How Automation Is Transforming Clinical Research Operations
Automation, AI, and digital health technologies can help make clinical trials more efficient, inclusive, and impactful
Colin Weller, BSc/BA, has worked in the drug development industry for over 20 years having spent several years with Big Pharma and biotech companies, such as Pfizer, Amgen, and AstraZeneca. Weller is driven by developing and deploying technology solutions into clinical research that enable bringing therapies to patients faster.
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Colin Weller, BSc/BA, has worked in the drug development industry for over 20 years having spent several years with Big Pharma and biotech companies, such as Pfizer, Amgen, and AstraZeneca. Weller is driven by developing and deploying technology solutions into clinical research that enable bringing therapies to patients faster.
In 2023, the FDA approved 55 novel therapies, a 48.6 percent increase from the 37 approved in 2022. While this increase is welcome, it only brings the industry back to pre-pandemic levels of therapy approval. The 10-year rolling average of new therapies approved remains stubbornly stable at less than 50 per year, despite increasing spending in research and development. But there is better news.
For the last five years, clinical research has laid the foundations for a future where the number of new therapies approved each year would increase tenfold, due to a new era of technology and AI-enabled clinical trials. Historically, clinical development has been constricted by manual processes and human-operated services that slow the trial pace and limit participants. Today, the rapid emergence of digital technology has broken down these barriers, helping drive clinical development rather than merely assisting.
The industry is now entering a phase of clinical trials marked by extensive technology integration, moving away from traditional dependence on human services. Thanks to the convergence of several technologies, like artificial intelligence (AI), digital health technologies (DHTs), and automation.
Automation’s real-life use cases
Automation’s abilities are a perfect match for many clinical research needs, including behind-the-scenes tasks like parsing through large amounts of data or configuring systems and software for a successful study startup. Automation can be paired with AI algorithms to streamline manual processes like system design and configuration. This pairing also excels at monitoring trial progress, predicting outcomes, ensuring protocol adherence in real-time, and conducting clinical trials efficiently.
Top pharmaceutical companies use automation to reduce testing, validation, and manual input associated with technology deployment. This shortens the timelines by taking existing laborious manual processes and complex technology roadblocks, such as electronic clinical outcome assessment (eCOA)—an industrywide delaying factor in the study startup stage—off the critical path to going live and enrolling patients.
Sweeping clinical research potential
Automation can effectively streamline and shorten a range of manually executed clinical research operations. For example, automation can be applied to site and study operational processes, including data entry, scheduling, and administrative paperwork. In recruitment, automation can parse volumes of data to find better, more representative participants much faster than traditional methods.
Additionally, real-time data collection and AI algorithms eliminate delays associated with manual input and analyses and can better help researchers identify trends and patterns quickly. The greatest potential of automation lies in combining it with AI and DHTs.
The Big 3: Automation, DHTs, and AI
The last five years saw a remarkable 97 percent increase in the use of DHTs in clinical trials. This is good news as case studies from across the industry demonstrate the transformative impact of digital measures.
For instance, a top pharmaceutical company achieved an impressive 80 percent reduction in the number of required participants for detecting disease progression in Huntington’s disease, due to the sensitivity of digital biomarkers. Similarly, collaboration with a major pharmaceutical company showed enhanced safety monitoring and digital biomarker development using electronic patient-reported outcomes (ePROs) with sensor-collected performance outcome (PerfO) data.
Digital biomarkers enable testing smaller sample sizes (in some cases, almost 70 percent less than the standard requirement) by employing more precise digital measurements. Automation can, therefore, accelerate the pace of clinical research, revolutionizing the way trials are conducted and yielding faster, more precise, and clinically significant results.
A better future starts today
The rate of technological advancements today is sounding a clarion call to the pharmaceutical industry to reevaluate and redesign the clinical trial process. Walking the path paved with automation, AI, and DHTs will help realize the goal of releasing more than 500 treatments each year. By leaning into technology, the industry can achieve a more dynamic, efficient, and inclusive approach to drug development and patient care.
