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The green and black elements at the top of the figure above show the production of reliable evidence by applying standardized analytics to standardized person-level clinical data. The extension of this approach by the GIS toolchain is shown below that. Geocoded patient addresses are linked to geospatial datasets and standardized in the OMOP GIS extension. The GIS toolchain uses the same modeling approach as the main OMOP CDM and vocabulary. This allows the same best practice analytics to be applied simultaneously to both person-level clinical data and geospatial data on exposures outside the clinic. As with OHDSI's use of clinical data, analyses of external exposures are done without sharing patient address information.
The text was updated successfully, but these errors were encountered:
The green and black elements at the top of the figure above show the production of reliable evidence by applying standardized analytics to standardized person-level clinical data. The extension of this approach by the GIS toolchain is shown below that. Geocoded patient addresses are linked to geospatial datasets and standardized in the OMOP GIS extension. The GIS toolchain uses the same modeling approach as the main OMOP CDM and vocabulary. This allows the same best practice analytics to be applied simultaneously to both person-level clinical data and geospatial data on exposures outside the clinic. As with OHDSI's use of clinical data, analyses of external exposures are done without sharing patient address information.
The text was updated successfully, but these errors were encountered: