Coverage of Physical Therapy Assessments in the Observational Medical Outcomes Partnership Common Data Model

High-value care aims to enhance meaningful patient outcomes while reducing costs and is accelerated by curating data across health care systems through common data models (CDMs), such as Observational Medical Outcomes Partnership (OMOP). Meaningful patient outcomes, such as physical function, must be included in these CDMs. However, the extent to which physical therapy assessments are covered in the OMOP CDM is unclear.

This study aimed to examine the extent to which physical therapy assessments used in neurologic and orthopaedic conditions are in the OMOP CDM.

After identifying assessments, two reviewer teams independently mapped the neurologic and orthopaedic assessments into the OMOP CDM. Agreement within the reviewer team was assessed by the number of assessments mapped by both reviewers, one reviewer but not the other, or neither reviewer. The reviewer teams then reconciled disagreements, after which agreement and the average number of concept ID numbers per assessment were assessed.

Of the 81 neurologic assessments, 48.1% (39/81) were initially mapped by both reviewers, 9.9% (8/81) were mapped by one reviewer but not the other, and 42% (34/81) were unmapped. After reconciliation, 46.9% (38/81) were mapped by both reviewers and 53.1% (43/81) were unmapped. Of the 79 orthopaedic assessments, 46.8% (37/79) were initially mapped by both reviewers, 12.7% (10/79) were mapped by one reviewer but not the other, and 48.1% (38/79) were unmapped. After reconciliation, 48.1% (38/79) were mapped by both reviewers and 51.9% (41/79) were unmapped. Most assessments that were mapped had more than one concept ID number (2.2 ± 1.3 and 4.3 ± 4.4 concept IDs per neurologic and orthopaedic assessment, respectively).

The OMOP CDM includes some physical therapy assessments recommended for use in neurologic and orthopaedic conditions but many have multiple concept IDs. Including more functional assessments in the OMOP CDM and creating guidelines for mapping would improve our ability to include functional data in large datasets.