Foundational to integrating nursing data into clinical data repositories for big data and science, is the implementation of standardized nursing terminologies, common data models, and information structures within EHRs. The 2014 National Action Plan for Sharable and Comparable Nursing Data for Transforming Health and Healthcare builds on and leverages existing, but separate long standing efforts of many individuals and organizations. The plan is action focused, with accountability for coordinating and tracking progress designated.

Although informatics is an important area of nursing inquiry and practice, few scholars have articulated the philosophical foundations of the field or how these translate into practice including the often-cited data, information, knowledge, and wisdom (DIKW) framework. Data, information, and knowledge, often approached through postpositivism, can be exhibited in computer systems. Wisdom aligns with constructivist epistemological perspectives such as Gadamerian hermeneutics. Computer systems can support wisdom development. Wisdom is an important element of the DIKW framework and adds value to the role of nursing informaticists and nursing science.

The problem list is a key component of the patient care and has been acknowledged as critical by the EHR Meaningful Use criteria. Nursing diagnoses on the problem list are foundational for constructing a nursing care plan. A multidisciplinary patient problem list will facilitate communication and evaluation of the contribution of nursing care to the patient's clinical care experiences and outcomes.

We demonstrated the feasibility of using documentation artifacts in a bottom-up approach to develop common models and sets of terms that are complete from the perspective of clinical implementation. Importantly, we demonstrated a process by which a community of practice can contribute to closing gaps in existing standards using SDO processes.

Disparate data must be represented in a common format to enable comparison across multiple institutions and facilitate Big Data science. Nursing assessments represent a rich source of information. However, a lack of agreement regarding essential concepts and standardized terminology prevent their use for Big Data science in the current state. The purpose of this study was to align a minimum set of physiological nursing assessment data elements with national standardized coding systems. Six institutions shared their 100 most common electronic health record nursing assessment data elements. From these, a set of distinct elements was mapped to nationally recognized Logical Observations Identifiers Names and Codes (LOINC®) and Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT®) standards. We identified 137 observation names (55% new to LOINC), and 348 observation values (20% new to SNOMED CT) organized into 16 panels (72% new LOINC). This reference set can support the exchange of nursing information, facilitate multi-site research, and provide a framework for nursing data analysis.

Widespread use of the NMMDS could reduce administrative burden and enhance the meaningful use of healthcare data by ensuring that nursing relevant contextual data are available to improve outcomes and safety measurement for research and quality improvement in and across healthcare organizations.

The purpose of this study was to translate and integrate nursing diagnosis concepts from the Clinical Care Classification (CCC) System Version 2.0 to DiagnosticPhenomenon or nursing diagnostic statements in the International Classification for Nursing Practice (ICNP) Version 1.0. Source concepts for CCC were mapped by the project team, where possible, to pre-coordinated ICNP terms. The manual decomposition of source concepts according to the ICNP 7-Axis Model served to validate the mappings. A total of 62% of the CCC Nursing Diagnoses were a pre-coordinated match to an ICNP concept, 35% were a post-coordinated match and only 3% had no match. During the mapping process, missing CCC concepts were submitted to the ICNP Programme, with a recommendation for inclusion in future releases.

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