Key takeaways
- AI enables data-driven site selection by forecasting site performance, investigator capacity and patient availability
- Using EHR, claims and registry data (with privacy safeguards) helps sponsors pre-identify eligible patients and shorten startup timelines
- Personalised, multichannel outreach and early risk flags reduce screen failures and patient dropouts
- Human oversight remains essential: models must be explainable, audited and aligned with GCP, GDPR and HIPAA
- A practical five-step framework to define goals, prepare data, model, pilot and monitor, which helps teams operationalise AI safely
Clinical trials are crucial for medical research and drug development, enabling pharmaceutical organizations to gather vital data on the safety and efficacy of health interventions.
These trials, ranging from small-scale Phase 1 studies involving 20 to 100 participants to late-stage, large-scale Phase 3 studies involving over 1,000 volunteers, play a pivotal role in advancing medical knowledge and bringing new treatments to patients. The duration of these studies can vary from several months to several years.
Challenges in conducting clinical trials
Clinical research conducted by Contract Research Organizations (CRO) operates within a vast ecosystem. This ecosystem includes various stakeholders such as pharmaceutical companies, biomedical researchers, regulatory authorities, patients and healthcare providers. It is characterized by countless connections and several permutations and combinations. Despite great strides in the pharmaceutical industry and biomedical research, the journey of bringing drugs to market remains complex, with significant room for enhancement. Clinical trials, due to their time-consuming nature and high costs, pose challenges that often lie beyond a company’s direct control, including regulatory constraints, patient access, costs, infrastructure and trial complexities.
Choosing the appropriate site is one of the most critical decisions that significantly impacts trial success. Optimal site selection can alleviate challenges related to infrastructure, patient availability and cost in decentralized, patient-centric trials.
What is site collection in clinical trials?
Site selection is the process of identifying, evaluating and prioritising clinical trial sites (institutions and investigators) that can recruit the target population, execute the protocol to quality standards and comply with regulations; on time and within budget. Its purpose is to maximise operational feasibility and data quality while minimising risk.
Scope: Review of historical site performance, investigator expertise and bandwidth, patient prevalence and care pathways, operational readiness (staffing, labs, pharmacy, imaging), start-up timelines, contracting and regulatory history, data systems and quality management practices
How it differs from feasibility and qualification:
- Feasibility assessment gathers market-level and site-level information to determine whether a trial could run (patient availability, standard of care, competing studies)
- Site qualification (e.g., pre-study visits) confirms a shortlisted site will meet protocol and GCP requirements by inspecting facilities, processes and documentation
- Site selection uses evidence across feasibility and qualification to choose and rank sites for activation
Common challenges: Optimistic self-reports, fragmented data across CTMS/EHR/registries, variable investigator attention amid competing studies, contracting delays, inconsistent SOPs and unreliable patient availability estimates
Key challenges when optimizing site selection
- Site experience and investigator performance: Selecting the right clinical trial site requires evaluating numerous factors. This includes assessing the number of patients screened, prioritizing quality over quantity and investigating screen failures to optimize recruitment strategies. Efficient patient enrollment, understanding the average time taken to recruit one patient, monitoring dropout rates and focusing on retaining patients are crucial throughout the study. Lastly, it is important to ensure adherence to the study protocol by minimizing protocol deviations. Adopting an integrated approach considers both quantitative metrics and qualitative aspects such as site expertise and commitment to trial success, as well as the availability of the right equipment.
- Infrastructure & equipment readiness: Adequate infrastructure facility and equipment are essential for a successful trial. Inadequate facilities can result in errors, delays or compromised data quality. Sites should be well-equipped with laboratories, storage areas and specialized tools relevant to the study. The presence of calibrated instruments and trained personnel is essential. Regular maintenance and quality checks for equipment are imperative to prevent disruptions during the trial.
- Patient access, prevalence and care pathways: Identifying sites with access to the target patient population is pivotal. Consider local demographics, disease prevalence and healthcare infrastructure. Sites located near hospitals or clinics where potential participants seek medical care are more beneficial. Collaborating with such sites can enhance subject recruitment and retention.
The data overload conundrum
Selecting the right site for a trial requires extensive data collection, encompassing various aspects such as patient demographics, site capabilities and regulatory considerations. However, challenges such as ensuring data completeness, quality maintenance and managing inconsistency hinder the effectiveness of traditional methods in utilizing the data.
By employing data analytics and AI techniques in the tech-infused evolution of clinical trials, sponsors can foster more objective and data-driven decision-making. Effective data management optimizes resource utilization, increases scientific collaboration and improves decision-making. Furthermore, leveraging this data helps in predicting the site performance, thereby improving the overall trial efficiency.
How does AI transform site selection in clinical trials?
Advancements in AI empower CROs in their search for Optimal Clinical Trial Site identification by churning and turning enormous amounts of data into actionable insights, facilitating informed decision-making on site selection or ejection. Despite the data being readily available, segregating, analyzing and understanding if it aligns with the requirements can be overwhelming and can even paralyze the process.
