The OMOP Data Model Helps Standardize Healthcare Data  

A key challenge in using real-world healthcare data (RWD) is how to standardize data across different healthcare providers and countries. 

– Although it is often thought that the data volume in healthcare is enormous, it isn’t. However, the data is diverse, and a large part of it is in free text format, which makes data processing difficult. For example, one distribution center of the leading Finnish delivery and fulfillment companies, Posti, produces as much data in four days as laboratories of the wellbeing services county of Southwest Finland (Varha) does in ten years, says Arho Virkki, Analytics Director of the wellbeing services county of Southwest Finland (Varha). Virkki held a presentation at Medaffcon’s EMMA client event. 

Due to the diversity of healthcare data, standardizing methods are needed to facilitate real-world (RWE) research and data-driven decision-making. One method is the OMOP data model (Observational Medical Outcomes Partnership). The OMOP data model is a relational model that collects data in a patient-centric manner.  

The use of (Observational Medical Outcomes Partnership) OMOP is increasing 

The Observational Health Data Sciences and Informatics (OHDSI) organization drives the utilization of the OMOP data model. This model standardizes healthcare data and enables uniform processing of data from different sources and regions. In addition to OMOP, other similar data models for leveraging healthcare information exist. 

– Many European organizations use OMOP, and its use is increasing. Therefore, I would say that it is the best bet to make right now, says Arho Virkki, Analytics Director of the wellbeing services county of Southwest Finland (Varha). 

According to Virkki, whether OMOP will become the dominant model for productivity studies in Finnish hospitals is unclear. 

– At least we should be inspired by OMOP. We may consider whether to build our own domestic model for productivity studies. On the other hand, if the welfare counties manage to transfer a lot of their data to the OMOP model, it is entirely possible that the OMOP data model could also be used in productivity studies. 

International research requires a unified OMOP data model  

Today, the benefits of OMOP are realized in international research that utilizes data from numerous hospitals across Europe. Combining diverse data would be impossible without a standardized data model. 

The wellbeing services county of Southwest Finland (Varha) is involved in the European Medicines Agency’s Data Analysis and Real World Interrogation Network (Darwin). The network conducted a drug utilization study on valproate using the OMOP data model for EMA. In this study, the wellbeing services county of Southwest Finland’s (Varha) biostatistician converted the welfare area’s data into the OMOP model and checked, among other things, the success of data bridging and the functionality of the analysis code. 

– We gained a lot of good experience from using the OMOP model. Extracting and translating data into OMOP is not entirely straightforward. However, a common data model is a prerequisite for international research, says Virkki. 

How does the OMOP (The Observational Medical Outcomes Partnership) Common Data Model (CDM) facilitate using health data?  

• The data model facilitates collaboration and innovation between different stakeholders  
• Data is easier to utilize and share among different stakeholders  
• OMOP enables studies to be more easily reproducible and comparable  
• Federated analyses can be performed without moving data between different hospital data lakes 
• Data becomes available in a standardized format, and the time spent on data preprocessing is reduced