In today’s race to generate regulatory-grade real-world evidence (RWE), pharmaceutical companies are hitting a familiar speed bump: data that’s messy, inconsistent, and maddeningly hard to use. Whether it’s building patient cohorts for clinical trials or deriving insights from real-world data (RWD), too often the story ends with siloed systems, vague codes, and mountains of unstructured notes.
The result? Critical decisions get delayed, trial recruitment stalls, and the high cost of RWD doesn’t translate into value.
Clinical terminology has the potential to streamline evidence generation – but only when it’s standardized, consistent, and context-aware. Without that foundation, even the best data can fall short of delivering real-world insight. That’s why the right terminology strategy isn’t just a backend fix – it’s a strategic advantage.
We explore this in our latest insight brief: Outsmarting data bottlenecks in pharma: The clinical terminology advantage. Through three essential use cases – scientific literature review, clinical trial cohorting, and real-world evidence generation – we examine how standardized terminology and expert-driven normalization can help research teams improve reproducibility, reduce costs, and move from raw data to actionable insight faster.
INSIGHT BRIEF
Outsmarting data bottlenecks in pharma:
The clinical terminology advantage
Here’s a look inside.
Patient cohorting for evidence generation
RWD holds the promise of bringing life sciences teams closer to real patients and their lived healthcare journeys. But all too often, the individual patient – their conditions, treatments, and outcomes – gets lost in a maze of messy, inconsistent data. Accurate patient cohorting is the first, essential step in generating evidence that reflects reality. However, building these cohorts requires cutting through the noise to define precise, reproducible group that can withstand scientific regulatory scrutiny.
The challenges of extracting and normalizing RWD include:
- Hidden and unstructured data: The insights needed (disease severity, treatment regimens, outcomes) are often trapped in free-text clinical notes, scattered across systems, or embedded in non-standard codes.
- Inconsistent coding and clinical meaning: RWD comes from many sources – EHRs, claims, labs, registries – each with its own language. Without normalization, even basic cohort definitions can vary wildly, undermining reproducibility.
- Costly data: RWD is expensive, with data sets costing upward of $5 million to license. Yet, without proper structuring and standardization, their value is often lost in translation.
The IMO Health approach
The core of meaningful evidence generation isn’t just data – it’s the ability to accurately extract and normalize patient information into a clear, clinically rich narrative that is computation ready. But that requires data that’s complete, consistent, and specific enough to reflect true patient experiences. IMO Health brings clarity to RWD with a clinical terminology engine and extraction framework expertly built and curated over 30 years. By harmonizing noisy, inconsistent data into a clean, clinically meaningful narrative, our solution enables:
- Automated data extraction: Eliminates the manual, time-consuming work of pulling key variables from messy notes, freeing teams to focus on analysis, not data wrangling.
- Insight into clinical intent, not just codes: Captures nuances like disease severity, progression, and comorbidities buried in free text, ensuring cohorts reflect real-world complexity.
- Fast return on RWD investments: Moves from raw data to actionable insights quickly, ensuring investments in RWD pays off with stronger, accelerated evidence generation.
In an industry where every delay has a cost – both financial and human – robust clinical terminology is emerging as a strategic differentiator. Read the full insight brief to learn how IMO Health is helping pharmaceutical and life sciences teams standardize, structure, and scale their evidence generation workflows from the ground up.
