OVERVIEW

The fragmented and complex nature of RWD— spread across numerous data types including claims, diagnostics, and EHRs—poses significant challenges to effective analysis. These datasets commonly remain siloed and disconnected due to the complexity and resources required to effectively link this data at scale. Large Language Models (LLMs) are emerging as transformative tools for linking disparate RWD records, enabling life sciences companies to generate deeper insights and accelerate innovation.

What are the current trends in patient linking RWD?
What are the common challenges being faced by the Life Sciences industry?
How can AI help address these needs?
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What are the root causes of interlinking challenges?
What are the common issues faced when trying to interlink RWD datasets?
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How is AI is being used to analyze real world data?
How can we process unstrucutred data?
How can AI help improve dataset analysis?
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Common challenges in analyzing RWD across datasets.
How can AI be used to analyze disconnected datasets?
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AI can analyze common terms and phrases for common synonyms, translations, or spellings to identify comonalities.
How can AI translate hand-coded terms to standard codes or terminologies?
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How can AI improve the efficiency of linking algorithms?
How can AI increase the quality of linking algorithms?
How can AI enable linking complex datasets at scale?
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How can AI-driven data linking improve RWD analysis?
What are the benefits of AI-driven linking?
What areas of life sciences can benefit from AI-driven linking?
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Read the summary of the ways that AI can drive more accurate, more efficient, and faster data linking within and across datasets.
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INTRODUCTION

We continually look for new and innovative ways to better quantify, measure, and understand the mechanics inside the human body, particularly for medical research and treatment. Real World Data has been a critical tool in answering these important questions about creating new, novel treatments and how to make existing ones more effective.

However, the fragmented and complex nature of RWD—spread across electronic health records (EHRs), diagnostics, multi-omics, claims, clinical trials, patient registries, and more—poses significant challenges to effective analysis. Large Language Models (LLMs) are emerging as transformative tools for linking disparate RWD records, enabling life sciences companies to generate deeper insights and accelerate innovation.

This has become a jigsaw puzzle where we lack the edges to know the boundaries, have a fleeting vision of what the end result might be, and struggle to find the right way to link many of the pieces. Yet amidst this struggle, researchers every day try to tackle this puzzle to find those miraculous pieces that do connect together and can result in those ground-breaking discoveries.

**RWD MARKET GROWTH**

The global RWD market size is projected to grow from **$1.59 B in 2023 to $4.07 B by 2030, at a CAGR of 14.4%.** The increasing adoption of RWD in drug development and approvals, market access, and post-market surveillance is driving the growth of the market.
[Source]

CHALLENGES OF LINKING RWD RECORDS

With the wealth of RWD sources available, it may seem counter-intuitive that we struggle to connect this wealth of data together to generate meaningful insights. However, companies encounter numerous, diverse hurdles in integrating and linking RWD datasets. A few of the most common issues include:

Inconsistent Data Formats

Patient data is stored in various formats, such as unstructured clinical notes, structured EHR tables, or semi-structured files like PDFs. Coupled with the limited standards deployed across EHR solutions, and any given patient may have dozens of different records, all comprised of components in different formats, making their full profile. This diversity of data that must be linked to be accurately analyzed causes many organizations to oper

Missing Data Elements

Incomplete patient or treatment records can easily break common linkage methods or algorithms, reducing usability. EHR data is only as valuable as its precision and accuracy. Datasets that lack critical data elements like identifiers or codes may be impossible to connect to other records without high error rates.

Ambiguous Identifiers

Common anomalies in data, like variations in patient names, dates, or provider details, can lead to missed connections between datasets. Whether it’s data entry errors, different formats, or lack of standardization, all too frequently datasets cause high rates of false-positive and false-negative matches due to lack of specificity in the matching algorithms or the underlying raw data.

Solution Scalability

As the volume of RWD continually grows, traditional data storage and record-linking methods struggle to keep pace. With the expected growth of RWD to continue at the exponential pace we’ve seen over the last 10 years, the volume and complexity of data the average organization processes is growing dramatically as well. Traditional tools cannot process this volume of data efficiently, at scale.

HOW LLMS ADDRESS LINKING CHALLENGES

1. PROCESSING UNSTRUCTURED AND SEMI-STRUCTURED DATA

While there have been techniques for parsing specific keywords and phrases in unstructured content, these methodologies have been rudimentary and lack the ability to understand the context around where and how the words or phrases are found.

By contrast, LLMs excel at extracting entities such as medical conditions, interactions, medications, or procedures from the unstructured text. More so, they can look for phrases with relevancy scores that prioritize the search based on the context of the document.

For instance, they can parse clinical notes to identify diagnoses and link these with structured claims data, creating a more complete patient profile. This capability is particularly valuable for life sciences companies conducting outcomes research or pharmacovigilance.

DATA PROCESSING

Unstructured and strucutred data alike can be processed and analyzed wiht new LLM capabilities.

2. ENTITY MATCHING ACROSS DATASETS

A common challenge in linking RWD datasets is the lack of quality matches between them. Simple variations in data formats, terminology, or standards (e.g., ICD-10) can create significant gaps, leaving as much as 67.8% unmatched data. This limitation affects the quantity and quality of insights derived from RWD investments.

Fortunately, LLMs excel at understanding context and meaning, making them valuable for connecting these disconnected data elements even if they lack direct matches but are semantically related. This is where LLMs shine, offering advanced semantic mapping capabilities to connect and harmonize RWD elements across datasets.

