-as-a-medical device. The core idea is to generate a patient-specific digital twin from different data sources, including lab tests, ultrasound, imaging devices, and genetic tests. In addition, digital twins can also help optimize the software in medical devices such as pacemakers, automated insulin pumps, and novel brain treatments.
15. Classifying drug risks
Pharmaceutical researchers are using digital twins to explore the heart risks of various drugs. This could help improve drug safety of individual drugs and drug combinations more cost-effectively than through manual testing. They have built a basic model for 23 drugs. Extending this model could help reduce the estimated $2.5 billion required to design, test, get approved, and launch new drugs.
16. Simulating new production lines
Siemens worked with several vaccine manufacturers to design and test various vaccine production line configurations. New mRNA vaccines are fragile and must be precisely combined using microfluidic production lines that precisely combine nanoscale-sized particles. Digital twins allowed them to design and validate the manufacturing devices, scale these processes, and accelerate its launch from 1 year down to 5 months.
17. Improve device uptime
Philips has launched a predictive maintenance program that collates data from over 15,000 medical imaging devices. The company is hoping that digital twins could improve uptime and help their engineers customize new equipment for the needs of different customers. In addition, it is hoping to apply similar principles across all of its medical equipment.
18. Post-market surveillance
Regulators are beginning to increase the emphasis for device makers to monitor the results of their equipment after-sales as part of a process called post-market surveillance. This requires either staffing expensive specialists to maintain the equipment or embedding digital twins capabilities into the equipment. For example, Sysmex worked with PTC to incorporate performance testing into its blood analyzer to receive a waiver from these new requirements, PTC CTO Steve Dertien told VentureBeat. This opened the market for smaller clinical settings closer to patients, which can speed diagnosis.
19. Simulating human variability
Skeletons and atlases commonly depict the perfect human. However, real-life humans typically have some minor variations in their muscles or bones that mostly go unnoticed. As a result, medical device makers struggle with how common anatomical variations among people may affect the fit and performance of their equipment. Virtonomy has developed a library of common variations to help medical equipment makers test conduct studies on how these variations may affect the performance and safety of new devices. In this case, they simulate the characteristics representing common variations in a given population rather than individuals.
20. Digital twin of a lab
Modern drug development often requires testing out thousands or millions of possibilities in a highly controlled environment. A digital twin of the lab can help to automate these facilities. It can also help to prioritize tests in response to discoveries. Digital twins could also improve the reproducibility of experiments across labs and personnel in the same lab. In this quest, Artificial recently closed $21.5 million in series A funding from Microsoft and others to develop lab automation software. The company is betting that unified data models and platforms could help them jump to the front of the $10 billion lab automation market.
21. Improving drug delivery
Researchers at Oklahoma State have been working with Ansys to develop a digital twin to improve drug delivery using models of simulated lungs as part of the Virtual Human System project. They found that only about 20% of many drugs reached their target. The digital twins allowed them to redesign the drug’s particle size and composition characteristics to improve delivery efficiency to 90%.
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