For decades, clinical trials in the U.S. have struggled with diversity. Despite ongoing calls for change, many groups—such as women, racial and ethnic minorities, rural patients, and older adults—remain vastly underrepresented in research that determines the effectiveness of new drugs and treatments. This lack of representation creates a dangerous gap in medical knowledge, as treatments that work for one group may not work as well—or at all—for others.
Historically, clinical trials have been conducted in urban academic medical centers, largely recruiting from easily accessible populations. But this means that the needs of rural patients, for example, are often overlooked in life-saving research. Likewise, testing a cardiovascular drug on a group of middle-aged white men doesn’t provide insights into how that drug would perform in a 70-year-old Latina woman with diabetes.
But this problem isn’t just about selective recruitment. As Rachel Richesson, a clinical professor at the University of Michigan, explains, many underrepresented groups are less likely to participate in clinical trials due to factors such as mistrust in the system, limited resources, and logistical barriers like transportation. This makes it difficult for drug developers to create treatments that reflect the real-world diversity of patients.
Fortunately, advances in technology, especially artificial intelligence (AI), are beginning to solve some of these long-standing challenges. With AI tools and integrated data platforms, pharmaceutical companies and contract research organizations (CROs) are finding innovative ways to design and conduct more inclusive trials that reach a broader range of patients.
Historical Barriers to Inclusion in Clinical Trials #
Traditionally, clinical trials have relied on easily accessible populations, often excluding groups that would benefit most from new treatments. For example, women have historically been underrepresented, partly due to concerns about the impact of menstrual cycles on trial outcomes. Moreover, the legacy of unethical medical research, such as the infamous Tuskegee Syphilis Study, has made some minority groups, particularly African Americans, hesitant to trust clinical studies.
Economic pressures have also played a role. Developing a new drug can take 10 to 15 years and cost upwards of $2.5 billion. With such high stakes, pharmaceutical companies have prioritized efficiency, often choosing established, high-performing medical centers for trials, rather than reaching out to new or diverse communities. This economic incentive to streamline recruitment perpetuates the cycle of exclusion, with trials frequently failing to reflect the diversity of real-world patients.
Ali Ahmed, Salesforce’s Global Head of Industry Innovation for Pharmaceuticals, points out that one of the biggest challenges is raising awareness about the importance of clinical trials among historically underrepresented groups. To solve this, he suggests that clinical trials should adopt the digital marketing techniques that have made consumer products so successful at reaching a wide audience.
Tackling Disconnected Data #
Another significant barrier to diversity in clinical trials is fragmented data. Today’s clinical trial systems are often siloed—patient records, recruitment databases, and marketing platforms don’t communicate with one another. This data fragmentation creates delays and prevents trial operators from getting the full picture of patient demographics and needs.
Magon Mair, Director of Solution Engineering at Wilco Source, explains that these disconnected systems can result in delayed responses to potential participants—sometimes taking up to three days to get back to someone who’s expressed interest in joining a trial. Given that patients often want quick responses, especially when they’re dealing with serious health issues, such delays can result in missed opportunities.
By integrating data from various sources and unifying systems, AI can speed up recruitment processes and ensure that trial operators have real-time access to the data they need to make informed decisions. For instance, AI can instantly match patients with the trials they’re eligible for and identify sites that best meet the trial’s needs.
The AI Advantage #
Unified data allows AI to unlock new possibilities in trial design. By pulling information from a variety of platforms, AI can analyze and interpret data faster and more accurately, helping match patients with the right trials and alerting researchers to potential issues before they arise.
For example, AI-powered tools can significantly reduce the time it takes to respond to patient inquiries, from several days to just 15 minutes. This responsiveness is crucial for patients who are eager to participate in trials, especially those dealing with serious medical conditions.
Moreover, AI can help ensure more equitable trial recruitment by identifying when certain groups are underrepresented. If a trial has too many participants in one age group, for instance, AI can suggest adjustments to balance the demographics, ensuring the trial more accurately reflects the broader patient population.
Meeting Patients Where They Are #
One of the most exciting developments in clinical trial recruitment is the integration of technology with a deeper understanding of patient needs. The COVID-19 pandemic highlighted the potential of decentralized trials—where patients could participate remotely via telehealth, home visits, or local healthcare partnerships.
Technologies like Salesforce’s Life Sciences Cloud are making this decentralized approach a reality. These platforms are capable of synthesizing data from wearables, local clinics, and even community health centers. AI can identify obstacles patients might face, such as transportation issues or language barriers, and proactively address them.
For instance, if a patient in a low-income neighborhood has difficulty getting to appointments due to unreliable public transportation, the system can arrange for a ride-sharing service like Uber or Lyft to pick them up. This kind of personalized support is key to reducing dropout rates and ensuring more patients can successfully complete their trial participation.
Looking Toward a More Inclusive Future #
Early results from the use of AI and data-driven platforms suggest that the industry is heading in the right direction. Mair points out that using automated reminders and data-driven recruitment tools has already led to a 25-50% improvement in patient appointment attendance. Furthermore, CROs have been able to double the number of study sites they manage without increasing staff.
As pharmaceutical companies and CROs continue to embrace these technologies, the future of clinical trials looks increasingly inclusive. By moving beyond the limitations of traditional recruitment methods, AI-powered platforms allow for trials that are not only more efficient but also more representative of the diverse populations they aim to serve.
Rather than treating diversity as a regulatory checkbox, AI is enabling the design of clinical trials that reflect the needs of all patients from the start. This approach is crucial for developing treatments that are effective for everyone, not just those who are easiest to recruit.
As Sharmin Nasrullah, General Manager of Life Sciences Clinical Development at Salesforce, puts it, “Patients are inherently diverse across many dimensions—demographics, wealth, education, and more. To crack the code on recruitment, we need to offer multiple methods and meet patients where they are.”