Scalable Data Infrastructure: The new backbone of modern clinical trials

Clinical trials have long been regarded as the gold standard for evaluating the safety and efficacy of drug candidates. However, the nature of clinical research has changed profoundly in recent years. What was once a domain dominated by binary outcomes (success or failure, response or non-response), has evolved into a data-intensive ecosystem characterized by continuous monitoring, molecular profiling, and real-time patient engagement.

Modern clinical trials now generate thousands of terabytes of data across a wide spectrum of modalities. These include genomics and transcriptomics, radiological imaging, laboratory biomarkers, wearable device outputs, and electronic health records (EHRs), among others. This multimodal approach enables a more comprehensive and nuanced understanding of therapeutic effects and patient variability. Yet, the scale and heterogeneity of this data also introduce significant challenges in integration, harmonization, and analysis. Data is often fragmented across systems, inconsistently formatted, and semantically misaligned, making it difficult to construct unified patient profiles or build reproducible analyses. Moreover, high-dimensional omics and unstructured clinical notes require sophisticated processing, while real-time data streams from global trial sites can overwhelm conventional data infrastructure.

Researchers often find themselves constrained not by a lack of data, but by the inability to extract meaningful insights from it. Manual data wrangling is not only time-consuming but often infeasible at the scale of modern trials. Moreover, many clinical scientists lack the coding skills or computational infrastructure required to build automated data pipelines. This creates a critical need and opportunity for data scientists to collaborate with research teams and develop scalable, intelligent systems that streamline data integration and enable insight generation.

With healthcare increasingly oriented toward precision medicine, the focus of clinical trials is also shifting from population averages to individual responses, mechanisms of action, and safety profiles. This evolution places an even greater premium on well-structured, high-quality, and interoperable data. Integrated datasets allow clinical teams to identify responder subgroups, link molecular features with clinical outcomes, and support AI-driven decision-making.

In this context, the ability to manage, harmonize, and analyze data at scale becomes a foundational requirement. For organizations seeking to accelerate discovery, reduce time to market, and improve patient outcomes, investing in robust clinical data infrastructure is imperative.

In this blog, we explore a set of best practices for effective analysis and integration of multimodal clinical data, drawn from real-world experience and scalable infrastructure design. We also present a case study illustrating how one diagnostics company adopted these practices using Elucidata’s Polly platform to transform fragmented datasets into a harmonized, AI-ready foundation for accelerated biomarker discovery and patient cohort analysis.

Best Practices for Clinical Trial Data Analysis and Integration

To harness the full potential of multimodal clinical trial data, research teams must move beyond traditional data management. The goal is not merely to store or access data, but to create a harmonized, AI-ready foundation that accelerates analysis, interpretation, and discovery. The following best practices provide a blueprint for designing scalable, compliant, and insight-driven data ecosystems in clinical research.

1. Start with Standardization – Early and Often

Standardization is the foundational step that ensures clinical trial data is interoperable, interpretable, and ready for integration across diverse modalities. In modern trials, data is collected from multiple institutions, instruments, and vendors, each with its own structures, terminologies, and documentation conventions. Without early and consistent standardization, ambiguity and inconsistency accumulate across datasets, complicating every downstream analysis.

A key part of standardization involves using controlled vocabularies that define how concepts are named and categorized. For example, LOINC provides standardized codes for laboratory tests and clinical observations, while MedDRA is used to classify adverse events, indications, and medical histories. Terminologies such as ICD-10 and SNOMED CT further support the classification of diagnoses, medications and conditions. These semantic standards ensure that identical concepts such as “myocardial infarction” and “heart attack” are consistently recorded and understood across data sources.

In parallel, data models such as CDISC’s SDTM and ADaM help structure datasets for regulatory submission and analysis. SDTM organizes collected clinical data into consistent domains (e.g., demographics, adverse events, vital signs), while ADaM transforms that data into analysis-ready formats, supporting traceability and statistical reproducibility.

In addition to terminologies and data models, formatting conventions also play a critical role. Variables must follow consistent naming schemes, units should be standardized across datasets (e.g., mg/dL vs. mmol/L), and dates must adhere to formats like ISO 8601. These formatting standards allow datasets from disparate sources to be merged and interpreted without excessive preprocessing.

