Clinical trials are notoriously expensive, time-consuming, and high-risk. Studies show that failure is very costly (millions of dollars and months of wasted development time), but the success rate is disastrously low (10-15%) .
Reasons for failure range from safety issues to lack of funding to regulatory violations. However, the most challenging aspects of clinical trial design revolve around patients, especially recruitment, engagement, and retention. Worse, research questions may be poorly formulated and fail to address critical patient pain points. This means that even if a drug passes testing, it may not provide the desired results for patients.
Thankfully, recent technological advances, particularly in artificial intelligence, are disrupting traditional research models, improving the patient experience, and increasing success rates.
Leveraging data to put patients at the center of clinical trial development
Big social data offers a unique opportunity to put patients at the center of the clinical trial development process. Every day, millions of people take to social media to talk about their lives and experiences. Worldwide, more than 6 billion people use social media, and 73% use social media to discuss or search for health information.
Patients are known to be much more vocal and share more details about their struggles when they can speak freely or anonymously in an interview format than in a face-to-face setting. And it's not just the patients. Caregivers, loved ones, clinicians, and nurses all share their observations about the diagnostic process, treatment, and everyday living experiences of patients with debilitating diseases. There are hundreds of millions of conversations about health issues online, and this data can be invaluable to clinical researchers. Miranda Mapleton, CEO of social analytics charity White Swan, said: 'Social data is incredibly versatile and can be used to bring patient-centric insights into all aspects of the clinical trial development process. ”.
By leveraging AI to analyze these millions of online patient conversations, pharmaceutical companies can eliminate bias in trial design, identify barriers to trial participation and risks, and better understand real-world symptom experiences. and treatment can reliably improve patient outcomes and quality of life.
Improving research design
It is difficult to understand the day-to-day realities of living with a disease unless you have battled it yourself, so it is important to gain deep insight into patients' lived experiences to demonstrate the necessary effectiveness.
Current patient engagement practices often rely on small sample sizes. Chris Rogers, professor of clinical trials at Bristol Medical School, said: “Researchers typically consult only six patient groups.” By leveraging millions of spontaneous patient conversations, pharmaceutical researchers will be able to understand the “why” deeper and at scale. It's also a way to access hard-to-reach, globally distributed voices that are often overlooked or ignored during the design process, which is great for promoting patient diversity. This is especially valuable in clinical trials involving patients with rare diseases.
Bayer is now leveraging big social data to enhance its clinical trial development process. Kelly Keel, Early Capital Strategist at Bayer & Co., said: [them] Gain a deeper understanding of a patient's experience with a condition, from initial symptoms to treatment [them] in a way [they] We approach clinical trial design to ensure: [they] put the patient at the center of things [they] Are doing”.
Identifying barriers and facilitators to clinical trial participation
One of the most common challenges in clinical trial participation is patient engagement and retention. Many of these challenges arise from barriers (physical and physiological) created by the symptoms themselves, which researchers do not fully understand at the trial design stage. In order to maximize patient participation, it is also important to assess the balance between 'asking' and 'burden' and minimizing trial complexity.
Again, big social data can come in to save the day, informing patient recruitment processes and ensuring trials are designed to prioritize patient participation. Masu. A deeper understanding of the average patient's lived experience allows researchers to design around people's lives and minimize disruptions and barriers that lead to patient dropout.
The future of clinical trial design
Properly conducting clinical trials is critical from the perspective of reducing wasted resources and improving patients' lives. There has been a lot of talk about the power of big data and artificial intelligence, especially since he announced his ChatGPT in late 2022.
I believe that within a few years, leveraging big social data to put patients at the center of the clinical trial design process will become a best practice. The earlier pharmaceutical companies adopt these technological advances, the better.
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