The Observational Health Data Sciences and Informatics (or OHDSI, pronounced "Odyssey") program is a multi-stakeholder, interdisciplinary open-science community with the overall goal of improving healthcare for patients. Through its open-source software portfolio and methods for data standardization and analysis (including OMOP CDM (see below)), it wants to enable analytics on substantial amounts of health data.
The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) is an open community data standard, developed by the OHDSI community. It is designed to standardize the structure and content of observational data and to enable efficient analyses that can produce reliable evidence.1
EHDEN is the European Health Data & Evidence Network. It is a federated data network launched to enable international large-scale research to generate real-world evidence (RWE) from real-world data (RWD). From the start, EHDEN has been working hand-in-hand with OHDSI.
With most health data being held in an unstructured format (e.g., Electronic Health Records) or in a structured format but in different databases and with different medical coding dictionaries, the data can serve for its primary use within the hospital but becomes difficult to reuse for research. Because of the growing need for standardized health databases, various initiatives have grown in the past decades, including the OHDSI community. The latter developed the OMOP CDM Model to create interoperable health databases. In practice, hospital data gets extracted, processed, and structured (by LynxCare) in the standardized OMOP format, so it can be used to create Findable, Accessible, Interoperable and Reusable (FAIR) data warehouses. These federated clinical data warehouses can serve hospitals as well as researchers to gain insights into real-world populations, on the effectiveness of treatments, the progression of diseases, and much more. This is the concept of a federated approach, as shared by EHDEN.
The quality and completeness of the data highly influences the quality of a performed analysis or research. It is therefore of utmost importance that the necessary quality checks are done throughout the data processing cycle, from source data to target data.
LynxCare guarantees data quality through various manual checks. To allow full transparency, LynxCare equally allows the hospital to assess the data quality through:
Example: Recall (true positive/all positive) and precision (true positive/true positive + false negative) on the datapoint ARDS
Example: “Acute respiratory distress syndrome” or “ARDS” are concepts found under the datapoint ARDS
Example: ARDS as a complication originated from structured data in 60% of patients diagnosed with ARDS
In addition, LynxCare deploys various OHDSI tools that can connect to the CDM: ACHILLES, ATLAS, and Data Quality Dashboard to ensure hospitals have everything at hand to make optimal use of their data (of which they remain owner and in control at all times).
Want to know more about ACHILLES, ATLAS, and the Data Quality Dashboard? Check Chapter 15 Data Quality | The Book of OHDSI and/or reach out.
As a hospital, the advantages of having an OMOP CDM data warehouse with standardized observational health data are numerous.
LynxCare is an EHDEN-certified SME, EHDEN Data Partner for various Belgian hospitals and complies to OHDSI standards. We help hospitals unlock clinical data, set up databases according to OMOP Common Data Model standards, for better patient care and scientific research. LynxCare’s federated AI data model ensures all data remains under the custody of the hospital. Hospitals are in full control of how that data is used, and patients remain their sole proprietors.
We strongly align with OHDSI's mission to improve health, by fostering collaboration generating real-world evidence that can serve better health decisions, improved care, and research.
Take the first step towards better healthcare insights - Contact LynxCare to learn how your hospital can benefit from an OMOP CDM data warehouse.
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