For life sciences teams, data is one of the most valuable and expensive assets. Organizations invest millions each year acquiring, licensing, and integrating real-world data (RWD), electronic health record (EHR) extracts, claims data, and linked observational sources. But too often, the return on that investment is limited, not by the quality of the data itself, but by how it’s structured, labeled, and interpreted.
When the underlying terminology infrastructure can’t keep up with clinical nuance, much of that value remains locked away.
The hidden cost of mismatched terminologies
Most data sets used in clinical research are mapped to standardized ontologies like SNOMED CT®, ICD-10-CM, MedDRA, or LOINC®. These play a critical structural role but were never designed for modern analytic use cases. They often fail to reflect how clinicians actually document care, how diseases present in real-world settings, or how nuanced a cohort definition may need to be.
This introduces friction across key areas:
- Cohort creation becomes inconsistent. A 2022 JAMIA study found that variability in diagnostic coding across systems led to a 30% difference in patient cohort sizes derived from EHR data.
- Trial timelines stretch. Nearly 80% of clinical trial delays are tied to patient recruitment and site activation challenges, often worsened by terminology mismatches or imprecise mappings.
- Data harmonization is resource intensive. Different data sources may use clinically similar but syntactically different terms, creating costly bottlenecks in data integration pipelines.
- Insights are delayed. Lagging terminology updates can prevent organizations from fully utilizing emerging disease or biomarker data.
This graphic illustrates how clinician-friendly terms, such as those used to describe a heart attack, are mapped to standardized code sets with IMO Health’s proprietary terminology infrastructure. This enables accurate patient ID, reliable data analysis, and meaningful clinical research outcomes.
This graphic shows how terminology with clinical intent facilitates the ID of patients and disease stages with biomarkers rather than generic ICD-10-CM codes. IMO Health’s terminology integrates all relevant clinical information, minimizing manual workflows and enhancing precision in patient population identification.
Standard ontologies can’t solve this alone
Resources like the Unified Medical Language System (UMLS) and the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM) are foundational to data standardization efforts. But while they bring structure, they don’t always bring semantic clarity.
- UMLS is built to integrate and crosswalk between vocabularies, but it doesn’t resolve clinical ambiguity, manage local term variants, or reflect how physicians describe conditions at the bedside.
- OMOP-CDM enforces data consistency across observational sources but relies on vocabularies that often flatten nuance in favor of uniformity.
While critical, these systems are not sufficient on their own for use cases that demand clinical precision, such as identifying hard-to-detect patient phenotypes, linking outcomes across care settings, or segmenting populations for observational studies.
Get more from the data you already paid for
The cost to acquire data, whether licensing commercial RWD, linking registries, or aggregating longitudinal EHR feeds, is substantial. And yet, without terminology infrastructure that can normalize and harmonize that data at scale, much of its potential remains untapped.
Investing in smarter terminology can result in:
- More precise cohorts. Reduce false positives and false negatives by aligning with how clinicians actually document care, not just how diseases are classified.
- Faster time to insight. Automate normalization across sources and reduce the need for manual curation, such as when integrating OMOP-based data with other proprietary or clinical systems.
- Fewer costly amendments. Protocol changes cost an estimated $141K–$500K each. Semantically robust criteria help teams avoid rework caused by misinterpreted eligibility definitions.
- Higher ROI on RWD investments. When terminology works for you, not against you, every dataset becomes more interoperable, more searchable, and more useful.
Terminology is a strategic asset and the foundation of data ROI
As organizations face mounting pressure to accelerate development, optimize study designs, and extract value from increasingly complex data ecosystems, one truth remains constant: the quality of insight begins with the quality of language.
No matter how advanced the analytics or how vast and deep the data coverage, meaningful outcomes depend on how accurately clinical concepts are captured, structured, and understood.
That’s why smarter terminology isn’t just a technical enhancement; it’s a strategic multiplier. It ensures that your real-world data is aligned, interpretable, and actionable across use cases and functions, enhancing the return on every data investment made.
No matter how advanced the analytics or how vast and deep the data coverage, meaningful outcomes depend on how accurately clinical concepts are captured, structured, and understood.
Built on over 30 years of clinical expertise, IMO Health offers terminology infrastructure that reflects the complexity and nuance of real-world care. Developed at the intersection of clinical practice and informatics, IMO Health’s solutions are grounded in clinical intent, optimized for usability, and built to bridge systems, data sources, and stakeholders.
In a landscape where every insight matters and every decision has a downstream impact; this foundation is not just helpful. It’s essential.
