Opinion: How to Make Today’s Clinical Research More Efficient

Clinical research is the lifeblood of the biopharmaceutical industry. In January 2024, there were more than 480,000 registered studies on ClinicalTrials.gov, a significant increase from three years ago, when there were 365,000. This uptick is driving demand for researchers and clinical trial participants—and the staggering amount of money and other resources needed to conduct these trials.

With medicine becoming more specialized and clinical trial protocols growing increasingly complex, we need to think about how we can drive efficiencies to overcome these challenges and keep up the pace of this research. As someone who has been involved in conducting hundreds of clinical trials over the course of my 34-year career in the biopharma industry, I’ve seen how complexity and inefficiency can bloom in studies and believe it is imperative that companies reverse that tendency. I have identified three solutions companies and institutions can embrace to improve efficiency in early and later-stage research.

Simplifying clinical trial protocols would be a good start in increasing the efficiency of research at every stage. Protocol designs and their requirements are getting overly complicated. And companies are requiring an increasing number and frequency of assessments and lab tests, which is making data collection and associated logistics harder to manage for patients and sites.

We’re also seeing companies combining multiple studies into one, compressing phases and repeatedly amending protocols to accelerate development timelines. Given the cost and time needed for research, there are advantages to doing it this way, principally that fewer patients are needed to generate useful data. But amendments make conducting the study more difficult for site teams, whose primary job is patient care, and that burden is significant and growing. If the starting point is an already-dense protocol, every amendment to the protocol increases the administrative, logistical and training burden for trial sites.

By its very nature, precision medicine requires complex trials and endpoints, and continuous data collection. At Precision for Medicine, we work closely with clients to execute these trials, and the added value we bring is in advising them about where it makes sense—or doesn’t—to reduce complexity or streamline trials. For example, we consider the burden placed on patients and their caregivers to determine how demanding participation may be. At the same time, we take into account the number of patient visits and biosamples required for analysis to understand the requirements placed on study staff—small things that also add to the burden on patients and caregivers.

Machine learning (ML) and artificial intelligence (AI) more broadly have the potential to transform how clinical development occurs by delivering significant time and cost efficiencies while providing better and faster insights to inform decision-making. For example, AI is increasingly being used in clinical study design, from providing direction on study endpoints to identifying trial sites with the highest potential to deliver the most participants. This technology promises to reduce the time needed for protocol development and accelerate the recruitment process, and even lead to more predictable results.

Considering that of all discontinued trials, around 55% were discontinued due to recruitment failure, AI could be the solution we’ve all been waiting for. Admittedly, use of AI in clinical trials is still in its infancy. But the number of use cases will increase over time once we overcome some of the regulatory and data privacy/security concerns.

Aside from the use of AI, automation has the potential to vastly improve clinical trial efficiency. The push to automate as many processes as possible—including Investigational New Drug (IND) development, study feasibility, site activation and data entry—is a logical step in the evolution of clinical research. Each institution uses a variety of technologies and, as they incorporate additional technologies, adoption will become more challenging. For example, when each study site uses a different platform to mine electronic health record (EHR) data to identify patients that meet the inclusion/exclusion criteria—one of many examples of technologies that have different user training, interfaces and set-ups depending on which system is used—an added layer of complication arises for both manufacturers and study sites. Standardization of these technologies across clinical trial sites could solve for the challenges hindering operational efficiency.

With any new technology, getting the training and the support model right to make it easy for sites is key to improving adoption. This takes time, but the initial investment will help streamline the process downstream.

Insurance claims data and EHRs, two forms of real-world data (RWD), are emerging as useful tools to optimize and accelerate clinical trials. While there remains a lot to be learned about how to best analyze RWD, there is reason to believe it can be helpful, particularly in the areas of site feasibility and protocol design.

Claims data can aid in site selection by providing companies with information about investigators’ experience with similar trials, site capabilities, patient populations and other criteria. Clinical research coordinators and nurses at activated trial sites can use their sites’ EHR data to find patients most likely to meet a study’s requirements. EHRconnect, a proprietary technology offered to Precision clients as part of our EHR consulting practice, provides trial site personnel with detailed search instructions that enable them to more effectively mine their data and better identify potentially eligible patients, a programmatic step forward that can decrease enrollment time and ease the burden on overworked site staff.

By providing a realistic picture of the experiences, treatment histories and challenges of a particular patient population, RWD can inform the development of effective trial inclusion and exclusion criteria. Ideally, this will result in the avoidance of unnecessarily restrictive criteria and enable trials to meet the needs of target patients more quickly and effectively.

The nature of doing clinical trials and research is growing ever more intricate and complicated. But I believe that by establishing pragmatic protocols with life science innovators and building in operational excellence to handle multifaceted trials, we can compress timeframes and generate efficiencies in research at every stage. As medicine becomes more specialized, this puts all of us—at the organizational level all the way down to individual sites—in a much better position to get medicines to patients faster.

Sofia Baig is president of clinical solutions at Precision for Medicine, where she is focused on global clinical solutions and delivering transformational change inclusive of people, processes and technology. She has over 30 years of experience in the research services industry.