Unlocking the Power of EHR Data Using OMOP: A Framework for Standardizing and Analyzing Healthcare Data

Electronic Health Records (EHR) data has helped to transform the healthcare landscape by providing detailed clinical information, including diagnoses, medications, lab results, and treatment histories captured at the point of care. Therefore, EHR data holds immense potential for understanding disease progression and patient outcomes and evaluating treatment effectiveness in real-world settings. For example, by identifying patterns of care and gaps in treatment, EHR data can enable more comprehensive patient journey insights. When combined with other types of real-world or clinical data, it opens doors to personalize medicine and drug development use cases.

However, deriving the full value of EHR data often comes with significant challenges. First, large EHR datasets often require intensive computational resources to store and process the data efficiently. In addition, EHR data may contain errors, omissions, or inconsistencies that demand extensive data processing and validation to ensure quality. One of the most persistent hurdles is the lack of EHR data standardization across different sources. EHR data elements may be encoded using diverse formats and coding systems (such as ICD, CPT, and SNOMED, among others). In addition, in some cases, EHR data elements may be highly relevant but lack their corresponding medical codes, which gives rise to a highly complex data semantics problem. Taken together, these inconsistencies in data capture, terminologies, and data structures may create barriers to EHR data integration and analysis. More importantly, in life science use cases, time is of the essence. The value of EHR data may decrease over time if the majority of time on a use case is spent on data pre-processing and cleansing rather than using the data for solving problems in clinical development and patient care.

The Observational Medical Outcomes Partnership (OMOP) common data model offers a compelling solution to these challenges. OMOP enables more efficient data integration across different data providers, institutions, and studies by providing a trusted standardized framework for transforming disparate EHR data into a unified format. This standardization framework is critical for unlocking the true potential of EHR from a scientific and clinical perspective. 

Transforming EHR Data with OMOP

The OMOP model provides an industry-standard framework to transform fragmented EHR data into a structured, reliable, and industry-known format. This transformation unlocks new opportunities for life science organizations to leverage EHR data in key R&D and Commercial use cases. Benefits of EHR data in OMOP format include:

• Advanced Analytics, Machine Learning, and AI: Standardized, interoperable data better serves advanced analytics and AI use cases, enabling predictive modeling and faster discovery of insights to inform clinical and commercial decision-making.
• Unified Patient Journeys for Cohort Building: Combining EHR data with claims or clinical data helps build a 360-degree view of the patient experience. This unified approach enables life science researchers to build precise and relevant cohorts for both clinical research and market access studies.
• Comparative Effectiveness Studies: EHR data in OMOP format may be useful for benchmarking treatment patterns and outcomes, and understanding healthcare utilization across different populations and geographies. This can improve evidence-based decision-making in clinical development and healthcare delivery.
• Synthetic Control Arms for Clinical Trials: Historical EHR data may be used as input when creating virtual control groups, which reduces the need to recruit patients in traditional control arms in clinical trials. This innovative approach may improve patient recruitment, speed up clinical trials, and lower clinical trial costs.

By standardizing data into the OMOP Common Data Model (CDM), organizations can unlock new opportunities in real-world evidence (RWE) generation, collaborative research, and healthcare innovation. Its broad adoption highlights its role as a cornerstone for advancing healthcare research, improving patient outcomes, and driving strategic decisions. 

Why OMOP Matters

OMOP is a well-known industry standard that allows life science organizations to take EHR and other healthcare data domains from various disparate sources and conform to a globally accepted and trusted format. OMOP helps to:

• Break Down Silos: It standardizes data from EHRs, claims, registries, and other sources into a unified, research-ready format.
• Simplify Integration: OMOP reduces the time and effort required to transform and harmonize data, enabling scalability for complex research and collaborations.
• Ensure Consistency: With a standardized structure and vocabulary, OMOP facilitates interoperability, making it easier to query and analyze data across multiple systems.

For life sciences organizations, this means faster, more reliable insights that drive innovation and decision-making.

Who Benefits from OMOP?

OMOP’s versatility supports a wide range of roles and functions within the life sciences. Here’s how different teams can leverage OMOP data:

• HEOR Teams: Evaluate cost-effectiveness and healthcare utilization trends.
• Clinical Research Teams: Identify eligible cohorts, disease progression analysis and endpoints for studies.
• Pharmacovigilance Teams: Monitor adverse events and safety signals.
• Commercial Teams: Identify market opportunities and align strategies with unmet needs.

Implementing OMOP

What It TakesTransforming data into the OMOP format requires a multidisciplinary approach combining data engineering, clinical expertise, and rigorous quality control. At Kythera Labs, our approach leverages a scalable, cloud-native infrastructure (our Wayfinder platform - built on Databricks) and industry standard tools to ensure efficient and correct implementation. Key components of our process include:

1. Data Engineering: Expertise in ETL processes to extract, clean, and map data to the OMOP schema.
2. Clinical Knowledge: Understanding diagnoses, procedures, and medications ensures accurate mapping and harmonization.
3. Data Quality and Validation: Rigorous testing ensures completeness, accuracy, and conformance to OMOP standards. The use of standard OHDSI tools help us verify we have implemented OMOP correctly and spot-check data quality issues.
4. Privacy Safeguards: De-identification techniques protect patient confidentiality throughout the process.

Conclusion

The OMOP standard provides an opportunity to standardize, integrate, and enhance healthcare data into a transformative tool for life sciences companies, healthcare providers, and researchers. By adopting OMOP, organizations can unlock the full potential of real-world data for robust research and innovative patient care solutions. Whether you're in clinical research, commercial strategy, or health economics, OMOP provides the foundation for data-driven success in today’s evolving healthcare ecosystem. At Kythera Labs, we focus on reducing the uncertainty, time, effort, and expense in the use of real-world data. We help our customers get to answers faster, driving greater value for every dollar spent and enabling innovation at speed and scale. If you’d like to learn more about our approach to OMOP-configured EHR data or share your OMOP experiences with us, get in touch or connect with me on LinkedIn.