Clinical research generates vast amounts of diverse data from laboratory tests, patients, medical equipment, and outside sources. By organising and analysing this information, researchers can extract actionable insights that improve patient outcomes, data accuracy, drug efficacy and speed up trials.
Furthermore, recent advances in cloud computing, machine learning, and real-world evidence (RWE) have revolutionised clinical trial data management. RWE data from wearables, data from Electronic Health Records (EHRs), and data from patient-reported outcomes (PROs) tools all expands the relevance of study findings, while cloud computing offers secure platforms for real-time data exchange and large-scale analytics. Together, these technologies enable more intelligent, efficient, and patient-centred research.
What is Big Data?
Before we dig deeper into big data in clinical trials it is important to understand the concept for those who may be unfamiliar with the term.
Big data is the collection of very large and varied datasets that exceed the capacity of standard tools to process. It includes information from various sources such as RWE, EHRs, lab tests, wearables, and genomics. This data grows rapidly and arrives in different formats, from numbers and images to text and signals. By uncovering hidden patterns and trends, big data helps teams make faster, more informed decisions. In clinical trials, this means spotting safety signals sooner, selecting the right patients, and adapting study plans on the fly.
The Characteristics of Big Data in Clinical Trials
Big data in clinical trials can be characterised by five V’s:
- Volume: The sheer scale of data, from millions of EHR entries to multi-omics sequences.
- Velocity: The speed at which data arrives, such as continuous streams from wearables and remote sensors.
- Variety: The range of formats, including structured lab results, unstructured notes, images and signals.
- Veracity: The need for accuracy and reliability, ensured through data cleaning and validation.
- Value: The actionable insights that drive better trial design, patient selection, and safety monitoring.
Examples of Big Data in Clinical Trials
As mentioned, there are several advances in technologies and trial design approaches that have led to vast amounts of data being generated in trial analysis. Let’s examine these new approaches in more detail:
Cloud Computing
Cloud platforms store and process petabytes of trial data. They scale to handle millions of data, and they stream data in real time from EDC systems and sensors. Cloud systems secure patient data, meet GCP, GDPR and HIPAA standards, and reduce costs by replacing on-site servers. For example, sponsors use cloud tools to integrate longitudinal registry data and continuous ECG feeds, speeding analysis and ensuring compliance with regulatory rules.
Real-World Evidence
RWE draws on external data from outside of a controlled, regimented clinical trial environment to support trial findings that you may not have captured in clinical sites. It can be information from registries, insurance claims, EHRs, and can be used to find more participants, create external control cohorts and confirm safety trends, resulting in faster insights and more relevant outcomes. RWE can also come from wearable devices and mobile apps, bring almost real time data to a study. By bringing in real world data (RWD) you are adding even more data to your big data datasets.
Genomics and Precision Medicine
Genetic and biomarker data allows trials to focus on groups with shared traits, improving the safety and effectiveness of new therapies for each patient’s profiles.
Machine Learning
Not exactly an example, but big data is what is making machine learning possible. ML uses large datasets to improve processing and interpretation by learning patterns over time. ML systems can predict patient outcomes, find trends, and flag safety concerns faster than manual review, optimising patient selection, dosing, endpoints, and supporting adaptive trial designs.
For example, NLP models can read doctors’ notes and reports alongside structured trial forms, adding context that helps identify patients more accurately than structured data alone (Chang et al., 2023).
ML also cleans and validates data, correcting missing values and spotting outliers in imaging files (CT, MRI, PET) and multi-omics datasets, and supports models that adjust dosing cohorts’ mid-trial to balance safety and efficacy. Furthermore, AI tools speed up patient matching, predict outcomes and flag dropout risks, combining all sources for analysis.
How Big Data Improves Clinical Trials
Having more data available improves clinical trials, simply put, the more data you can collect, the more beneficial this will be for your trial because you are increasing the amount of data that can be analysed to help demonstrate the drug’s efficacy and safety. Therefore, big data enhances every stage of a clinical trial, from planning through post-market surveillance. Let’s look at some specific examples:
Faster Patient Recruitment
Big data platforms combine EHRs, genomics, registries and social media to identify eligible participants and estimate who will complete the study. This approach speeds up enrolment, broadens participant diversity and improves retention.
Real-Time Monitoring
Wearables in clinical trials and mobile sensors increases the amount of data available as always able to record and capture and record vital signs and activity metrics directly into trial databases. This is a huge increase in data compared to just taking readings during site visits and benefits researchers as they are able to spot adverse events sooner, adjust protocols immediately and keep participants engaged even at a distance.
Optimised Trial Design
Big data sources from past study results and real-world data are what machine learning models access to perform analyse to predict outcomes, forecast dropout risks, and recommend study parameters to optimise trial designs. These big data sources enable trials to achieve higher success rates, require fewer amendments, and face fewer delays.
