Reimagining the Clinical Research Coordinator in the Age of AI: A Commentary

Clinical Researcher—June 2026 (Volume 40, Issue 3)

PEER REVIEWED

Tracy Arakaki, PhD, PMP, PBA; Justin Scott Brathwaite, MBA, PMP; Kwame Le Blanc, BSc, MD; Chinedu Agumadu, BS

 

 

The Increasing Complexity of Clinical Trials Research

Clinical trials have become increasingly complex and costly in recent years as the requirements for study monitoring, regulatory reporting, and greater protocol intricacy have surged.{1} What underlies these burgeoning requirements are increases in protocol complexity and the number of study endpoints. For instance, a 2021 report by the Tufts Center for The Study of Drug Development illustrated that the average Phase II and III clinical trial protocol required 263 procedures per patient and approximately 20 endpoints on average, which was a 44% increase in procedures since 2009.{2} Greater protocol complexity necessitates larger patient cohorts and significantly more research sites, which has ultimately increased study timelines by almost two years.{2}

These growing demands translate to significant increases in workload for frontline clinical research staff such as clinical research coordinators (CRCs). For CRCs, this means higher cognitive load, increased per-patient workload, and a greater probability of errors that can compromise both patient safety and data integrity.

This commentary illustrates how the CRC role necessitates a more data-driven and strategic function, while simultaneously preserving core competencies in patient care and clinical judgment. Furthermore, it identifies existing gaps and potential areas for future exploration regarding the practical implementation of artificial intelligence (AI) tools within clinical research workflows.

The Clinical Research Coordinator: From Data Entry to Data Oversight

AI is revolutionizing the way clinical trials are conducted, which will redefine how roles within the clinical research workforce are conducted. One of AI’s key strengths is its ability to efficiently scan electronic medical records (EMRs) for eligibility criteria and flag potential trial participants with greater speed and accuracy.{3} However, before examining how AI can optimize the CRC role, it is essential first to outline the challenges and responsibilities CRCs currently face. Within this evolving landscape, CRCs play a vital role in executing clinical trials successfully by managing participant recruitment, ensuring protocol adherence, overseeing data entry, obtaining informed consent, reporting adverse events, and coordinating logistical operations.{4}

After initial participant screening, CRCs encounter a continuous cycle of data entry, data management, and documentation. Trial data must be meticulously captured, often involving redundant data entry into multiple systems.{5} Moreover, coordinators are often responsible for developing source documents, which are critical for quality control yet require substantial time and attention. The administrative workload also includes regulatory compliance tasks, such as organizing regulatory binders, preparing protocols, and manually adapting sponsor consent templates to fit site-specific requirements.{6} Because CRCs frequently manage numerous studies and sometimes work on weekends, staff turnover is high.

With these challenges in mind, AI has the potential to evolve the CRC role into a more data-driven and strategic function. CRCs can leverage their clinical expertise to interpret AI-generated data, guide strategic decisions, uphold trial quality, and participate in advanced project management. When thoughtfully deployed, AI offers a promising avenue to alleviate administrative burdens and restore balance to the CRC’s responsibilities. In this new paradigm, the CRCs evolve from tactical performers or logistics managers into patient-centric strategists and data-driven quality experts, while also serving as dedicated advocates for the human connection essential to successful trial execution.

By automating routine tasks, a CRC is free to devote more time to patient interactions, complex communications, and ethical considerations. CRCs and research staff currently spend hours on pre-screening and must often manually evaluate large pools of potential participants for trial eligibility. Thus, identifying the fundamental challenges faced by CRCs is crucial to understanding how AI can redefine CRC’s role, alleviate the most significant burdens, improve enrollment, and expedite drug approvals to help patients more quickly.

Preserving Human Clinical Judgment Within AI-Supported Workflows

The time saved by automation will likely create opportunities for new, high-value, and demanding roles that require human input. This shift was vividly demonstrated when IBM Watson Health partnered with the Mayo Clinic to automate the screening of patient records using natural language processing (NLP). By analyzing unstructured data in seconds, a task that previously took clinical coordinators weeks, the system achieved an 84% increase in enrollment for breast cancer trials. This efficiency freed CRCs to move further up the value-added chain, shifting their focus from mundane tasks to strategic trial management.{7}

Such increased specialization in data-driven strategy and quality oversight allows professionals to interpret AI-generated insights, apply them to real-world scenarios, and engage in complex problem-solving. This includes providing strategic oversight, real-time monitoring of trial data quality, and ensuring protocol compliance is maintained at a high level. The modern CRC leverages clinical domain expertise to validate AI findings, assess biases, and drive the high-level project management necessary for trial success.