These models also facilitate the integration of structured and unstructured data by extracting meaningful entities from sources such as clinical notes or reports and linking them to structured data elements like lab results or medication records. This semantic understanding transforms disjointed datasets into unified, analyzable assets.

For life sciences companies, the implications of streamlining RWD integration are profound. Companies can accelerate drug development, enhance pharmacovigilance, and generate deeper insights into patient outcomes. Furthermore, the ability to semantically link data elements enables a more comprehensive understanding of patient journeys, supporting innovations in personalized medicine and treatment optimization.

For example, they can map different terminologies used in medical records, such as recognizing “myocardial infarction” and “heart attack” as equivalent. Similarly, they can reconcile coding inconsistencies, such as linking ICD-10 codes with SNOMED CT concepts.

TERMINOLOGY TRANSLATION

LLMs can map different terminologies used in medical records; recognizing “myocardial infarction” and “heart attack” as equivalent and linking the proper ICD-10 code.

3. STANDARDIZING TERMINOLOGIES

A persistent challenge in linking RWD lies in the diverse and inconsistent terminologies used across datasets. Terms like “myocardial infarction,” “MI,” “heart attack,” and “ICD-10: I21.9” all refer to the same condition, but these discrepancies and inconsistencies can create significant barriers to data linkage. Large Language Models (LLMs) provide a powerful solution by standardizing and harmonizing terminologies, mapping terms to standardized ontologies, enabling seamless integration, and ensuring consistency across linked records. By understanding the context in which terms are found, they can interpret a more complete meaning.

LLMs excel at understanding the meaning and context of terms, regardless of their variations. By mapping disparate terminologies to standardized vocabularies like SNOMED CT, ICD codes, or MedDRA, LLMs can deliver an all-important consistency across datasets. This capability also bridges the gap between unstructured and structured data, ensuring all elements are harmonized for analysis.

By adopting LLM-driven standardization, life sciences companies can overcome the complexity of linking diverse RWD sources. This not only accelerates the integration process but also improves data quality and reliability. Enhanced data linkage supports critical applications such as drug development, pharmacovigilance, and personalized medicine, where a unified view of patient data is essential.

For instance, these models can automatically identify and reconcile variations in disease names, medication descriptions, and procedural terms, aligning them with global standards. Additionally, LLMs can handle free-text data from clinical notes, extracting and normalizing information to match structured fields.

STANDARDIZATION

AI models can automatically identify and reconcile variations in disease names, medication descriptions, and procedural terms.

4. AUTOMATING AND SCALING LINKAGE PROCESSES

Traditional record-linking methods require complex layers of algorithms and manual rule creation. This takes personal intervention in the design and coding to attmept to identify the potential record linking candidates.

Traditionally, this is a time-consuming process, resource-intesive setup and limited in scalability. Many organizations resort to operating in relative data silos. Rather than gaining the complex, contextual insights across multiple datasets, they are limited to the scope that one dataset can provide.

LLMs can learn and adapt to new patterns in data, automating the linkage process and handling the growing scale of RWD in real time.

Linked Medical Files — LINKING AT SCALE

LLMs can link data at unprecedented scale with improved quality, using context-driven linking.

APPLICATIONS OF LLM-DRIVEN RECORD LINKAGE IN LIFE SCIENCES

1. CLINICAL TRIAL OPTIMIZATION:

Linking EHR data with patient registries helps identify suitable candidates for clinical trials more efficiently, reducing recruitment time and costs.

➤ See our blog article on the ways that AI is transforming Clinical Trial recruitment [READ ARTICLE]

2. POST-MARKET SURVEILLANCE:

Integrating pharmacovigilance reports with claims and EHR data enables companies to monitor real-world drug safety and efficacy more effectively.

➤ Read our previous coverage of ways to leverage AI in Pharmacovigilance [READ ARTICLE]

3. PRECISION MEDICINE RESEARCH:

Comprehensive patient data linkage facilitates the identification of biomarkers and the development of targeted therapies.

➤ See our blog article on the ways that AI is transforming Clinical Trial recruitment, especially for personalized medicine [READ ARTICLE]

4. TREATMENT PATHWAY ANALYSIS:

By linking records across healthcare providers, life sciences companies can gain insights into treatment adherence, switching patterns, and outcomes.

➤ We will be covering this topic in subsequent articles, stay tuned for more [MORE ARTICLES]

CONCLUSION

As LLMs continue to evolve, their ability to link and interpret RWD will unlock transformative possibilities for the life sciences industry. By breaking down silos between datasets, life sciences companies can generate richer insights, improve patient outcomes, and drive innovation in drug development and healthcare delivery.

By leveraging LLMs for record linkage, life sciences organizations can ensure that their RWD is not just a repository of information but a catalyst for actionable, real-world insights.

Are you ready to dramtically increase the quantity of RWD you can link and analyze? The future of RWD linking is here—powered by AI.

REFERENCES

ⁱ Interpretable deep learning to map diagnostic texts to ICD-10 codes [Link]

ⁱⁱ The Many Benefits of AI-Based ICD-10 Coding for Medical Coders [Link]

ⁱⁱⁱ Real-World Data (RWD) Market Analysis & Forecast 2032 [Link]

Interlnking Real World Data At Unprecedented Scale

OVERVIEW

CONTENTS

INTRODUCTION

RWD MARKET GROWTH