Crucially, standardization should not be an afterthought. When implemented during study design, through clear data collection guidelines and pre-defined schemas, it reduces variability across sites, streamlines cleaning efforts, and ensures that trial outputs are ready for submission and analysis. Practiced early and maintained rigorously, standardization becomes the backbone of every scalable and reliable clinical data pipeline.

2. Invest in Scalable, Automated ETL Pipelines

As the volume and complexity of clinical trial data increases, manual data wrangling quickly becomes unsustainable. Scalable ETL (Extract, Transform, Load) pipelines allow teams to ingest raw data from multiple sources, apply transformation logic, and output harmonized, analysis-ready datasets efficiently and accurately.

An effective ETL pipeline begins with automated ingestion from a wide variety of sources – relational databases, flat files, cloud storage, or unstructured documents. These pipelines validate incoming data structures, identify missing values, and flag formatting inconsistencies before errors propagate downstream. Once ingested, the transformation stage performs standardization, ontology mapping, and variable alignment. It links patient records across timepoints and modalities, and annotates each record with the necessary metadata to preserve context.

Crucially, transformation logic must be modular and version-controlled, allowing traceability and reuse across multiple studies. After transformation, the cleaned data is loaded into secure analytical environments in compliance with regulations like HIPAA, GDPR, and 21 CFR Part 11. Encryption and access control safeguard sensitive information, while role-based permissions and audit trails ensure accountability. Platforms like Polly exemplify this architecture, making harmonized data easily accessible to analysts, clinicians, and data scientists without repeated manual intervention.

3. Preserve Context with Robust Metadata Management

Structured data alone is insufficient without the metadata that gives it meaning. Metadata, i.e. information about who generated a data point, when, under what conditions, using which methods, anchors data in its scientific and operational context. Without it, even clean datasets risk being misinterpreted or rendered unusable.

In clinical trials, metadata encompasses patient demographics, sample origin, assay parameters, instrument settings, visit numbers, and more. It supports cohort creation, longitudinal analysis, reproducibility, and regulatory audits. Yet, metadata is often inconsistent, incomplete, or embedded in unstructured text. To overcome this, modern tools, especially large language models and natural language processing algorithms, can extract structured metadata from clinical notes, radiology reports, and protocol documents. They can recognize named entities, infer timepoints, and convert narrative descriptions into structured fields.

Once captured, metadata must be stored in structured, queryable formats and indexed to support filtering and analysis. Researchers should be able to query datasets by metadata attributes, for example, to compare gene expression across samples collected at Week 12 from female patients aged 50 and above. Versioning ensures that any changes to metadata are logged and auditable. Without this infrastructure, metadata is easily lost, and with it, so is the reliability of the analysis. Investing early in metadata management ensures that every data point remains interpretable and actionable throughout the research lifecycle.

4. Enable Cohort-Based Analysis and Visualization

Clinical data gains value when it is explored in context and as patterns within patient groups. Cohort-based analysis allows researchers to segment data by shared characteristics and compare outcomes across well-defined subpopulations.

For example, defining a cohort of EGFR-positive patients treated with a specific dose and achieving partial response at Week 8 allows researchers to isolate predictors of efficacy. This is especially important in precision medicine, where meaningful insights often emerge from subgroup-level trends.

To support this, data platforms must provide intuitive cohort builders that support both clinical and molecular filtering using logic, stratification rules, and metadata fields. No-code or low-code interfaces make this capability accessible to clinicians and translational scientists without programming expertise. Once cohorts are defined, integrated visualization tools like Kaplan-Meier plots, gene expression heatmaps, and biomarker timelines, enable teams to explore trends, communicate findings, and validate hypotheses rapidly.

Without these tools, teams resort to static exports, spreadsheet filtering, and custom scripting, slowing down discovery and introducing inconsistencies. Cohort analysis should be embedded directly into the data environment, enabling fast iteration, better collaboration, and higher confidence in decision-making.

5. Make Data AI-Ready, Not Just AI-Accessible

AI holds enormous promise for clinical research, but only when built on a foundation of high-quality, well-prepared data. Making data AI-accessible simply means it exists in a digital format; making it AI-ready means it is labeled, aligned, clean, and contextually rich enough to support robust algorithmic learning.

This begins with clear, accurate labeling. Clinical datasets must be annotated with outcomes such as response status, survival endpoints, or adverse events, and time-aligned to trial visits or treatment milestones. Labels must be traceable back to raw data and metadata to ensure model reproducibility and regulatory validity.