AI automates data cleaning processes, improving data quality and completeness. ML algorithms detect patterns in historical data to forecast site performance, refining site selection efficiency. Real-time monitoring of trial data facilitated by AI also enables proactive decision-making. Leveraging advanced AI technology allows sponsors to integrate predictive analytics, automate data workflows, and enhance overall trial efficiency. Furthermore, AI and ML personalize patient recruitment strategies based on data trends, enhancing patient enrollment rates. Lastly, they model complex relationships between variables, supplementing traditional statistical methods for more accurate site selection.
How AI improves patient recruitment and retention
AI analyses structured (EHR, claims, registries) and unstructured data (clinical notes, radiology reports) to match inclusion/exclusion criteria with local care pathways, producing ranked pre-screen lists for investigators. It personalises outreach by choosing channels (SMS, portals, mail), timing and message content most likely to engage each patient cohort, and it predicts dropout risk to trigger early interventions (transport support, visit windows, ePRO nudges).
Examples include:
- Pre-screening acceleration: An oncology sponsor applies NLP to pathology reports and staging notes to pre-identify candidates meeting biomarker and line-of-therapy requirements, cutting manual chart review and enabling sites to contact eligible patients sooner
- Retention risk flagging: A cardiology device trial uses a model that combines prior missed appointments, distance to site and social determinants (such as transport access) to flag high-risk participants, prompting coordinators to schedule telehealth visits or rideshare support, reducing early discontinuations.
Balancing data volume and signal
Now, how do we strike the right balance? By using AI and ML, we can make sense of this data overload. Instead of drowning in too much or too little information, we focus on the important stuff. Here is how:
Prior enrollment metrics (screen, randomize, retain): AI models can quickly scrutinize historical data points related to patient enrollment in past clinical trials. This analysis predicts enrollment rates for future trials and helps optimize planning and resource allocation.
Patient access, eligibility and SDoH signals: By identifying potential barriers to enrollment and suggesting ways to improve patient access by utilizing information about patients’ ability to access trial sites, their health status and other relevant factors.
Equipment availability, calibration and uptime: Advanced AI solutions can help ensure that trials are well-equipped and predict and prevent potential equipment-related issues by accessing information about the medical equipment used in trials, such as its availability, functionality and usage.
Leveraging AI and ML can help prevent trial sponsors from compromising on the data volume, enabling them to make an informed decision on site selection and adhering to all the criteria required to be an optimal site for the trial.
Risks, ethics and regulatory considerations
AI can introduce bias, such as under-representation of certain demographics, privacy and security risks, model drift and opacity in decision-making. Sponsors and CROs should:
- Align processes with ICH-GCP (quality, monitoring, documentation) and comply with data protection laws such as GDPR (lawful basis, minimisation, DPIAs, data subject rights) and HIPAA (PHI safeguards, minimum necessary)
- Use de-identification/pseudonymisation, role-based access and audit trails and obtain appropriate consents/authorisations where required
- Ensure transparency and human oversight by documenting model intent, features and limitations, enable explainability for site ranking and patient-matching outputs and involve investigators in final decisions
- Validate models prospectively, monitor performance and bias over time, and maintain change control for algorithms and datasets
Practical framework – AI-assisted site selection in 5 steps
- Set goals & guardrails: Define protocol-specific objectives (enrollment rate, diversity, timelines), target geographies, fairness constraints and success metrics
- Assemble and govern data: Integrate historical site KPIs, EHR/claims signals, SDoH, start-up timelines and quality metrics, while establishing data dictionaries, lineage and privacy controls
- Build and select models: Engineer features (patient prevalence, investigator bandwidth, startup predictability), train ranking/forecast models and perform explainability and bias checks
- Pilot and validate: Run a limited-scope pilot across a subset of countries/sites, compare against traditional selection, capture deviations, screen failures, cycle times and audit evidence.
- Operationalise and monitor: Embed outputs in workflows (site lists, pre-screen queues), enable human review, set alerts for drift/bias and review metrics in governance meetings; iterate.
The path forward
As AI solidifies its presence, the clinical trial landscape is poised for transformation. Success lies in identifying sites with principal investigators capable of attracting high-quality subjects meeting baseline clinical trial requirements. AI models, under the direction of CRO experts, can meticulously analyze data and rank results, institutions, sites, investigators, countries and geos, empowering the trial sponsor to zero down on the right sites. This helps in seamless engagement and conduct of the trial and demonstrates the value of advanced biopharma solutions in accelerating drug development.
By embracing AI-enabled capabilities, biopharma companies can:
- Optimize site selection
- Develop core AI competencies
- Reinvest resources strategically
The proliferation of AI within the clinical domain is both promising and well-deserved. However, this symbiotic relationship between technology and biopharma necessitates robust collaboration, rigorous testing and a deeper collective comprehension.