While initiatives like Pfizer’s Blue-Sky Project demonstrate that AI can scale tasks as critical as patient identification and engagement, the CRC’s role remains essential. By utilizing Internet of Things (IoT) sensors and wearables to transition Parkinson’s research from episodic clinic visits to continuous, 24/7 monitoring, Pfizer established a more patient-centric “Clinical Trial Anywhere” model.{8}

However, even within this high-tech framework, human judgment and empathy are necessary to guide participants. In our conversations with CRCs, many expressed the need to retain human clinical judgment. For instance, the human CRC must “guarantee a true informed consent,” a process that demands nuanced, empathetic communication that technology cannot replicate. Furthermore, CRCs must maintain “critical thinking and problem-solving” skills to “check for accuracy” against potentially “fabricated output” or confabulations. Ultimately, the human-in-the-loop serves as the last safeguard against errors and the erosion of trust in the clinical trial process.

Beyond empathy, human critical thinking and problem-solving are required to check for accuracy against AI models that currently lack the ability to check themselves. Future success depends on the AI’s ability to identify accurate data and CRC’s interpretation of AI-generated insights without over-depending on them. This high-level oversight extends to monitoring algorithmic bias and ensuring strict adherence to regulatory frameworks such as Health Insurance Portability and Accountability Act and U.S. Food and Drug Administration guidelines. In the end, by offloading routine tasks to AI, coordinators can reallocate their expertise toward complex problem-solving, patient engagement, and strategic oversight.

Enhancing Clinical Trial Efficiency with AI

AI is being deployed to reduce costs, enhance recruitment strategies, and boost operational efficiency across the clinical trial lifecycle.{9} These advancements have become particularly crucial as the industry is challenged by rising burnout rates and a shortage of skilled professionals.

Despite the growing use of AI, there remains a significant gap in considering the viewpoints of CRCs, who are on the front lines of clinical trials. CRCs face significant challenges, high rates of burnout, and turnover. Recent findings show that 74% of CRCs are considering a job or career change.{10} Consequently, technological innovation is essential to sustain the pace and quality of clinical development.{11}

Innovative platforms analyze clinical trial data to assess how adjusting the criteria for participation could increase the number of eligible subjects without compromising patient safety or data integrity. By streamlining criteria, these tools help shorten recruitment periods and study cost. Similarly, other platforms automate the generation of appropriate trial participation criteria based upon the objectives of the trials, which can further expedite the recruitment phase.{12}

This workforce burden often translates into hours of manual prescreening and chart review, diverting time and energy away from meaningful patient interaction and toward exhaustive documentation.

AI’s primary function is to automate high-volume, repetitive, and time-consuming tasks like manual patient pre-screening, EMR chart review, and logistical scheduling (including reminders and calls). By automating these tasks, AI enables CRCs to focus on more human or patient-centric activities and trial or data management.

CRCs and research staff dedicate substantial time to pre-screening, often manually reviewing a large pool of potential participants and their data.{13,14} The inefficiency of this process is pronounced, especially when matching potential participants to complex protocols, as CRCs must meticulously search EMRs for specific clinical indicators, co-morbidities, and medication exclusions.{15,16} For example, verifying exclusionary medications can consume dozens of hours per participant for a single trial, creating significant friction in the recruitment pipeline and delaying time-to-enrollment. This slow pace in identifying potential participants means valuable research time is wasted and missed opportunities for potentially eligible patients who need new treatments.

In addition to EMR-based matching, AI-driven outreach tools within social media platforms can further expand the pool of potential participants by enabling study teams to reach broader populations more efficiently. Social media platforms leverage advanced demographic and geolocation targeting, while their ad-delivery algorithms automatically optimize campaigns based on user engagement and qualified leads. As these algorithms learn, they refine target profiles, lower cost per lead, and establish a real-time feedback loop that allows CRCs to adjust messaging, refine pre-screening filters, and reduce the volume of unqualified applications.{17}

The logistical and documentation burdens are also ripe for AI-driven transformation. AI can be leveraged to automate day-to-day logistics, which include automated reminders and schedule tracker builders that account for patient-specific details.{3}

Patient engagement is essential for retention for successful clinical trials, but CRCs often rely on antiquated and labor-intensive tools. Maintaining adherence to the protocol and contact with participants requires significant manual effort, including scheduling, sending reminder e-mails, and follow-up calls. This administrative work ties directly to the core clinical challenge of retaining patients throughout the trial.

AI could be leveraged to monitor engagement patterns, mitigate dropout risks, and deliver personalized communication. More advanced capabilities could include AI-driven tools that can detect early warning signs and provide actionable, patient-specific details to the research team. For instance, a patient’s preference for afternoon appointments, a specific nurse’s request, or a variable work schedule.