Equally important is cross-modal alignment: integrating molecular, imaging, clinical, and behavioral data under unified patient IDs and synchronized timepoints. A model predicting response to breast cancer therapy, for instance, may require genomic data, HER2 protein levels, imaging results, and clinical staging, which are all captured from different systems.

AI models are also highly sensitive to inconsistencies. Proper preprocessing must include normalization, batch correction, missing value imputation, and bias mitigation especially in omics data. The infrastructure must support model training, validation, and deployment within secure, auditable environments, with versioned datasets and integrated outputs.

When this groundwork is missing, AI models produce spurious correlations, fail to generalize, or generate insights that can’t be trusted clinically. Investing in AI readiness ensures that models are grounded in structured, high-quality data, enabling them to support biomarker discovery, predictive modeling, and decision support with confidence.

Case Study: Applying Best Practices at Scale with Polly

A large diagnostics company faced a familiar yet formidable challenge: how to integrate, harmonize, and analyze massive volumes of clinical and biomarker data coming from diverse sources. The goal was to accelerate biomarker discovery and patient stratification for a range of therapeutic programs. But the starting point was chaotic.

They were working with:

• Over 100 million electronic health records (EHRs) spanning a decade
  
  
• 50 million imaging files, many with limited metadata
  
  
• 25 million omics datasets from various assay platforms

This data was fragmented across departments and vendors, inconsistently labeled, and lacked the harmonization needed to perform reliable, reproducible analysis. Traditional data management approaches simply couldn’t keep up, both in terms of scale and speed.

Implementing Best Practices with Polly

To overcome these challenges, the organization adopted Polly by Elucidata, the only AI-ready clinical data platform purpose-built to handle multimodal datasets.

Here’s how Polly operationalized the five best practices:

1. Standardization at Scale

Polly’s proprietary data model supported over 1,200 attributes across clinical, molecular, and imaging data. This ensured uniform formatting, terminology mapping (e.g., ICD codes, MedDRA), and seamless integration.

2. Scalable ETL Pipelines

Customizable ETL workflows enabled ingestion from cloud storage platforms (like S3, Blob, Google Drive), with automated quality control and pre-harmonization reports generated before data even entered the system. This eliminated over 1,500 hours of manual cleaning.

3. Metadata Extraction Using LLMs

Polly used large language models to automatically extract and standardize metadata from free-text clinical notes, pathology reports, and imaging annotations, removing one of the largest manual burdens in the pipeline.

4. Cohort-Based Insights with Polly Atlas and Insights

Once harmonized, data was made accessible through Polly Atlas, allowing researchers to define patient cohorts based on any combination of data types. With Polly Insights, even non-technical users could create dashboards and analyze trends across biomarkers, treatment arms, or outcomes, without the requirement of code.

5. AI-Readiness by Design

Polly transformed raw data into a machine-learning ready format, ensuring compatibility with modeling workflows for adverse event prediction, biomarker discovery, and subgroup analysis.

Impact

The adoption of Polly led to measurable gains:

• 4 times lower cost in generating clean, multimodal data products
  
  
• 2 times faster model development and deployment
  
  
• Near-instantaneous data access (sub-100 ms latencies)
  
  
• A unified view of patients across modalities that enabled more confident decision-making

This case highlights how adopting the right data infrastructure and applying best practices early can turn clinical trial data from a bottleneck into a strategic asset.

Accelerate your clinical research with AI-Ready data integration

As clinical trials become more sophisticated and data-rich, the systems and practices we use to manage that data must evolve in parallel. Integrating EHRs, imaging, omics, and patient-reported outcomes is a requirement for answering today’s most urgent research questions. Yet, without harmonization, standardization, and scalability, multimodal data becomes more of a burden than a benefit. It delays insights, dilutes discoveries, and undermines the potential of precision medicine.

Platforms like Polly prove that it’s possible to go from fragmented data to integrated insight efficiently, affordably, and at scale. If your team is still spending hours wrangling spreadsheets, stitching together datasets, or waiting on siloed systems, it’s time for an upgrade. Polly is the only AI-ready platform designed to integrate multimodal clinical data, from EHRs to omics, and turn it into insights you can trust.

Book a demo today to see how Polly can help you accelerate discovery, streamline analysis, and scale your clinical data infrastructure – all while saving time and cost.

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