Reducing site burden and expanding research capacity with AI

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Clinical research sites face challenges in managing the growing complexity of trials. The increasing administrative burden, protocol complexities, constant staff turnover, and operational inefficiencies are stretching site personnel beyond their capacity to manage this strain. This threatens to overwhelm even the most established research programs.

We find ourselves at a crossroads where artificial intelligence (AI) offers an avenue to improve operations as well as quality and research capacity.¹ Just like electronic data capture (EDC) systems started to replace paper-based methods 2 decades ago and became indispensable, we now stand on the threshold of AI becoming equally essential for clinical trials. The integration of AI “teammates” into trial operations may soon become a necessity for clinical research sites to continue to operate.

Augmentation with AI technology holds immense promise to streamline operations, reduce burden, and elevate the quality of research. Drawing from our experiences at Georgia Retina, one of the Southeast’s largest retina practices, and through the lens of technology innovation in clinical trials more broadly, we are seeing firsthand how AI is transforming trials into more efficient, scalable, and patient-centered endeavors.

Modern challenges facing clinical trial sites

Clinical research sites are the backbone of drug and device development, yet they are plagued by foundational issues. These challenges manifest in 3 critical areas: staff retention and training, administrative overload, and financial management.

1. Staff turnover and training challenges

A predominant challenge facing research sites is the high turnover of staff and the time-consuming process of training replacements. There is a persistent shortage of qualified personnel who can bridge the gap between clinical expertise and research requirements.² At Georgia Retina, we’ve observed firsthand the difficulty of finding individuals who possess both ophthalmology knowledge and clinical trial experience. The specialized nature of ophthalmology research demands expertise not only in clinical procedures but also in research-specific protocols, regulatory requirements, and standardized testing methods.

Instead, many sites recruit technicians experienced in clinical practice and train them in the nuances of research. However, even this presents barriers. New staff must learn complex regulatory requirements, master data entry protocols, and understand the nuances of clinical trial documentation, all while maintaining the high standards required in ophthalmic studies for tasks like visual acuity testing and image capture. Employees must also possess the communication skills necessary to interact with industry sponsors, contract research organizations (CROs), and regulatory bodies. The learning curve is steep, and the training period is extensive. Each new hire represents weeks to months of onboarding, during which sponsoring organizations expect error-free trial execution.

Collaborative AI teammates can significantly reduce the training burden by providing structured learning paths and real-time guidance for new staff. These systems support site staff by helping to standardize processes across studies while ensuring compliance with protocol-specific requirements. For technicians transitioning from clinical practice to research, AI augmentation offers contextual support, suggests appropriate terminology, and guides them through regulatory documentation.

The impact extends beyond initial training. AI continues to support staff throughout their tenure, providing automated checks on data entry, flagging potential protocol deviations, and ensuring consistency in documentation. This helps maintain quality while reducing the pressure on senior staff to oversee new team members.

2. Administrative overload on coordinators

Administrative requirements also pull site staff away from patient-focused activities. Coordinators frequently spend entire days grappling with regulatory forms, sponsor correspondence, recruitment logs, and data entry, leaving little bandwidth to focus on patient engagement or strategic study planning.^3,4 The burden of managing multiple studies, each with its own set of requirements and systems, creates a workload that often leads to staff burnout and reduced operational efficiency.

Communication with sponsors, CROs, and regulatory bodies adds another layer of complexity. Coordinators must navigate this while ensuring compliance with various protocol requirements, often leading to redundant work and increased stress levels.

This inefficiency is particularly troubling because so much of the administrative workload is repetitive by nature, including tracking compliance, cross-checking study data, resolving queries, and managing finance-related processes. Without intervention, these tasks continue to erode the productivity of clinical research teams.⁵ 

3. Site financial inefficiencies

Perhaps one of the least discussed challenges in clinical research is financial management. Many research sites underbill for their services, not due to negligence, but rather to the complexity of trial budgets and invoicing processes. Each study operates under different payment terms, making it difficult to track and bill for all performed activities.

This is compounded by the way payments are processed. Sites often receive lump-sum payments without detailed breakdowns, requiring significant time and effort by the staff to reconcile against specific study activities. This financial opacity leads to underbilling and hampers sites’ ability to invest in growth and quality improvement.

How AI teammates alleviate clinical trial burden

The integration of AI teammates into clinical trial operations offers a comprehensive solution to these persistent challenges. By automating routine tasks, standardizing processes, and providing real-time guidance, AI augments study team members and transforms how sites manage their research programs through the following:

1. Efficient training and onboarding

Clinical trial efficiency begins with well-trained staff. AI helps shorten the learning curve for new hires while ensuring consistency in training across all research procedures. When a new coordinator or technician joins the research team, the AI teammate serves as a dedicated training assistant, providing real-time guidance through essential processes like visit planning, informed-consent preparation, procedure scheduling, and compliance workflows while flagging potential errors and offering corrections before they become habits.

