Export clinical scores to OMOP CDM v5.4 tables

Maps one or more ClinicalScore objects to OMOP Common Data Model v5.4 MEASUREMENT and OBSERVATION rows. The total and each subscale become a row, routed to MEASUREMENT or OBSERVATION by the concept map's domain_id. Concept ids are looked up in the packaged omop_concept_map.csv; an unmapped instrument yields measurement_concept_id = 0 (the OMOP "No matching concept") with a warning. No Athena concept ids are fabricated — the shipped map leaves them blank; supply a site concept_map to populate them.

Usage

toOMOP(scores, concept_map = NULL, person_map = NULL, type_concept_id = 0L)

Arguments

scores

A ClinicalScore or a list of them.

concept_map

Optional data frame overriding the packaged concept map (columns instrument, subscale, domain_id, measurement_concept_id, unit_concept_id).

person_map

Optional named vector/list mapping subject_id to an integer person_id. When absent, a plain positive-integer subject_id (matching "^[0-9]+$") is used directly; any other id is left as NA with a warning.

type_concept_id

Integer *_type_concept_id for provenance (default 0L = "No matching concept"); set to your site's type concept (e.g. EHR).

Value

A list with MEASUREMENT and OBSERVATION data frames conforming to the OMOP CDM v5.4 column layout.

References

OHDSI OMOP CDM v5.4 (MEASUREMENT, OBSERVATION).

See also

writeOMOPTables(), toFHIRObservation()

Examples

sc <- methods::new("ClinicalScore", instrument_id = "berg", total = 45,
                   subject_id = "1001", timestamp = "2026-07-26T09:00:00Z")
suppressWarnings(toOMOP(sc))
#> $MEASUREMENT
#>  [1] measurement_id                person_id                    
#>  [3] measurement_concept_id        measurement_date             
#>  [5] measurement_datetime          measurement_type_concept_id  
#>  [7] value_as_number               value_as_concept_id          
#>  [9] unit_concept_id               measurement_source_value     
#> [11] measurement_source_concept_id unit_source_value            
#> [13] value_source_value           
#> <0 rows> (or 0-length row.names)
#> 
#> $OBSERVATION
#>   observation_id person_id observation_concept_id observation_date
#> 1              1      1001                      0       2026-07-26
#>   observation_datetime observation_type_concept_id value_as_number
#> 1  2026-07-26 09:00:00                           0              45
#>   value_as_string value_as_concept_id unit_concept_id observation_source_value
#> 1            <NA>                  NA               0                     berg
#>   observation_source_concept_id unit_source_value
#> 1                             0           {score}
#>