Standardized clinical data capture promotes data integrity and facilitates efficient point-of-care and secondary data usage, thereby supporting quality patient care. Clinical information models (CIMs) enable standardization of clinical data capture by establishing the data elements and valuesets necessary to represent a given clinical domain or topic. Design and refinement of CIMs that support standardization and re-use is challenging. We compared details and semantic relationships between unique elements specified within a validated skin assessment CIM. We show how semantic relationships between data elements can be leveraged to confirm congruence and identify incongruences, thereby supporting successful CIM implementation and maintenance efforts.

Learning Objective 1: After participating in this session, the learner should be able to successfully compare and reconcile a clinical information model with Electronic Health Record (EHR) data elements.


Karen Bavuso (Presenter)
Semedy, Inc.

Melinda Wong, Semedy, Inc.
Man Wai Chan, Semedy, Inc.
David Taylor, Semedy, Inc.
Saverio Maviglia, Semedy, Inc.
Roberto Rocha, Semedy, Inc.

Presentation Materials: