AI can now search electronic health records and flag people who appear to meet a trial’s criteria in a fraction of the time manual screening takes. That solves one problem, finding people, and leaves a harder one untouched: whether those people understand the study well enough to say yes and stay in it. Used well, the same tools can help here too, presenting studies in language that makes sense to different communities, giving underrepresented groups a way to find research that is relevant to them, and using predictive analytics to widen who gets included in important research. A match is a starting point, not a result.
The evidence for the finding part is solid. The Journal of the National Cancer Institute published a compilation of 10 cancer trials from the US, Europe, and Australia that used AI tools for recruitment. Four of the studies reported on time savings from AI recruitment, and all four noted substantial savings. The studies broadly conclude that AI-based systems are efficient and reliable, with results generally concordant with gold standard manual screening. AI also supports retention: machine learning models can predict which participants are at risk of dropping out so coordinators can step in early.
Yes, generative AI pilots have shown promise, but for the technology to deliver business value in the life sciences industry, organizations need to rethink how they scale it. In July 2023, researchers at the McKinsey Global Institute estimated that gen AI could generate between $60 billion and $110 billion a year in economic value for the pharmaceutical and medical products industries, boosting productivity and innovation in areas across the industry’s value chain, from how new treatments are discovered to how they are marketed and administered by physicians. Six months later, McKinsey experts dug deeper into those numbers and uncovered more than 20 use cases with the potential for near-term impact.
Now, with gen AI use cases proliferating across the business community, McKinsey experts decided to find out how much progress life science organizations have made in capturing this value. In late summer 2024, they surveyed more than 100 pharma and medtech leaders responsible for driving their organizations’ gen AI efforts. All respondents report having experimented with gen AI, and 32 percent say they have taken steps to scale the technology, yet only 5 percent say they have realized gen AI as a competitive differentiator that generates consistent and significant financial value.
So what does a successful gen AI initiative look like? McKinsey describes one life sciences company that recognized the opportunity early and treated it as a company-wide change rather than a set of pilots. A C-level task force steered the overall strategy, governing bodies were set up across R&D, commercial, medical, and operations, and each area was asked to prioritize one use case with high-value potential. Proofs of concept were run with scaling in mind, reusable components were organized into area-specific platforms, and technology and business teams partnered from the outset so that every solution addressed a priority business need. The company also brought in learnings and assets from partners across the industry and built stage gates to focus resources on solutions ready to scale across therapeutic areas and geographies.
Leaders framed gen AI as a way to help employees handle growing workloads rather than replace them, gave early users close support, and used those early adopters to build momentum from the bottom up. Impact metrics were defined, tracked, and reviewed at regular governance meetings to keep initiatives on track to scale.
This experience does not have to be an outlier. Capturing the value of gen AI takes more than experimentation and one-off use cases. It has to be built into how the organization works, aligned with business strategy, and designed to scale and last. The same is true of every digital tool discussed below. Now let’s look at some digital tools besides AI that simplify and increase trial participation.
Digital Tools for Broadening Trial Participation
The way we have designed and run trials has excluded many people, often unnecessarily, and the cause is usually logistical. Digital advances now offer practical ways to improve accessibility and include a more diverse range of participants. The industry can use these technologies to remove barriers to new treatments and offer fairer patient trial access.
One of the most promising areas for improvement is digital trial patient tools that simplify participation. Telehealth platforms and mobile apps let people take part remotely and on their own terms, and reduce the strain of travel or rigid schedules.
Digital tools also give people a way to take part without feeling overwhelmed by complex trial processes. They provide transparency and let individuals make informed decisions about their involvement. For sponsors, that shows up as better adherence and retention and fewer costly dropouts. This is where understanding earlier pays off: a person who sees clearly what participation involves before joining is less likely to leave part way through.
How Digital Trial Patient Tools Simplify Participation
Traditional enrollment processes are often cumbersome and involve multiple visits to clinical sites and time-consuming paperwork. Digital platforms automate many administrative tasks and offer self-service portals for participants. eConsent platforms simplify informed consent and give participants the option to review and sign documents electronically.
Remote monitoring tools allow participants to share real-time health data without having to schedule or travel to an in-person visit. This significantly reduces logistical burdens and makes trials accessible to those in remote locations. A 2017-2019 study on the effect of telemonitoring on the rate of dropout after one year during home non-invasive ventilation found that digital monitoring reduced dropout rates by 21.4%. Telemonitoring has come a long way since then, as the study was done before the COVID-19 pandemic accelerated the worldwide adoption of telemedicine as a practical medium for healthcare.
Patient dashboards provide a centralized place where participants can track their trial journey. They often include reminders, medication schedules, and channels for direct communication with trial coordinators. These tools keep people informed, and that builds trust and long-term participation.
Telemedicine has further expanded accessibility. Participants can save time and reduce the need to travel by consulting with clinicians online. This is especially practical for individuals with mobility issues or those who live in areas with limited healthcare options. Studies show that the use of remote monitoring in the care of cancer patients has shown a significant reduction in the mortality rate compared to standard care.
Patient Trial Access in Underserved Communities
Equity is crucial for populations excluded from research due to socioeconomic or geographic barriers. Digital innovations address this disparity by providing patient trial access to those who were previously left out. Virtual trials allow individuals to participate without leaving home and make participation feasible for those living far from trial sites.
Multilingual digital interfaces and culturally adapted materials provide inclusivity for diverse populations. One story involved a trial using a bilingual app to reach Spanish-speaking participants, which resulted in a 40% increase in enrollment from this demographic.
The flexibility of digital solutions also benefits those who balance demanding schedules or caregiving responsibilities. Mobile-friendly platforms allow participants to record symptoms or complete surveys when they have time. Adaptable trials accommodate the realities of participants’ lives and provide greater inclusivity.
Digital Outreach Strategies to Boost Awareness
The most advanced digital tools are ineffective if people are not aware of them. Digital outreach is essential to raise awareness of studies. Social media campaigns, targeted email, and online forums are valuable ways to reach people. Awareness is only the first step, though. A person who has heard of a study still has to understand it before they can decide.
Collaboration with patient advocacy groups also builds trust and makes sure outreach efforts resonate with specific communities. A partnership between a diabetes-focused trial and an advocacy organization developed culturally relevant materials and resulted in higher engagement rates.
Another practical approach uses patient registries to share information about upcoming trials. Combined with chatbots, these registries provide instant responses to inquiries and simplify the path to enrollment.
Video content is a powerful medium for outreach. Educational videos that explain trial benefits, processes, and safety measures demystify participation for hesitant individuals. On platforms like YouTube, these videos reach a global audience and expand the pool of potential participants.
Improving trial accessibility takes innovation and collaboration. Digital trial patient tools can change how trials are run, widening who can take part while keeping data quality high. Finding people faster is only the first step. The tools that matter most are the ones that help each person understand what they are being asked to do.
If you are weighing where digital tools fit in your next study, see what trialport does for sponsors and CROs and how the pathway works.
About the author
Keith Berelowitz has spent more than twenty years watching clinical trials work on paper and struggle in real life. He has helped run studies, advises sponsors and CROs on how they engage with people, and chairs a UK research ethics committee, where consent forms and participant information sheets cross his desk every month. That vantage point led to one conclusion: most trial problems are not failures of science. They are failures of understanding at the moment a person decides.
He founded trialport, an AI native clinical trial navigation and decision-support platform, in the belief that technology earns its place in research only when it makes a study easier to understand and a decision easier to make. Understanding comes first. Decisions follow.