Evaluation of the content coverage of SNOMED-CT to represent ICNP Version 1 catalogues.

To evaluate the ability of SNOMED-CT (Systematized Nomenclature of Medicine Clinical Terms) to represent ICNP (International Classification for Nursing Practice) nursing diagnosis and intervention catalogue concepts. We selected the 194 nursing diagnosis and 139 nursing intervention catalogue statements from ICNP Version 1.0. From June 2007 through December 2007, the first author mapped the ICNP catalogue concepts to SNOMED-CT using Apelon's TermWorks and CLUE browser 5.0. The second and fourth authors from SNOMED Terminology Solutions and the third author from ICN validated the mapping result. SNOMED-CT covered 172 concepts of 194 nursing diagnosis and 136 concepts of 139 nursing intervention catalogue concepts. SNOMED-CT can represent most (92.5%) of the ICNP nursing diagnosis and intervention catalogue concepts. Improvements to synonymy and adding missing concepts would lead to greater coverage of nursing diagnosis and intervention catalogue concepts.

Nursing standards to support the electronic health record.

Quality and low cost health care that is free of medical mistakes requires continuity of person-centric healthcare information across the life span and healthcare settings. Interoperable clinical information systems that rely on the use of multiple standards to support health information exchange and, in particular, nurse sensitive data, information, and knowledge are key components to support high quality, safe care. A 2004 Executive Order called for a National Health Information Network and the widespread adoption of electronic health records (EHRs) by 2014. While there are numerous standards influencing the exchange of health data, the primary focus of this article is to synthesize the state-of-the-art in nursing standardized terminologies to support the development, exchange, and communication of nursing data. Research exemplars are described for information systems to support nursing practice using standardized terminologies and secondary use of standardized nursing data from EHRs for knowledge development.

Nursing interventions classification in systematized nomenclature of medicine clinical terms: a cross-mapping validation.

The Systemized Nomenclature of Medical Clinical Terms, or SNOMED-CT, was developed to create a comprehensive clinical healthcare reference terminology. Standardized nursing language concepts and terminologies recognized by the American Nurses Association have been added to SNOMED-CT and include the NANDA's Taxonomy II, NIC, NOC, the Omaha System, and CCC. The relationship link between terminologies and SNOMED-CT is provided in a mapping table, which identifies the source terminology. The purpose of this study is to examine the validity of the cross-mapping between the source system (NIC) and the target system (SNOMED-CT) using the methodology developed by Lu and colleagues to detect misassigned concepts. Knowledge representation concepts in the NIC and SNOMED-CT systems were compared using expert human judgment. Of 514 NIC concepts, 14 (2.7%) were identified as misassigned in SNOMED-CT. Two inappropriate representations of concepts were discovered in NIC. Results and recommendations were given to NIC and to SNOMED-CT.

Nursing outcomes classification in the systematized nomenclature of medicine clinical terms: a cross-mapping validation.

The Systematized Nomenclature of Medicine Clinical Terms is a reference terminology that can be used to cross-map standardized healthcare languages across healthcare disciplines. It is important to ensure that the knowledge represented in individual source systems has been preserved during the process of cross-mapping to the target system. The purpose of this study was to validate the cross-mapping between the Nursing Outcomes Classification and the Systematized Nomenclature of Medicine Clinical Terms. The methodology to detect incorrectly assigned (mis-assigned) concepts from the source system to the target system was developed by the authors. The methodology compared knowledge representations of Nursing Outcomes Classification concepts in both systems by using human expert judgments. Each concept's meaning is defined by both the structures and definitions in the concept model. Sixteen (4.8%) mis-assigned Nursing Outcomes Classification concepts in the Systematized Nomenclature of Medicine Clinical Terms, and one inconsistent concept representation in the Nursing Outcomes Classification were found. Results and recommendations were given to both the Nursing Outcomes Classification and Systematized Nomenclature of Medicine Clinical Terms developers for consideration of future system improvements.

Clinical Care Classification (CCC) System Charting Model.

This is a demonstration of the Clinical Care Classification (CCC) System Charting Model. The Model software is designed for any personal computer (PC) with Windows using Microsoft Office and Access. The program has been tested as efficient and effective for charting patient care plans using the CCC System's Standardized language following the nursing process. It is available for integration in any electronic health record (E.H.R) system.

Standardized nursing language in the systematized nomenclature of medicine clinical terms: A cross-mapping validation method.

Many standardized healthcare languages have been mapped to the Systematized Nomenclature of Medicine Clinical Terms known as SNOMED CT, which was developed by the College of American Pathologists. This study describes a methodology for detecting misassigned concepts from source systems to SNOMED CT and presents the results of applying this methodology to a subset of concepts from two standardized nursing languages, the Nursing Interventions Classification and the Nursing Outcomes Classification. The methodology is based on comparing the knowledge representations of a set of nursing concepts between source systems (nursing languages) and SNOMED CT. If any nursing concept differs in knowledge representation in the target system compared with the source system, editorial misassignment of the concept was declared and recommendations for target system developers were made. In a total of 75 nursing concepts used to test this method, five misassigned concepts(6.6%) were found in SNOMED CT. This method can be used to validate other healthcare languages.

Mutual enhancement of diverse terminologies.

The purpose of this study was to map the North American Nursing Diagnosis Association (NANDA) nursing diagnoses to the International Classification for Nursing Practice Version 1.0 (ICNP) and to compare the resulting representations and relationships to those within SNOMED Clinical Terms (CT). Independent reviewers reached agreement on 25 (i.e. 64%) of the 39 parent-child relationships identified via the mappings between NANDA entities. Other parent-child relationships were more questionable and are in need of further discussion. This work does not seek to promote one terminology over any other. Rather, this collaborative effort has the potential to mutually enhance all three terminologies involved in the study: ICNP, SNOMED CT and NANDA. In doing so it provides an example of the type of collaborative effort that is needed to facilitate the development of tools to support interoperability at a global level.

Where is the nursing in SNOMED CT? CTGFN has the answer!

The SNOMED" micro CTGFN (Convergent Terminology Group for Nursing) is a working group reporting to the SNOMED International Editorial Board. They are charged with ensuring a terminology supportive of nursing's requirement for describing patient care. The strategies utilized for nursing content follow.

Information model and terminology model issues related to goals.

Goal statements are a significant component of structures that support the process of health care delivery such as practice guidelines, standards of care, critical pathways, disease management plans, patient education plans, and nursing care plans. Although these structures are increasingly computer-based, there has been little attention to the formal representation of goal statements. This is a necessary prerequisite for enabling semantic interoperability. Existing and evolving information model and terminology model standards offer some approaches that may be applicable to goal statements, however, a number of issues require resolution