This dynamic support extends seamlessly from training into daily research activities, making it particularly valuable for clinical staff transitioning into research roles. As staff conduct actual study visits, the AI teammate continues to provide structured guidance through protocol requirements, helps coordinate required procedures, and ensures all visit documentation is complete and compliant. This ongoing assistance helps research staff confidently manage multiple protocols and procedures while maintaining high standards of accuracy and compliance, effectively bridging the gap between initial training and successful long-term performance in their research roles.

2. Automated administrative workflows

AI also acts as a productivity multiplier for coordinators. AI teammates streamline tasks such as data entry, regulatory documentation, and compliance tracking.

For instance, AI can extract patient data records, cross-check them against protocol requirements, and prefill EDC forms. It can also track compliance deadlines, flag incomplete fields automatically, and organize vast volumes of regulatory forms, making it simpler for staff to review and approve submissions. All the while, AI maintains an audit trail. This type of intelligent automation saves time while also reducing the risk of transcription errors and protocol deviations. This furthermore reduces the volume of back-and-forth correspondence with sponsors, freeing coordinators to focus on patient enrollment and oversight or any other activities that require human judgment and interaction.

3. Optimized financial processes

AI also addresses financial inefficiency. By automatically tracking billable activities and generating timely invoices, these systems ensure sites capture all revenue opportunities. The technology can match complex payment structures to perform activities, reconcile payments against invoices, and flag potential billing discrepancies.

By integrating AI teammates into financial workflows, site personnel are freed to focus on patient care, even as sites can retrieve revenue that might otherwise go unnoticed. This enables these personnelto invest meaningfully in program expansion, quality improvement, and staff incentives.

4. Enhanced data quality

Data quality remains paramount in clinical trials. AI proactively monitors for errors by flagging inconsistencies at the point of data entry, ensuring compliance with study requirements before data reaches CROs or sponsors. Additionally, AI enables standardization in documentation, removing ambiguity and ensuring data sets remain clean, complete, and ready for statistical analysis. This proactive approach to quality control at the site level reduces the likelihood of queries and minimizes audit risks while accelerating the timeline for data reviews.

5. More efficient recruitment

AI can assist with patient recruitment by analyzing electronic health records to match patients with trial criteria, reducing timelines without compromising on protocol specificity. Once a patient qualifies, AI systems automate consent-form preparation, logistics coordination, and workflow scheduling, ensuring a seamless onboarding process. This efficiency translates to faster enrollment and timely study starts, benefiting sponsors and patients alike.

Expanding trial capacity

As AI streamlines operations, it creates opportunities for expanding clinical trial capacity while maintaining operational stability. For growing practices, AI enables the efficient expansion of research operations across multiple locations. Georgia Retina, for instance, is now in the early stages of expanding its footprint, with plans to open additional clinical trial sites in underserved areas. One of the main enablers of this vision is AI’s ability to lower the administrative and resource barriers associated with new site launches.

By applying AI augmentation to streamline operational setup, recruitment, and compliance, even smaller or less-experienced organizations can become competitive players in the clinical research space. AI also replicates scalable workflows, allowing multisite studies to launch and maintain uniform quality across locations.

As a result of AI’s ability to lower the barrier to entry for new sites, sites and investigators have the possibility of expanding access to more patients who otherwise might have struggled to get care.

The future of clinical trials is AI enabled

Given the proliferation of advanced science, the clinical trial ecosystem must evolve to keep pace with accelerating medical discovery. The future of clinical trials depends on our ability to scale research operations while maintaining quality and efficiency. AI represents the critical infrastructure that makes this evolution possible. Sites that fail to adopt AI risk falling behind in recruitment efficiency, trial quality, and scalability in an increasingly complex research environment.

For researchers wondering how to get started, the answer is simple: Start with a careful assessment of current operational pain points. Do bottlenecks exist in recruitment, staff training, data management, or invoicing? AI solutions are available today to address each of these. Starting with a small pilot program often proves invaluable before scaling operations further. The key is selecting partners who understand both the technology and the unique demands of clinical research.

Sites that embrace this change will lead the next wave of clinical research, enabling faster development of new treatments while setting new standards of quality and compliance.

David Chin Yee, MD

Yee is a vitreoretinal surgeon and partner at Georgia Retina. He reports no relevant disclosures.

Ram Yalamanchili

Yalamanchili is the founder and CEO of Tilda Research, a developer of AI teammates for clinical research.

References

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2. Peralta G, Sánchez-Santiago B. Navigating the challenges of clinical trial professionals in the healthcare sector. Front Med (Lausanne). 2024;11:1400585. doi:10.3389/fmed.2024.1400585

3. Getz K. Characterizing the real cost of site regulatory compliance. Appl Clin Trials. 2015;24(6).

4. Mora V, Colantuono S, Fanali C, et al; SC&RN group. Clinical research coordinators: key components of an efficient clinical trial unit. Contemp Clin Trials Commun. 2023;32:101057. doi:10.1016/j.conctc.2023.101057

5. Cagnazzo C, Filippi R, Zucchetti G, et al. Clinical research and burnout syndrome in Italy – only a physicians’ affair? Trials. 2021;22(1):205. doi:10.1186/s13063-021-05158-z