This paper presents the modeling strategy of nursing knowledge, based on multilevel modeling of health systems. Through a systematic review, nursing record instruments were obtained. and one was chose for knowledge modeling according to the Multilevel Healthcare Information Modeling (MLHIM) specifications.

Summary Background: Healthcare information technologies have the potential to transform nursing care. However, healthcare information systems based on conventional software architecture are not semantically interoperable and have high maintenance costs. Health informatics standards, such as controlled terminologies, have been proposed to improve healthcare information systems, but their implementation in conventional software has not been enough to overcome the current challenge. Such obstacles could be removed by adopting a multilevel model-driven approach, such as the open EHR specifications, in nursing information systems. Objectives: To create an open EHR archetype model for the Functional Status concepts as published in Nursing Outcome Indicators Catalog of the International Classification for Nursing Practice (NOIC-ICNP). Methods: Four methodological steps were followed: 1) extraction of terms from the NOICICNP terminology; 2) identification of previously published open EHR archetypes; 3) assessment of the adequacy of those open EHR archetypes to represent the terms; and 4) development of new open EHR archetypes when required. Results: The “Barthel Index” archetype was retrieved and mapped to the 68 NOIC-ICNP Functional Status terms. There were 19 exact matches between a term and the correspondent archetype node and 23 archetype nodes that matched to one or more NOIC-INCP. No matches were found between the archetype and 14 of the NOIC-ICNP terms, and nine archetype nodes did not match any of the NOIC-ICNP terms. Conclusions: The open EHR model was sufficient to represent the semantics of the Functional Status concept according to the NOICICNP, but there were differences in data granularity between the terminology and the archetype, thus producing a significantly complex mapping, which could be difficult to implement in real healthcare information systems. However, despite the technological complexity, the present study demonstrated the feasibility of mapping nursing terminologies to open EHR archetypes, which emphasizes the importance of adopting the multilevel model-driven approach for the achievement of semantic interoperability between healthcare information systems.