Clinical trials have been the cornerstone of medical innovation for centuries, but as the effort continues to improve patient care and speed the development of new treatments and medicines, sticking with the same old trial processes is no longer an option.
Innovative technologies like digital twins and generative AI are transforming the healthcare landscape and expanding what’s possible for the future of medicine with next-gen clinical trials.
Beyond benefits associated with cost and time savings, innovative technology is paving the way for advancements in analytics, genetic mapping, cloud data access, and more. So, what does the future of clinical trials look like?
Saving time, money, and lives
Today, clinical trials cost millions of dollars per trial and can take up to 15 years to complete. When compared to the immediate care and treatment sick patients typically require, these numbers are exorbitant. But digital twins can make an immeasurable difference and revolutionize the trial process in several ways.
In a traditional clinical trial, patients must travel to the designated trial location where investigators record data that lives in a single place for future analysis. This can be time-intensive for patients who aren’t located near the site and can negatively impact their overall health and mental wellbeing.
With digital twins, decentralized trials can become the new normal. Through advanced sets of physical sensors, physicians can secure the same level and quality of data that are captured during traditional trials, but patients can be located anywhere. This saves time and eases the burden of travel for patients while also creating efficiencies for investigators. Decentralized trials make patient data storage more accessible on enterprise systems that can be accessed and analyzed from wherever a doctor is located.
Digital twins can even predict if a clinical trial is going to succeed or not, helping sponsors and organizations save immense time and money that could have potentially been wasted on an unsuccessful venture. A staggering 90% of drug development programs fail during the clinical trial phase for a variety of reasons. Digital twins can identify these factors and help to mitigate risks and wasted resources.
A common challenge with clinical trials is attrition across investigators and doctors due to the lengthy trial process. Digital twins can analyze previous trials and identify the medical staff and management’s performance history to gauge if they would be a strong fit for upcoming opportunities. There are also inconsistencies around patient participation, but with the adoption of digital twins, sponsors can easily identify how a particular area or site has behaved in the past or if there are common problems across patient populations.
The flexibility, proactivity, and historical knowledge digital twins can bring to trial decision makers means they can make faster, more informed decisions, save resources on trials doomed to fail, and invest more in the efforts that have the highest likelihood of success and patient benefit.
Getting strategic with digital twins
The use of digital twins in clinical trials is not a brand-new concept, but the adoption and implementation of this technology is beginning to expand. New use cases for digital twins are ramping up, and the results are expected to revolutionize the industry.
For example, the use of genetic mapping is being accelerated by digital twin implementation. For a person with a developing disease, digital twins can mix and match the genetic data of a particular patient with disease databases and predict the kind of health outcomes the patient will have over a period of time.
These genetic databases have historically been very expensive, but with digital twins, the price and therefore, barrier to entry is reduced substantially for customers. This technological innovation enables more cloud-based databases versus on-premises for a plug-and-play resource that can be taken advantage of by smaller, more tactical customers and large pharma companies alike.
Across both customer groups, there is a shared strategic view that digital twins are the next big thing to support breakthroughs in science, pushing tech and data to the forefront of minds in the clinical trial space and opening doors for widespread adoption.
Gen AI and clinical trials
Digital twins are not the only technological advancement making strides in the clinical trial space. Similar to almost every industry across the board, generative AI has a role to play in transforming trial development and organizations are taking an ambitious yet conservative approach as they push to keep up with the speed of innovation while answering to their more cautious stakeholders.
While there are still hesitations around generative AI when human lives are at stake, lower risk use cases, such as summarizing visit notes, managing workflows, and creating personalized patient communications are already making a measurable difference for investigators, doctors, and patients.
In the next three to five years, there will be quantum jumps in Gen AI technology, guardrails in place, and wider adoption in clinical trials, and leaders are continuing to explore the possibilities in preparation for when that exciting time comes.
Clinical trials are an imperative area where innovation cannot rest as patients hope for research and breakthroughs in lifesaving and life-improving medications and treatments. Digital twins and Gen AI are saving organizers time and money, unlocking patient and historical trial data, and opening the door to the future of clinical trials and medical innovation.
About Arijit Chatterjee
Arijit Chatterjee is the Pharma & Med Tech Solutions Engineering Leader, for Capgemini Americas, steering presales, solutions, and go-to-market strategies. Arijit leads transformative initiatives for key accounts in the Pharma and Med Tech sectors, focusing on product design, verification & validation, and data-driven clinical research. His work also extends to Quality and R&D labs of the future, Clinical Data Management, Regulatory Compliance, and predictive quality operations.