Just another beer debate between OHDSI colleagues
Argument: Standardize all health care data to OMOP
- OMOP CDM can standardize a wide range of longitudinal health event data at the person level, with few exceptions. Exceptions include: 1. Data with intraday granularity., 2. Data involving terminology not covered by OMOP standard vocabularies, like images and genomics., 3. Datasets heavy on measurement values pose challenges due to limited experience with standard tools.
- Standardizing data requires significant expertise; errors in standardization can lead to non-representative data, impacting analysis quality.
Counterargument: Standardization should be done to support generalizable analytic tools
- Data standardization, such as with OMOP CDM, serves specific purposes like leveraging standardized analysis tools or ensuring consistent application of analytic codes across databases.
- OMOP CDM can handle datetime granularity, but the absence of standardized tools for optional datetime fields necessitates custom analytics. Hence CDM 6.0 is not adopted.
- OMOP CDM supports various data domains and has extensions for imaging; challenges arise from mapping when source vocabularies are not in OHDSI standardized vocabularies.
- OMOP CDM effectively models measurement values, and tools like ATLAS can use these values; however, standardization issues persist across concepts, units, and values_as_concepts.
- OMOP CDM also models procedures well, though lack of a procedure hierarchy often requires intensive curation and context-specific code lists.