By ensuring the trial is as patient-centered as possible, AI could help enhance participant satisfaction and adherence. This personalization ensures that the “human touch” is applied where it matters most: in the quality of the interaction.

Accordingly, Table 1 summarizes common administrative burdens encountered by CRCs, along with potential AI-driven solutions to mitigate these challenges.

Table 1: The Evolution of the CRC Role—Administrative Burdens and AI Augmentation

Administrative Burden ———————————————————————-	AI-Based Proposed Solution (Augmentation) ———————————————————————-
Manual Patient Pre-Screening and EMR Review	Advanced machine-learning algorithms automatically analyze structured and unstructured data within EMRs to assess patient eligibility across complex inclusion and exclusion criteria. By continuously scanning large patient populations in near real time, these systems can rapidly flag potential trial participants, reduce manual screening burden, and improve recruitment efficiency and accuracy.{15,16} ———————————————————————-
Time-Consuming Exclusionary Checks	AI-based systems evaluate complex clinical trial protocols by systematically applying detailed inclusion and exclusion criteria, such as prior or concomitant medication use, comorbidities, and lab thresholds, to patient records. Compared to manual chart review, this automated approach increases speed, consistency, and accuracy, while significantly reducing screening time and human error.{18} ———————————————————————-
Inefficient Patient Identification/Outreach	AI-driven advertising algorithms on social media platforms such as Facebook and Instagram dynamically optimize audience targeting based on engagement patterns and demographic signals. This approach improves outreach efficiency by lowering cost per lead, increasing recruitment yield, and broadening access to potential trial participants beyond traditional referral pathways.{17} ———————————————————————-
Relentless Cycle of Data Management	AI-enabled systems extract and transfer clinical data from source documents into electronic data capture systems and other trial management systems. By minimizing redundant manual data entry across multiple platforms, this approach reduces transcription errors, improves data consistency, and decreases administrative burden on research staff.{19,20} ———————————————————————-
Tedious Documentation and Regulatory Overhead	AI-assisted document management tools streamline the organization and maintenance of regulatory binders by automatically classifying, indexing, and version-controlling study documents. In addition, AI can automate the manual conversion of informed consent forms from sponsor-provided templates to site-specific formats, reducing administrative effort, formatting errors, and startup timelines.{21} ———————————————————————-
Labor-Intensive Patient Engagement Logistics	AI-powered systems coordinate visit scheduling, generate patient-specific trackers, and automate reminders through e-mails, texts, or calls based on individualized study timelines. By reducing missed visits and manual follow-up, these tools improve protocol adherence, enhance participant engagement, and lower the administrative burden on site staff.{22} ———————————————————————-
Managing Dropout Risk/Patient Retention	Emerging AI tools could analyze participant engagement patterns, visit adherence, and communication preferences to detect early indicators of disengagement or dropout risk. By identifying subtle signals, such as preferred appointment times or responsiveness to specific communication channels, AI systems could enable personalized outreach strategies aimed at improving retention and sustaining participant involvement throughout the trial lifecycle. ———————————————————————-

 The Patient Retention Gap: AI Innovation vs. Coordinator Burden

Most AI applications and tools leveraged in clinical trials have focused on recruitment challenges, which the current literature supports. A recent systematic review analyzed 5,731 studies published between 2004 and 2023 that referenced AI to support clinical trial recruitment, including 51 of those studies in their analysis, and found that every identified AI application supported recruitment—while none were specifically designed for patient retention.{23} Although the literature demonstrates strong innovation in AI-enabled eligibility, matching, prescreening, and feasibility optimization, retention remains largely unaddressed.{23}

Poor patient retention can significantly increase the workload of a CRC.{24} When participants withdraw or become noncompliant or lost to follow-up (LTFU), CRCs must spend more time on follow-up calls, re-education, and problem-solving to keep them engaged. Moreover, missed visits require extra scheduling, documentation of protocol deviations, and coordination with investigators and sponsors. Lastly, data gaps trigger additional queries and monitoring activities, further exacerbating administrative burden.{24} Altogether, retention increases operational complexity, extends timelines, and amplifies pressure on CRCs to protect data integrity and maintain compliance while supporting participant needs.

The current landscape often necessitates that CRCs manage retention through manual processes, which can contribute to administrative burden and potential data integrity challenges. Taken together, these factors highlight a significant opportunity for industry and academia to collaborate. By co-developing AI systems designed to identify attrition risks and facilitate consistent engagement, these partnerships may provide research staff with the infrastructure needed to better support study cohorts. Thus, prioritizing AI-enabled retention strategies could help mitigate operational complexity, protect trial timelines, and support the successful completion of increasingly complex protocols once patient enrollment begins.