Improved Compliance and Standardisation
Big data drives ML models that can improve trial compliance by detecting protocol deviations, data inconsistencies, and missing values across large datasets. This reduces manual errors, supports audit readiness, and ensures clean, compliant data throughout the trial.
Adverse Event Detection and Safety Monitoring
Machine learning tools are using big data source to continuously scans data streams, triggering alerts when safety thresholds are crossed which helps identify safety signals earlier, reduce event severity, and build trust in safety reporting.
Decentralised Trials and Virtual Trials
Big data drives telemedicine platforms, ePRO and wearables feed data in real-time into unified systems. This makes decentralised and virtual trial methodologies possible and allows drug developers to recruit and reach remote participants, lowering travel burdens and ensure studies remain operational during crises, like we saw with the global pandemic when there were restrictions on travel and patients could not visit sites.
Post-Market Surveillance and Follow-Up
As mentioned, post-market surveillance is improved as global registries, claims databases, PROs and EHRs all monitor long-term drug performance. These data sources are big data environments, and it enables sponsors to detect early identification of rare or long-term side effects, gather evidence for RWE for label expansion, and support informed regulatory and clinical decision-making.
Regulatory and Compliance Considerations
The emergence of big data has resulted in drug developers now handling, processing and having access to a vast amount of data that previously wasn’t available, despite the benefits it has also created regulatory and compliance considerations to protect patient privacy, study and data integrity that cannot be ignored.
Regulatory Perspectives
- U.S. Food and Drug Administration (FDA): The FDA’s RWE framework guides registry, EHR and claims data in submissions. Furthermore, their Digital Health Center of Excellence issues best practices for AI and digital tools.
- European Medicines Agency (EMA): The EMA created DARWIN EU® to standardise RWE access and speed up reviews.
- UK’s Medicines and Healthcare products Regulatory Agency (MHRA): The MHRA’s road map insists on clear data provenance and aligns UK rules with global standards.
Data Privacy Rules
From a data privacy perspective, on the other hand, GDPR and HIPAA have also issued guidance for handling large datasets in clinical research.
- General Data Protection Regulation (GDPR): Organisations must obtain patient consent, minimise data collection, pseudonymise records and conduct Data Protection Impact Assessments.
- Health Insurance Portability and Accountability Act (HIPAA):
HIPAA requires de-identification of PHI and maintenance of detailed audit trails.
For a greater idea on regulatory considerations for big data on a global scale, we've included a glimpse of additional regulations in place that echo the above standards upheld across the UK, EU, and US. These include:
- PIPEDA: Canada applies GDPR-like rules to research data.
- APPI: Japan enforces consent and data localisation for clinical data.
- PIPL: China restricts data transfers and mandates local storage.
Challenges and Considerations of Big Data in Clinical Trials
Despite the possibilities that the use of big data in clinical research present, there are still several challenges that need to be taken into consideration and resolved. These include the following:
Data Quality
Inconsistent data formats and incomplete records can prevent seamless integration, so organisations must enforce CDISC standards and implement thorough data-cleaning protocols.
Infrastructure
Analysing petabyte-scale datasets requires powerful, secure cloud platforms that can scale on demand and maintain high performance.
Analytic Bias
ML models can reflect biases in their training data, so teams should conduct regular audits, use diverse datasets for training, and document all algorithmic assumptions.
Patient Diversity
To ensure study results apply broadly, trials must include data from under-represented groups, apply demographic stratification and partner with community organisations for inclusive recruitment.
The Future of Big Data in Clinical Trials
Looking ahead, big data promises to continually transform clinical research, leading to more efficient trials and improved patient outcomes. Some of the key trends to look out for include:
Interoperability and Data Integration
Standards such as CDISC and HL7 FHIR link clinical, genetic, imaging and wearable data, ensuring that all sources can be combined and analysed together.
Blockchain for Data Integrity and Transparency
Immutable, timestamped records ensure data origin and auditability, support secure sharing among sponsors, sites and regulators, and simplify consent tracking.
Federated Learning for Privacy
This method trains AI models across multiple sites without moving patient data, preserving privacy, meeting data-protection rules and enabling insights from diverse populations.
Partnerships among tech firms, sponsors, sites, and regulators will unlock faster development, better outcomes, and lower costs. Trials will operate in real-world settings, with AI flagging safety signals and data personalising treatment.
Conclusion
Big data now supports modern clinical research. Harnessing vast, varied information accelerates timelines, improves protocols, enhances safety, and broadens inclusion. While privacy, interoperability, and validation challenges remain, they spur more rigorous, transparent governance. Regulators increasingly recognise data-driven evidence. Sponsors and CROs must invest in analytics, ML models, and data talent today to lead the next wave of medical breakthroughs.
Quanticate's statistical programming team help turn complex trial data data into clear, real-time insights you can act on, with secure platforms and advanced analytics to uphold data integrity and regulatory compliance every step of the way. To learn how our expertise can optimise your study execution and outcomes, submit an RFI today.
