FirstNarration
The United States may be producing groundbreaking medical research, but its clinical trial system is struggling to connect potentially eligible patients with experimental treatments, according to Seema Verma, Executive Vice President and General Manager of Oracle Health and Life Sciences.
In a commentary published by U.S. News & World Report, Verma argues that slow patient recruitment has become a major obstacle in translating scientific discoveries into new medicines.
She believes artificial intelligence (AI), electronic health records and clinical research platforms could help bridge the gap between everyday healthcare and medical research.
Patients Often Struggle to Find Clinical Trials
According to Verma, one of the biggest problems is that patients who could potentially qualify for clinical trials may never learn that those studies exist.
She writes that fewer than 5% of Americans participate in clinical trials, despite millions more potentially qualifying.
Doctors may also lack the time or technology required to determine whether individual patients qualify for one of the thousands of active studies.
As a result, patients and their families can be left searching independently for experimental treatment opportunities.
Recruitment Delays Can Slow Medical Research
Verma identifies patient recruitment as one of the biggest causes of delays in clinical research.
She writes that upwards of 80% of clinical trials miss their enrollment timelines, while nearly one in five oncology trials does not reach completion because researchers cannot recruit enough participants.
These delays can have consequences for both patients and the cost of developing medicines.
Every additional month can add expenses associated with investigators, research facilities, monitoring and administration.
Verma argues that making clinical trials faster and more efficient could therefore help reduce development costs while allowing promising treatments to move through research more quickly.
AI Could Help Match Patients With Research
Artificial intelligence is presented as one possible way to address the problem.
Verma argues that healthcare and clinical research have historically developed as separate systems, with patient care taking place through one infrastructure and medical research operating through another.
Connecting electronic health records with clinical research platforms could allow technology to help identify patients who may meet the eligibility requirements for particular studies.
Instead of requiring patients to search independently for clinical trials, relevant studies could potentially identify eligible participants through their existing healthcare providers.
The approach could also make clinical research more accessible outside major academic medical centres by involving community hospitals, physician practices and rural clinics.
Oracle Developing Connection Between Healthcare and Research
Verma says Oracle is working on technology intended to connect clinical care with medical research.
The company's approach combines electronic health record technology, clinical research data systems, cloud infrastructure and AI.
The goal, according to Verma, is to create a system in which healthcare information can help connect potentially eligible patients with appropriate research opportunities.
Because Verma leads Oracle Health and Life Sciences, these claims represent her description of Oracle's strategy and the potential role of its technology.
Real-World Healthcare Data Could Play Larger Role
Connecting clinical care with research could potentially offer benefits beyond patient recruitment.
Verma argues that such systems could generate stronger real-world evidence, broaden participation in medical research and improve post-market monitoring of medicines and medical devices.
It could also contribute to what she describes as a continuously learning healthcare system, where information from patient care helps inform researchers, physicians, sponsors and regulators.
Privacy and Human Oversight Remain Essential
Verma acknowledges that expanding AI's role in clinical research would require safeguards.
She says AI should be deployed with human oversight, strong privacy protections, transparent governance and rigorous scientific standards.
The article therefore does not argue for removing physicians or researchers from clinical trial decisions.
Instead, AI is presented as a tool that could help healthcare professionals identify potential matches and manage information more efficiently.
Verma Calls for Broader Regulatory Support
Verma also points to the US Food and Drug Administration's real-time clinical trials initiative as an initial step toward modernising clinical research.
She argues that regulators should encourage responsible AI use for identifying eligible patients, expand the use of regulatory-grade real-world data and support more adaptive approaches to clinical trials.
She also calls for reducing unnecessary or duplicative documentation and making it easier for qualified healthcare providers to participate in research.
For now, the central argument is that medical innovation alone is not enough. Verma believes the systems used to recruit patients, conduct trials and connect healthcare with research must evolve alongside the science.
If those systems become faster and more connected, she argues, promising treatments could potentially reach patients sooner while reducing some of the costs and delays associated with developing new medicines.