Preparing the Workforce: Identifying Research Opportunities in AI Literacy

Given that CRCs are essential to the operationalization of AI in clinical research, incorporating their specialized perspectives and training needs is critical for the development of effective tools and successful professional adoption. One approach to achieving this is using semi-structured focus groups with CRCs, which would allow for in-depth exploration of current knowledge, practical experience, and perceived challenges related to AI integration. Focus groups conducted with employed CRCs across the United States could further examine how AI is expected to influence their roles and identify the types of institutional support needed to facilitate a smooth transition toward a more technology-driven research environment.

While it is hypothesized that CRCs require additional knowledge and training related to AI, semi-structured focus groups enable rich, contextualized discussion that can uncover nuanced gaps in understanding, concerns about workflow impact, and variability in readiness for adoption. As key stakeholders in the implementation of emerging technologies, CRCs can provide valuable insights into both the benefits and barriers associated with AI use in clinical research.

These discussions can elucidate specific training needs, preferred learning formats, and optimal delivery methods, thereby informing organizational strategies to better prepare CRCs for AI-augmented clinical trial operations. For example, the focus groups could clarify the potential need among CRCs for workshops and practical seminars focused on AI fundamentals, ethical use, and workflow integration. Such training programs could emphasize hands-on application and role-specific use cases to ensure CRCs can critically interpret AI outputs and apply them appropriately within protocol-driven site operations.

However, challenges to achieving this aim include CRC workforce shortages and high turnover rates, especially in the U.S.{25} In this context, the implementation of AI-enabled tools presents additional hurdles to be overcome, as such how technologies must be embedded within real-world site workflows. Implementation efforts therefore typically depend on trained end-users with deep operational and protocol-level knowledge, often operationalized as “super users.”{26} Consequently, experienced CRCs, after completing appropriate AI training, may be better positioned than novice staff to identify workflow failure modes, ensure protocol-compliant use, and translate AI outputs into actionable process improvements.

While AI offers significant gains in efficiency, its implementation introduces the risk of automation bias, where staff may become overly reliant on AI outputs. This complacency can lead to overlooked errors, potentially compromising patient safety and data integrity. Therefore, the role of the CRC remains vital.{27}

Experienced CRCs—provided they receive targeted AI training—are uniquely positioned to mitigate these risks. Unlike novice staff, veteran CRCs possess the clinical intuition to identify workflow failure modes and ensure that AI tools are used in a protocol-compliant manner. Their expertise allows them to validate AI-generated insights and translate them into actionable process improvements that align with the International Council for Harmonization E6(R3) guideline for Good Clinical Practice standards and maintain human-in-the-loop systems. To maintain these standards, clinical sites should implement a dedicated compliance manager role. This individual would oversee the Quality by Design framework, ensuring that AI integration consistently monitors critical-to-quality factors and adheres to rigorous data integrity guidelines.{28}

Concluding Remarks

As clinical trial protocols grow in complexity, the CRC role is transitioning toward a more strategic, data-centric function that remains anchored in clinical judgment. However, the current disparity between AI-driven recruitment and manual retention efforts continues to place a significant administrative burden on site staff, highlighting a critical need for collaboration to develop AI systems focused on participant retention. Because CRCs are essential for operationalizing these technologies, it is imperative to identify their professional development needs and ensure successful adoption.

Within this evolving landscape, new core competencies may emerge, including data literacy, AI ethics, and practical skills like prompt engineering. While these advancements offer the potential to reduce operational complexity, rigorous and continuous evaluation remains essential to verify the effectiveness of AI tools. Prioritizing these insights and competencies will ensure that technological integration delivers meaningful improvements while maintaining the highest standards of patient care and data integrity.

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Tracy Arakaki, PhD, PMP, PBA, became Director of Project Management with the Precia Group in January this year following nearly eight years of service as a Global Clinical Project Manager for Labcorp Drug Development and earlier work with UCB and the University of Washington.

Justin Scott Brathwaite, MBA, PMP, is a Clinical Research Associate at Fortrea, a PhD student in Clinical Research at the University of Jamestown, and an Editorial Advisor for ACRP’s Clinical Researcher journal. He has five years of experience in study start-up operations and is a two-time finalist in the PharmaTimes Clinical Researcher of the Year (Study Start-Up category) competition.

Kwame Le Blanc, BSc, MD, is studying in the Master of Public Health program of Queen’s University Belfast and has been a Teaching Fellow for St. George’s University in Granada, a Medical Intern for Mount Saint John Medical Center in Antigua and Barbuda, and a House Officer for the Millennium Heights Medical Complex in France and Owen King European Medical Center in Saint Lucia.

Chinedu Agumadu, BS, is a Clinical Research Coordinator at the University of Texas Southwestern Medical Center and previously worked at VAST Clinical Research, Parkland Health, and Metrocare Services.