Clinical trials are notoriously expensive, time consuming, and risky. Failure comes at a very high cost (millions of dollars and months of development time wasted), yet studies show that the rate of success is dismally low (10-15%).

The reasons for failure are plentiful and diverse, ranging from safety issues to lack of funding to regulatory non-compliance. Yet the most challenging aspects of clinical trial design revolve around the patient, particularly surrounding recruitment, engagement, and retention. To make matters worse, research questions can be poorly formulated, failing to address critical patient pain points. This means that, even when drugs do make it through trials, they can fail to deliver the desired outcomes for patients.

Thankfully, recent technological advancements, particularly within artificial intelligence, are disrupting the traditional research model, improving the patient experience, and boosting success rates.

Leveraging Data to Put Patients at the Heart of Clinical Trial Development

Big social data provides a unique opportunity to put patients at the heart of the clinical trial development process. Every day, millions of people jump on social media to talk about their lives and experiences. Over 6 billion worldwide are using social media and 73% use it to discuss or search for health information.

Patients are known to be far more vocal and share more details about their struggles when they can do so freely or anonymously, as opposed to face-to-face, in an interview-style format. And it’s not just patients – carers, loved ones, clinicians, and nurses all share their observations of the diagnosis process, treatments, and the day-to-day lived experience of patients with debilitating conditions. There are hundreds of millions of online conversations around health struggles, and this data can be incredibly valuable to clinical researchers. According to Miranda Mapleton, CEO of social analytics charity White Swan, “social data is incredibly versatile and can be used to bring patient-centric insight into every aspect of the clinical trial development process.”

Pharmaceutical companies can leverage AI to parse these millions of online patient conversations, helping to eliminate trial design bias, identify blockers and risks to trial participation, and better understand the lived experience of a condition to ensure that treatments improve patient outcomes and quality of life.

Improving research design

It’s difficult to understand the day-to-day reality of living with a condition if you don’t struggle with it yourself, and so getting deep insight into the lived experiences of patients is crucial to demonstrate required efficacy.

Current patient-involvement practices often rely on small sample sizes. According to the Professor of Clinical Trials at Bristol Medical School, Chris Rogers, “patient groups that researchers consult will typically consist of only half a dozen people.” By tapping into millions of unprompted patient conversations, pharmaceutical researchers can better understand the “why” in depth and at scale. It is also a way of accessing hard-to-reach, globally dispersed voices that would often be overlooked or sidelined during the design process, which is fantastic for promoting patient diversity. This can be particularly invaluable for clinical trials involving rare disease patients.

Bayer is currently leveraging big social data to enhance their trial development process. Kerry Kiel, an Early Asset Strategist at Bayer, says “this data has helped [them] gain a greater understanding of the patient’s experience with a condition (from early symptoms through to treatment), which has really informed [them] in the way [they] approach clinical trial design ensuring that [they] keep the patient at the centre of what [they] are doing”.

Identifying barriers and facilitators to trial participation

One of the most common challenges with trial participation is patient engagement and retention. Many of these challenges occur due to barriers (physical and phycological) created by the condition itself, which the researchers are not fully understanding of during the trial design phase. It is also important to minimize trial complexity, assessing the “ask” vs “burden” balance to maximize patient participation.

Once again, big social data can come in to save the day, informing both the patient recruitment process and ensuring that the trial is designed to prioritize patient engagement. By better understanding the lived experiences of the average patient, researchers can design around people’s lives and minimize disruptions and barriers that would otherwise lead patients to drop out.

The Future of Clinical Trial Design

Getting clinical trials right is incredibly important, both in terms of reducing resource waste and improving the lives of patients. There has been a lot of talk, especially since the launch of ChatGPT in late 2022, about the power of big data and artificial intelligence.

I believe that, in a few years, leveraging big social data to put patients at the heart of the clinical trial design process will become a best practice. The earlier pharmaceutical companies embrace these technological advancements, the better.